COMMISSION STAFF WORKING DOCUMENT IMPACT ASSESSMENT REPORT Accompanying the document Proposal for a Directive of the European Parliament and of the Council on adapting non-contractual civil liability rules to artificial intelligence
Date: 2022-09-28
Institutions: Directorate-General for Justice and Consumers, European Commission
Subjects: artificial intelligence, civil liability, civil liability proceedings, damage, digital technology, disclosure of information, evidence, information technology applications, new technology, proof

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 28.9.2022  SWD(2022) 319 final  COMMISSION STAFF WORKING DOCUMENT  IMPACT ASSESSMENT REPORT  Accompanying the document Proposal for a Directive of the European Parliament and of the Council on adapting non-contractual civil liability rules to artificial intelligence {COM(2022) 496 final} - {SEC(2022) 344 final} - {SWD(2022) 318 final} - {SWD(2022) 320 final}  
 
 
Table of contents 

1. 

2. 

INTRODUCTION: POLITICAL AND LEGAL CONTEXT ............................................................... 1 

PROBLEM DEFINITION .................................................................................................................... 7 

2.1. 
2.2. 

2.3. 
2.4. 
2.5. 

2.6. 
2.7. 

Introduction and overview ......................................................................................................... 7 
Problem driver: the specific characteristics of AI make it difficult to meet the burden 
of proof ...................................................................................................................................... 8 
Problem 1: Legal uncertainty leading to internal market obstacles ....................................... 100 
Problem 2: Legal fragmentation leading to internal market obstacles .................................. 111 
Problem 3: Lack of compensation for victims leading to a lack of societal trust and 
hampering uptake of AI-enabled products and services ........................................................ 122 
The consequences: internal market obstacles and affected stakeholders ................................. 14 
Likely evolution of the problems without Union intervention ................................................ 17 

3.  WHY SHOULD THE EU ACT? ........................................................................................................ 19 

3.1. 
3.2. 

Legal basis ............................................................................................................................... 19 
Subsidiarity: Necessity and added value of EU action ............................................................ 20 

4.  OBJECTIVES: WHAT IS TO BE ACHIEVED? ............................................................................... 23 

4.1.  General objectives ................................................................................................................... 23 
Specific objectives ................................................................................................................... 23 
4.2. 

5.  WHAT ARE THE AVAILABLE POLICY OPTIONS? .................................................................... 25 

5.1.  What is the baseline from which options are assessed? .......................................................... 25 
5.2.  Description of the policy options ............................................................................................ 29 
5.3.  Measures discarded at an early stage ....................................................................................... 41 

6.  WHAT ARE THE IMPACTS OF THE POLICY OPTIONS? ........................................................... 42 

PO1: Easing the burden of proof for AI-related claims ......................................................... 422 

6.1. 
 6.2.     PO2: PO1 + strict liability for AI use-cases with a specific risk-profile, possibly coupled with 

mandatory insurance ……………………………………………………………………….. 51                                                                                                                                            

6.3. 

PO3:  PO1  +  targeted  review  regarding  the  strict  liability  and  mandatory  insurance 
elements of PO2 (staged approach) ......................................................................................... 57 

7.  HOW DO THE OPTIONS COMPARE? ............................................................................................ 59 

8. 

PREFERRED OPTION ...................................................................................................................... 60 

8.1.  Rationale and main impacts of the preferred PO ..................................................................... 60 
The interplay with the PLD review: consistency and complementarity of the package 
8.2. 
of measures for the compensation of damage caused by AI .................................................... 61 

8.3.     The interplay with the PLD review: consistency and complementarity of the package of                                                      

measures for the compensation of damage caused by AI ………………………………….. 61 

8.4. 

One-In-One-Out …………………………………………………………………………… 62 

9.  HOW WILL ACTUAL IMPACTS BE MONITORED AND EVALUATED? .................................. 62 

ANNEX 1: PROCEDURAL INFORMATION .......................................................................................... 123                                                                                      

ANNEX 2: STAKEHOLDER CONSULTATION  .................................................................................12370 

ANNEX 3: WHO IS AFFECTED AND HOW?  ..................................................................................... 9393 

ΑΝΝΕΧ 4: ANALYTICAL METHODS AND KEY FINDINGS OF SUPPORTING STUDIES / 
EXPLANATIONS ON HOW THE EUROPEAN PARLIAMENT’S LEGISLATIVE OWN-
INITIATIVE RESOLUTION ON A CIVIL LIABILITY REGIME FOR AI WAS TAKEN 
INTO ACCOUNT  ............................................................................................................................ 101 

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ΑΝΝΕΧ 5: DETAILED EXPLANATIONS ON HOW THE SPECIFIC CHARACTERISTICS OF AI 
ARE CHALLENGING EXISTING LIABILITY RULES ……………………………………….…..... 120 

ANNEX  6:  DETAILED  DESCRIPTION  OF  THE  LEGAL  CONTEXT  AND  INTERPLAY 
BETWEEN  EXISTING  /  PROPOSED  LEGAL  RULES  AND  THE  AI  LIABILITY 
INITIATIVE ..................................................................................................................................... 123 

ANNEX 7: INTERPLAY WITH THE AI ACT ......................................................................................... 148 

ANNEX  8:  AI-SPECIFIC  FUNDAMENTAL  RIGHTS  CONCERNS  –  OVERALL 
COMMISSION  POLICY  APPROACH  AND  THE  ROLE  OF  THE  AI  LIABILITY 
PROPOSAL ...................................................................................................................................... 153 

ANNEX  9:  THE  TYPES  OF  COMPENSABLE  (IN  PARTICULAR  IMMATERIAL)  HARM 
AND  THE  ADMISSIBILITY  OF  CONTRACTUAL  EXCLUSIONS/LIMITATIONS  OF 
LIABILITY  –  MEMBER  STATES’  LEGAL  APPROACHES/TRADITIONS  AND 
REASONS FOR NOT HARMONISING THESE ASPECTS SPECIFICALLY FOR AI................ 162 

ANNEX  10:  DETAILED  EXPLANATIONS  AND  RESULTS  REGARDING  THE 
ASSESSMENT  AND  COMPARISON  OF  POLICY  OPTIONS  (MULTI-CRITERIA 
ANALYSIS)...................................................................................................................................... 175 

ANNEX 11: THE ECONOMIC IMPACT OF ADAPTING CIVIL LIABILITY RULES TO THE 
SPECIFIC  CHALLENGES  OF  AI  –  THE  EXAMPLE  OF  THE  EUROPEAN  MARKET 
FOR VACUUM CLEANERS ........................................................................................................... 214 

ANNEX 12: MONITORING AND EVALUATION ................................................................................. 231 

ANNEX  13: 

ILLUSTRATION  OF  AI-SPECIFIC  DIFFICULTIES 

IN  CLAIMING 

COMPENSATION BASED ON CASE SCENARIOS .................................................................... 233 

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1.  INTRODUCTION: POLITICAL AND LEGAL CONTEXT 

1.1.  Introduction and political context 

AI  is  a  set  of  enabling  technologies  with  the  potential  to  transform  our  economy  and  society  to 
such a profound extent that it is often compared to electricity. As a key driver for future economic 
development,  AI  has  strategic  importance1.  While  AI  can  bring  about  economic  and  societal 
progress, it can also put at risk European values.  In particular, when AI systems cause harm, the 
victims’ legal interests of life, health and property are at stake. In addition, the right to an effective 
access  to  justice  can  be  in  doubt  because  the  specific  characteristics  of  certain  types  of  AI  can 
make it prohibitively difficult to prove their claim under existing liability rules.  

For  these  reasons,  Commission  President  Ursula  von  der  Leyen  announced  in  her  political 
Guidelines2 a coordinated European approach on AI. In its White Paper on AI3, the Commission 
undertook to promote the uptake of AI and to address the risks associated with certain of its uses 
by building an ecosystem of excellence and trust. One of the White Paper political objectives for 
liability  and  this  impact  assessment  is  that  persons  having  suffered  harm  caused  by  AI  systems 
enjoy the same level of protection as persons having suffered harm caused by other technologies.4 
In  the  Report  on  AI  Liability5  accompanying  the  White  Paper,  the  Commission  identified  the 
specific  challenges  posed  by  AI  to  existing  liability  rules.  The  Commission  Work  Programme 
2020 envisaged a follow-up to the White Paper in the form of new legislative initiatives, including 
on liability.6 

In its conclusions  on shaping Europe’s digital  future of 9 June 2020, the Council welcomed the 
consultation on the policy proposals in the White Paper on AI and called upon the Commission to 
put  forward  concrete  proposals.  On  20  October  2020,  the  European  Parliament  (EP)  adopted  a 
legislative  own-initiative  resolution  requesting  the  Commission  to  adopt  a  proposal  for  a  civil 
liability regime for artificial intelligence based on Article 114 TFEU.7 

The Coordinated Plan on AI in the Annex to the Communication ‘Fostering a European approach 
to  Artificial  Intelligence’  of  21  April  20218  specifies  that  the  Commission  will  propose  in  2022 
measures adapting the liability framework to the challenges of AI. 

AI systems could also contribute to the accomplishment of a several targets across the Sustainable 
Development Goals included in the 2030 UN Agenda for Sustainable Development.9 The use of 
AI  could  support  environmentally  beneficial  outcomes  for  instance  by  improving  prediction  as 

1  McKinsey  Global  Institute  estimated  that  by  2030  AI  technologies  by  2030  could  contribute  about  16%  higher 
cumulative  global gross domestic product (GDP) compared to 2018, or about 1.2% additional GDP per  year. For a 
comparison, the introduction of steam engines in the 1800s boosted labour productivity by 0.3% a year and spread of 
IT  during  the  2000s  by  0.6%  a  year,  cf.  ITU/McKinsey,  Assessing  the  Economic  Impact  of  Artificial  Intelligence, 
2018. 
2 https://ec.europa.eu/commission/sites/beta-political/files/political-guidelines-next-commission_en.pdf  
3 White Paper On Artificial Intelligence  – A European approach to excellence and trust,  19.2.2020, COM(2020) 65 
final. 
4 Ibid., p. 15. 
5 Report from the Commission to the European Parliament, the Council and the Economic and Social Committee on 
the  safety  and  liability  implications  of  Artificial  Intelligence,  the  Internet  of  Things  and  robotics,  19.2.2020, 
COM(2020) 64 final. 
6  Communication  from  the  Commission  of  29  January  2020,  Commission  Work  Programme  2020  –  A  Union  that 
strives for more‘, COM(2020) 37 final, Annex I, row 10 of the table. 
7  European  Parliament  resolution  of  20  October 2020  with  recommendations  to  the  Commission  on  a  civil  liability 
regime for artificial intelligence (2020/2014(INL)). 
8 COM(2021) 205 final. 
9 See Annex 3, sections 1.2(c) and 3. 

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well as optimising operations and resource allocation. 

Liability-related issues linked to AI are the subject of debate in various EU trading partners. As a 
consensual  regulatory solution  has not  yet emerged,  the EU  has  the opportunity to  influence the 
global  discussion  with  a  practical  and  constructive  approach  which  has  a  chance  to  become  a 
global model.   

In  terms  of  strategic  foresight,  the  AI  liability  initiative  takes  into  account  the  megatrend  of 
Accelerating technological  change and hyper-connectivity.  This  initiative  is  a crucial part of the 
EU’s  efforts  to  shape  the  AI-driven  transformation  in  a  human-centric,  yet  innovation-friendly 
way. The potential of AI underlines the importance of creating the right conditions for its rollout 
through  legal  certainty  and  consistent  rules  across  the  internal  market.  The  ubiquitous  and  fast 
evolving  nature  of  AI  also  means  that  the  risks  linked  to  the  deployment  of  AI  systems  can 
potentially affect a very broad range of stakeholders and legal interests. By adapting the traditional 
liability rules to the challenges of AI, the initiative anticipates the novel difficulties of proof faced 
by victims of harm, and aims to ensure that societal trust in AI is not diminished due to a lack of 
compensation in the future. Due to this forward-looking perspective of the AI liability initiative, a 
strategic foresight approach underlies the problem definition, policy objectives and policy options. 

1.2. Scope of the impact assessment: AI systems and their characteristics 

In order to ensure consistency, it is appropriate to use the same general concept of AI as in the AI 
Act10 also as the basis for the present initiative which is complementary to the AI Act.  

AI-specific  issues  regarding  liability  are  linked  to  certain  characteristics  of  AI  systems 
(opacity/lack  of  transparency  and  explainability,  autonomous  behaviour,  complexity,  continuous 
adaptation  and  lack  of  predictability)11  which  make  the  application  of  existing  liability  rules 
uncertain  or  more  difficult  (see  the  following  section  on  legal  context).  AI  systems  not  having 
such characteristics can be dealt with under the existing liability rules, similarly to other types of 
software. For example, rule-based algorithms are designed to automatically execute rules encoded 
by their programmers, following  the classical ‘if   then logic’  as  other types of software. Such 
algorithms do not put victims of harm in a fundamentally more difficult situation than other types 
of software. For example, robotic process automation in a factory  setting may rely on such rule-
based  systems.  In  contrast,  the  highly  autonomous  operation  of  e.g.  mobile  robots  in  more 
unpredictable environments (such as delivery robots) – often involving interaction with humans – 
will  typically  require  more  complex  AI  systems,  in  particular  developed  through  machine 
learning.  Such  AI  systems  are  expected  to  challenge  existing  liability  rules  and  are  therefore 
concerned by the present initiative.  

Their  operation  can  potentially  affect  legally  protected  interests  (for  example  AI  used  for 
recruitment, in autonomous vehicles, delivery robots, or drones). Certain AI-enabled products and 
services with  the potential to  cause harm are  already in  an advanced development  stage  or even 
starting  to  be  deployed,  such  as  e.g.  certain  mobile  robots  or  AI-systems  used  in  the  context  of 
recruitment.  However, AI systems  with  the specific characteristics challenging liability  rules are 
mostly not  yet  on the market,  among others because  they are not  yet  approved. Therefore, there 
are  no  available  judicial  cases  yet  where  the  problems  to  be  addressed  by  this  initiative  have 
materialised.  The  objective  of  this  proposal  is  to  create  the  legal  certainty  conditions  and  to 

10  In  the  proposed  AI  Act,  ‘AI  systems’  are  defined  as  software  developed  with  certain  techniques  and 
approaches (machine learning, logic- and knowledge-based approaches, statistical approaches etc.) that can, for 
a  given  set  of  human-defined  objectives,  generate  outputs  such  as  content,  predictions,  recommendations,  or 
decisions influencing the environments these systems interact with.   
11 See 2.2. 

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promote the societal trust needed for such AI systems to be brought to market in the EU. 

1.3. Legal context12 

Liability rules  determine how damage, caused by human activities and goods for which humans 
are considered liable by law, can be compensated. 

This initiative concerns ‘extra-contractual’ civil liability rules, i.e. rules providing a compensation 
claim irrespective of a contractual link between the victim and the liable person. Those rules will 
be referred to as ‘liability’ in the following. Criminal liability is not concerned.  

A  victim  currently  relies  on  national  liability  rules  and  in  certain  cases  on  the  Product  Liability 
Directive13  (PLD)  to  claim  compensation  for  damage  arising  from  AI-enabled  products  and 
services.  

Depending  on  each  case,  a  victim  has  several  avenues  available  for  claiming  compensation. 
Various  persons  could  be  held  liable  in  parallel  under  different  conditions  and  for  different 
reasons. Neither of these rules contains specific provisions for AI. 

Example:  A  traffic  accident  victim  may  have  in  parallel  a  fault-based  liability  claim  against  the  driver 
whose fault caused the accident, a ‘PLD’ claim against the producer if the vehicle had a defect, and a strict 
liability claim against the vehicle’s owner. 

Firstly,  the  victim  could  have  a  fault-based  liability  claim  based  on  another  person’s  wrongful 
behaviour, i.e. fault.14 A fault-based claim usually requires proving the existence of:  

-  a damage,  
-  a fault of the liable person, and  
-  causality between that fault and the damage.  

As a matter of principle, it is the victim that needs to prove these three elements.15  

Such  claims  apply  irrespective  of  the  technology/activity  involved  in  causing  harm  and  can  be 
addressed against any type of wrongdoer (e.g. businesses, including service providers, or private 
persons)  by  any  type  of  victim  (e.g.  business,  private  individual,  public  entity).  They  can 
compensate  any  type  of  harm  protected  by  national  law,  e.g.  physical  injury,  property  damage, 
damage to or loss of data, economic loss like lost profit, non-economic loss like pain/ suffering or 
harm caused by discrimination.16  

Secondly, in addition to fault-based claims, victims could rely on liability independent of fault  - 
so-called strict liability, with different scopes and conditions set by national laws.17  

Strict liability rules assign liability for the relevant risk to a person, irrespective of fault. This is 
usually justified by the fact that this person draws a benefit from exposing the public to that risk18. 

12 For further explanations, including on other relevant EU and international law instruments, see Annex 6. 
13  Council  Directive  85/374/EEC  of  25  July  1985 on  the  approximation  of  the  laws,  regulations  and  administrative 
provisions of the MS concerning liability for defective products (OJ L 210, 7.8.1985, p. 29). 
14 See Karner/Koch/Geistfeld, Comparative Law Study on Civil Liability for Artificial Intelligence, November 2020, 
p. 38 (https://op.europa.eu/publication/manifestation_identifier/PUB_DS0921157ENC). 
15  In  a  few  MS,  the  fault  is  presumed  once  the  other  two  elements  are  proven  by  the  victim;  several  fault-based 
regimes  contain  specific  rules  which  modify  the  premises  of  fault-based  liability  (especially  the  distribution  of  the 
burden of proving fault) (cf. ibid., p. 41 et seq). 
16 There are currently many differences between MS rules on the types of compensable harm, see Annex 9. 
17 Comparative Law Study, p. 59 et seq. 
18 Ibid, p. 58. 

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Normally, for that person to be found liable, the victim has only to prove that the risk stemming 
from the sphere of the liable person materialised. 

Thirdly, victims may have, under the PLD, a claim against the producer of a defective product for 
a defect present when the product was put on the market. The victim must prove that the product 
was defective and the causal link between that defect and the damage.  

Under  the  PLD,  the  victim  can  claim  compensation  for  personal  injury  or  damage  to  consumer 
property.  The  PLD  does  not  cover  damage  caused  to  property  intended  for  professional  use, 
damage caused during the provision of a service, damage to victims other than natural persons or 
claims  based  on  the  wrongful  use  of  a  product.  In  all  these  cases,  victims  can  only  get 
compensation according to national liability rules. 

Types of civil liability 

Scope 

National fault-based liability 
Any human actions or omissions, 
including manufacturing, providing 
services and using products 

Any wrongdoer: e.g. producer, service 
provider, user (both businesses and 
private persons), owner, cyber 
attackers, etc. 
Any victim: e.g. private persons, 
businesses, public entities 

Any damage, e.g. death, personal 
injury, damage to property (for private 
or professional use), pure economic 
loss, discrimination, non-economic 
loss like pain and suffering, depending 
on national law. 
Fault of a person + causal link between 
fault and damage 

Liable 
person 

Type of 
victim 
protected 
Type of 
damage 

What 
victim 
needs to 
prove 
Period of 
liability 

National strict liability regimes 
Operating specific technologies 
(e.g. operation of motor vehicles, 
aircrafts, nuclear power plants), the 
production/use of ‘dangerous 
products/activities’ or more general 
the use of objects.  
Owner or operator of technologies 
within scope 

PLD 
Producing defective products 

Producer (manufacturer, 
importer) and, in some cases, 
supplier/seller 

Any victim: e.g. private persons, 
businesses, public entities 

Natural persons 

Varies, e.g. harm to life, health and 
property. 

Death, personal injury, 
damage to consumer property 
worth more than EUR 500 

The risk subject to strict liability 
materialises (e.g. a motor vehicle is 
involved in accident) 

Defect in product (irrespective 
of fault) 

Varies 

Varies 

10 years after putting product 
into circulation 

A holistic approach to liability: Looking at all liability pillars 

The  liability  follow-up  to  the  AI  White  Paper  looks  holistically  at  the  three  avenues  to 
compensation currently available to victims. The purpose is to ensure that the specific challenges of 
AI do not lead for victims harmed by AI-systems to a lower level of protection, compared to victims 
harmed by traditional technologies, under all three avenues.  

For  the  purposes  of  presenting  the  problems  and  solutions  in  the  form  of  impact  assessments,  a 
distinction is drawn between, on the one hand, the route to compensation provided by the PLD, and 
on the other hand, the routes to compensation provided by non-harmonised national fault-based and 
strict liability rules. It is necessary to cover these complementary pillars of liability in separate, but 
closely coordinated impact assessments, because: 

-  The PLD and national liability rules provide liability claims based on different grounds, against 
different liable persons and for different types of victims and damages. This means that different 

4 

 
 
 
 
markets and different possible addressees of liability claims have to be considered as regards the 
PLD and national liability rules. The PLD covers only part of the harm which can be caused by 
AI-systems. It covers damage done by defective products, while other liability rules compensate 
also the harm caused for instance by services or any use of products. It covers the producer as 
liable person, while other liability rules cover the harm done by other actors like operators/users 
of AI-systems. It covers certain damages, while other liability rules compensate also other harm 
suffered by victims like economic and non-economic loss.  

-  The  PLD  evaluation  found  that  it  was  difficult  to  apply  its  rules  to  digital  technologies  in 
general,  including  AI-enabled  products.  It  found  also  that  the  rules  made  it  difficult  to  prove 
liability in the case of complex products, including AI-enabled products, but also others such as 
pharmaceuticals.  These  problems  call  for  technology-neutral  measures.  They  will  provide 
victims  of  damage,  including  that  caused  by  AI-enabled  products,  a  more  effective 
compensation claim against the producer, to ensure the same level of protection as for damage 
caused by other technologies or types of products.  

-  Digital technologies in general have not been identified as making it difficult to apply national 
liability  rules.  It  is  the  specific  features  of  certain  types  of  AI,  especially  its  autonomy  and 
opacity, which challenge the application of national liability rules. This needs to be addressed 
through targeted, AI-specific measures.  

-  Due to this difference between the problems, different objectives and policy options  had to be 
developed  respectively.  In  light  of  the  AI  White  Paper  objective  to  ensure  that  the  specific 
challenges  of AI do not  lead for victims harmed  by AI-systems  to  a lower level  of protection 
compared  to  victims  harmed  by  traditional  technologies,  this  is  an  expression  of  the  holistic 
Commission approach to examine all three pillars of liability. The PLD review is about adapting 
it to the digital age, preserving its technology-neutral nature and coverage. It also helps victims 
of damage caused by AI-enabled products to have a more effective compensation claim against 
the producer. The AI liability proposal tackles the AI-specific problems regarding other liability 
rules.  Together,  they  will  contribute  to  guaranteeing  the  effective  functioning  of  the  internal 
market in AI-enabled products/services and ensure that victims of harm caused by AI have the 
same level of protection as victims of harm caused by other technologies. 

1.4. Related policy initiatives 

The Commission is aiming to support the roll-out of AI in Europe by developing an ecosystem of 
trust.  In  accordance  with  the  White  Paper,  the  Commission  proposes  a  forward-looking 
comprehensive package of measures to address problems caused by the introduction and use of AI, 
comprising of three complementary work streams: 

- 

a  horizontal  framework  addressing  fundamental  rights  and  safety  risks  specific  to  AI  systems 
(AI Act);19  
- 
a revision of sectoral and horizontal product safety rules; 
-  EU rules to address liability issues related to AI systems. 

Interaction with already adopted proposals: The Commission has already proposed rules in the AI 
Act  reducing  risks  for  safety  and  fundamental  rights.  While  they  will  reduce  risks,  they  are  not 
intended  to  prohibit  the  placing  on  the  market  of  AI-systems  posing  a  residual  risk  to  safety  and 
fundamental rights.20 Harm will therefore still occur. When this happens, the liability rules subject 
to  this  impact  assessment  determine  which  party  is  liable  for  harm  and  under  which  conditions  a 

19 Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial 
intelligence (Artificial Intelligence Act) (COM(2021 206 final). 
20 Only a small number of AI use-cases are expressly prohibited by the AI Act. 

5 

 
 
 
                                                           
victim can be compensated.  

The AI Act also provides for definitions and specific documentation/logging requirements for high-
risk AI systems. The information from these documents and logs could be useful in liability claims 
but the AI Act does not foresee a right of victims to access that information.21  

The  Commission  has  also  put  forward  updates  of  general22  and  sectoral  product  safety  rules 
applicable  to  AI-enabled  machinery  products23  and  radio  equipment.24  These  initiatives  do  not 
contain provisions on liability or access to information for the purposes of damages claims. Their 
requirements  (e.g.  general  safety  requirement,  risk  assessment  or  regarding  human  oversight, 
testing, data quality) can be relevant for determining what constitutes fault of producers or what is a 
defective product in relation to AI – points which the AI liability proposal will not regulate.  

Safety  and  fundamental  rights  requirements  are  meant  to  prevent,  monitor  and  address  risks  and 
thus address societal concerns25. While they employ tools that address a potential wrongdoer, such 
as authorisations, controls, monitoring, administrative sanctions, they do not compensate the victim 
for the harm suffered. 

Therefore, safety and liability are two sides of the same coin: they apply at different moments and 
reinforce each other. Safety rules intend to reduce the risk of harm. Liability rules come in to ensure 
compensation  of  the  victim  when  harm  has  nevertheless  occurred.  Effective  liability  rules  also 
provide an  economic incentive  to comply  with  safety rules and  contribute therefore  to  preventing 
the occurrence of harm.26 

As AI is only gradually being rolled-out, there are not yet many documented cases of harm as the 
relevant  products/services  are  not  yet  on  the  market.  However,  as  part  of  the  Commission’s 
forward-looking and enabling approach for the uptake of this technology, action is needed to adapt 
the applicable liability rules in advance in order to increase trust and create the conditions for the 
roll-out of these products and services.  

Interaction with the pending revision of the PLD: The Commission takes a holistic approach in its 
AI  policy  to  liability  by  considering  adaptations  to  the  producer’s  liability  for  defective  products 
under the PLD as well as a targeted harmonisation of specific aspects of the other liability rules.  

These two policy initiatives are closely linked and form a package, as the claims within their scope 
deal with different types of liability. They complement one another to form an overall effective civil 
liability system. For AI, they achieve the AI White Paper objective that persons having suffered 
harm  caused  by  AI  systems  enjoy  the  same  level  of  protection  as  persons  having  suffered 
harm caused by other technologies. 

Example:  The  victim  may  decide  to  pursue  a  liability  claim  against  the  one  who  caused  the  damage  by 
operating the product (e.g. a service provider), based on strict or fault-based liability.  

21 For details on the interaction of this initiative with the AI Act, see Annex 7. 
22 Proposal for a Regulation of the European Parliament and of the Council on general product safety (COM/2021/346 
final). 
23 Proposal for a Regulation of the European Parliament and of the Council on machinery products (COM(2021) 202 
final). 
24 Commission Delegated Regulation (EU) 2022/30 supplementing Directive 2014/53/EU of the European Parliament 
and of the Council with regard to the application of the essential requirements referred to in Article 3(3), points (d), 
(e) and (f), of that Directive (OJ L 7, 12.1.2022, p. 6). 
25 For explanations on how this initiative addresses fundamental rights (in particular discrimination) risks, see Annex 
8. 
26 Commission SWD(2021) 84 final, Impact assessment accompanying the Artificial Intelligence Act, p. 88.  

6 

 
 
                                                           
The victim may also rely on product liability to claim compensation for losses as a result of material harm 
from the producer of the AI-product. In order to get compensation from the producer for damage not linked 
to material harm (such as pure economic loss or damage caused by discrimination), the same victim would 
need to invoke national liability rules outside the scope of the PLD, in particular fault-based liability. 

Together  they  will  deliver  the  ex-post  trust  element  in  AI  (and  other  digital  technologies),  by 
ensuring  that  victims  have  an  effective  chance  of  compensation  if  damage  occurs  despite  the 
preventive requirements of AI Act and the other safety rules.  

2.  PROBLEM DEFINITION 

2.1. Introduction and overview 

Liability  ranks  amongst  the  top  three  barriers  to  the  use  of  AI  by  European  companies  in  a 
representative survey of 2020. It is the most relevant external obstacle (43%) for companies that are 
planning to, but have not yet adopted AI.27 In the White Paper, the Commission counted liability-
related  issues  amongst  the  main  risks  related  to  the  use  of  AI,  because  victims  may  not  have 
effective  access  to  the  evidence  necessary  to  build  a  case  in  court  and  economic  operators  face 
uncertainty as regards the allocation of responsibilities.  

The accompanying Report identified those AI-specific characteristics that challenge liability  rules, 
(see  section  2.2.).  Economic28,  behavioural29  and  legal30  studies  commissioned  for  this  impact 
assessment,  as  well  as  comprehensive  stakeholder  consultations31,  have  shown  three  AI-specific 
problems:  legal  uncertainty  (see  2.3.),  increased  legal  fragmentation  (see  2.4.)  and  a  lack  of 
compensation for victims contributing to the lack of societal trust in AI use (see 2.5.), which in their 
combination cause internal market obstacles (see 2.6.). 

Delineation  from  the  problems  covered  by  the  parallel  impact  assessment  on  the  PLD  review: 
Under  the  PLD,  the  burden  of  proof  is  less  onerous  on  victims  than  under  national  fault-based 

27European  enterprise  survey  on  the  use  of  technologies  based  on  AI,  Ipsos  2020,  Final  report  p. 58 
(https://op.europa.eu/en/publication-detail/-/publication/f089bbae-f0b0-11ea-991b-01aa75ed71a1). 
28 Deloitte, Study to support the Commission’s IA on liability for artificial intelligence, 2021 (‘economic study’). 
29 Kantar, Behavioural Study on the link between challenges of Artificial Intelligence for MS’ civil liability rules and 
consumer attitudes towards  AI-enabled products and services, Final  Report 2021  (‘Behavioural Economics Study’); 
cf. also Special Eurobarometer Survey 460, ‘Attitudes towards the impact of digitisation and automation on daily life’, 
May 2017. 
30 Comparative Law Study. 
31 For details, see: Annex 2 on Stakeholder consultation. 

7 

 
 
 
                                                           
liability rules, because the victim does not have to prove fault. Nevertheless, proving that a product 
is  defective  (i.e.  that  it  does  not  provide  the  level  of  safety  a  person  is  entitled  to  expect)  is 
challenging for complex products, of which AI-enabled products are an example, even if the victim 
does not have to prove how the product became unsafe.  

As the PLD regulates producer liability for various different types of defective complex products, of 
which AI-enabled products are only a part, this problem is covered in the parallel impact assessment 
on  the  PLD  review.  It  will  be  addressed  by  amendments  of  the  PLD  regulating  in  a  horizontal 
manner all those products. 

2.2. Problem  driver:  the  specific  characteristics  of  AI  make  it  difficult  to  meet  the 

burden of proof 

The  three  problems  (uncertainty,  fragmentation  and  lack  of  compensation)  are  driven  by  the  fact 
that,  under  existing  liability  rules,  victims  normally  have  to  prove  the  necessary  conditions  for  a 
successful  claim.  Under  current  rules,  victims  generally  have  to  identify  the  possibly  liable 
person(s), the action/omission of that person which could be characterised as fault, and the causal 
link between that action/omission and the damage. There are neither targeted provisions  for AI to 
ease the victim’s burden of proof under fault-based claims, nor AI-specific strict liability regimes 
that would exempt the victim from making this proof. 

However,  certain  AI-systems  have  characteristics  that  make  it  prohibitively  difficult  or  even 
impossible  for  victims  to  provide  that  proof.32  The  specific  characteristics  that  challenge  current 
liability  rules  are: 
transparency  and 
increasingly  autonomous  behaviour,  opacity/lack  of 
explainability, complexity, continuous adaptation and lack of predictability.33 These characteristics 
make it very difficult to attribute directly the damaging output of the AI to an action/omission of a 
liable person, i.e. to identify: 

- 
- 

- 

the person who could possibly have done something wrong,  
the action or omission of that person which might not comply with the relevant standard of care, 
and  
the causal link between that action or omission and the victim’s damage.  

This is linked to two situations that can occur due to the use of AI systems:  

-  between the human actor(s) related to the AI system and the damage caused by this AI system 
to a victim, the AI-system takes an autonomous action which is not pre-determined by a human 
and/or  
the  way  the  AI  action  is  performed  cannot  be  understood  (mainly  due  to  the  opacity  and 
complexity of certain forms of AI).  

- 

Example:34  A  company  deploys  a  fleet  of  autonomous  cleaning  robots  to  provide  cleaning  services 
throughout a city. It tasks one of its employees with the remote supervision of the fleet. One of the cleaning 

32 Regarding the difficulty to identify a potentially liable person, see e.g. Renda e.a., Study to support an impact 
assessment of regulatory requirements for Artificial Intelligence in Europe, final report, 2021, p. 67. Concerning the 
difficulty to trace back the damage to an action or omission of that person, to prove human fault and establish a causal 
link with the damage, cf. Comparative Law Study, pp. 23 et seq. and 32. 
33 For more detailed explanations on these characteristics and the related challenges regarding  liability, see Annex 5. 
In order to avoid misunderstandings, it should be noted that the specific characteristics of certain AI systems are not 
referred to as grounds of liability but because these characteristics make it difficult for victims to prove the conditions 
of national civil liability claims. While the provider of the AI system is responsible for its design and can therefore be 
subject to a liability claim for this reason, the characteristics of the AI system can make it difficult to meet the burden 
of proof also in the context of claims against other parties. 
34 For details, see Annex 13. 

8 

 
 
                                                           
robots  fails  to  recognise  a  colourful  baby  stroller,  which  is  parked  in  front  of  a  similarly  patterned 
advertising  banner.  Because  of  the  collision,  the  baby  is  injured  and  the  stroller  is  damaged.  The  father 
witnessing  the  accident  suffers  psychological  trauma.  The  accident  could  be  due  to  a  variety  of  possible 
causes, e.g. an image segmentation error of the robot’s AI-based perception system, a failure by the provider 
of  the  AI  vision  component  to  provide  an  available  software  update  or a  failure  by the user  (the cleaning 
company) to install it, a failure by the human remote operator to appropriately monitor the operation of the 
fleet (possibly due to a malfunction of the human-robot interface) or also a deliberate attack on the robot’s 
sensors by a third party (jamming, spoofing, sabotage through adversarial machine learning etc.). 

Fault:  Given  the  cleaning  robot’s  highly  autonomous  mode  of  operation,  as  well  as  the  opacity  and 
complexity  of  the  different  AI  components,  it  is  highly  uncertain  that  the  victim  could  ascertain  relevant 
actions  or  omissions  of,  for  instance,  the  employee  charged  with  providing  the  cleaning  instructions  or 
monitoring the fleet of robots remotely. This would however be necessary to make a successful claim against 
the company. 

Causality: Assuming a wrongdoer’s fault can be established, it is uncertain whether and how the victim can 
prove  the  causal  link  between  such  faulty  behaviour  and  the  damage.  If  an  expert  has  access  to  logged 
information on inputs, outputs and internal states of the AI subsystems, she may be able to discard certain 
causes of the accident (e.g. jamming or spoofing of sensors) and to suppose certain correlations between, for 
instance, a detection failure and a control decision to move forward until colliding with the stroller. But due 
to the high degree of autonomy, complexity and the lack of explainability of the AI systems involved (not only 
the perception module but also e.g. the trajectory planning system and low-level controllers), it will likely be 
impossible  to  infer  a  clear  causal  link  between  any  specific  input  and  the  harmful  output.  In  view  of  AI 
specificities, to prove the necessary degree of likelihood is highly uncertain.35 

PLD: When claiming compensation under the PLD, the victim does not have to prove the producer’s fault, 
but  that  the  cleaning  robot  was  defective  (i.e.  it  failed  to  provide  the  level  of  safety  the  public  at  large  is 
entitled to expect) and the causal link between the defect and the harm. The victim would not have to prove 
how  the  cleaning  robot  became  defective;  whether  it  was  a  mechanical  or  software  flaw  is  irrelevant. 
However, the PLD has shortcomings when it comes to digital products: producers are not liable for defects 
that  emerge  after  the  product  was  put  into  circulation  (e.g.  if  the  defect  was  due  to  a  subsequently 
downloaded software module) and software producers themselves, including AI-system providers, cannot be 
pursued.  These  issues  are  dealt  with  in  the  PLD  impact  assessment,  as  they  concern  not  only  AI 
technologies. 

The PLD does not help the victim for any claims against other parties than the producer (e.g. the  cleaning 
company),  claims  based  on  other  grounds  than  defect  (e.g.  a  failure  to  appropriately  supervise  the  robot 
fleet),  and  claims  for  the  compensation  of  damage  not  covered  by  the  PLD  (e.g.  the  psychological  harm 
suffered by the baby’s father). 

The results of the public consultation confirmed the existence of this problem driver. Two out of 
three overall respondents agreed (67% or 153 out of 227, of which 46 % or 105 strongly) that it 
could be difficult to link damage caused by highly autonomous AI to a liable person (only 20% or 
47 out of 227 disagreed). The agreement among  citizens was even larger (84% or 78 out of 93) 
and  almost  unanimous  among  consumer  organisations,  national  ministries  and 
academic/research  associations  (94%  or  31  out  of  33,  with  only  one  disagreeing).  Among 
business stakeholders a relative majority did not agree with the difficulties to link the damage to 
a liable person (45% or 38 out of 85) while a strong minority (36% or 30 out of 85) agreed. 

Three  out  of  four  overall  respondents  agreed  (74  %  or  167  out  of  227,  of  which  76%  or  127 
strongly) that it could be difficult for victims to prove that the conditions of liability (fault, defect or 

35 The required proof of causality depends on the degree of likelihood (i.e. ‘How likely is it that this faulty behaviour 
caused the damage?’) required by each national law.  

9 

 
 
 
                                                           
causation) are fulfilled in the case of opaque and complex AI , while only 12% disagreed (26 out of 
227). The agreement among responding EU citizens was overwhelming (91% or 85 out of 93) and 
unanimous  among  responding  consumer  organisations,  national  ministries  and  research 
institutions. Even a relative majority among business stakeholders agreed with the difficulties of 
proof (41 %, or 35 out of 85, while 28% or 24 out of 85 disagreed). 

61% (or 139 out of 228) of overall respondents agreed (41,8 % or 95 out of 228 strongly) that this 
means less victim protection. The majorities among consumer organisations, national ministries, 
NGOs and research institutions (84% or 32 out of 38 with only one disagreeing) and EU citizens 
(79%  or  74  out  of  94)  were  even  larger.  Even  a  majority  of  business  stakeholders  agreed  that 
victims are less protected (53,6 % while 26,2 % disagreed).  

However, in situations where the liable person acted on advice or recommendations given by an AI 
system,  this  AI-specific  problem  driver  does  not  apply.  In  such  cases,  the  human  acting  on  the 
advice or recommendation will be responsible as there is a human action/omission, which can be a) 
identified, b) characterised as not complying with the relevant standard of care, and which would c) 
be the cause for a specific damage. This is for example the case with AI systems providing medical 
analysis  or  even  suggestions  for  diagnosis  and  treatment,  which  are  feeding  into  a  decision  on 
diagnosis and treatment, but that decision is ultimately taken by a human physician.36  

As AI-enabled products and services with the specific characteristics challenging liability rules are 
for the most part not yet on the market, it is not possible to quantify the scale of the problem or of 
its  relevance  for  different  market  segments.  The  economic  study  did  however  provide  some 
indications.37 The market share affected by legal uncertainty and legal fragmentation regarding AI 
liability  will  likely  differ  substantially  per  sector  and  is  potentially  larger  where  AI  technologies 
directly interact with humans and where – in the case of a failure or damage – large-scale negative 
effects can be expected. Potential bodily and psychological harm, property damage, economic loss 
and damage caused by discrimination present recurring risks linked to the use of AI across various 
sectors.38 The economic study found39 that the share of AI-enabled products and services potentially 
affected by legal uncertainty and legal fragmentation of civil liability rules ranges between ca. 20% 
in  sectors such as real  estate  or oil and gas  and  around 40% in  sectors such as human health and 
transport.           

2.3. Problem 1: Legal uncertainty  

As there are currently no rules addressing the AI-specific difficulties of proof, the general burden of 
proof conditions apply. This has several consequences, creating legal uncertainty.  

The  victim  and  the  possible  liable  person  wanting  to  check  liability  risks,  as  well  as  the  judges 
having to decide a liability claim, will be confronted with cases where they would need to interpret 
general  rules  which  were  not  designed  with  AI  in  mind.  They  would  have  to  apply  them  to  AI-
technologies  that  are  qualitatively  different  from  previous  technologies,  because  of  their  specific 
characteristics  (see  2.2.).  It  is  quite  uncertain  how  existing  liability  rules  would  be  interpreted  in 

36 For this, it is not necessary that the human understands for instance what exactly triggered the ‘wrong’ AI output. 
37 Based on a qualitative analysis differentiating between various economic sectors, the analysis took into 
account the percentage of enterprises perceiving liability issues as a barrier to the adoption of AI-enabled 
technologies as well as the potential of AI-enabled products and services to cause harm. Cf. Economic Study, 
pp. 104 et seq. 
38 Ibid, p. 109. 
39 By triangulating qualitative research findings with the results of the representative survey on the use of AI 
technologies (Ipsos, op. cit.) 

10 

 
 
                                                           
light of the AI-specific difficulties of meeting the burden of proving fault and causation.40  

Such difficulties would lead in all likelihood to the result that the victim would not be able to meet 
this  burden  of  proof.  Victims  may  therefore  decide  to  not  even  claim  compensation.  As  an 
economic consequence, they would have to bear the burden of the damage. 

If the case goes to court nevertheless, national courts may decide - on an ad-hoc basis - to stick to 
the traditional allocation of the burden of proof on the victim. This would lead in all likelihood to 
the  result  that  the  victim  would  lose  the  case,  not  obtain  compensation  and  bear  the  economic 
burden of the damage. If such a result is not considered as equitable, national courts may decide  - 
again on an ad-hoc basis - to adapt to the specificities of AI and alleviate the burden of proof of the 
victim, in order to achieve a fair outcome in each single case.  

These  uncertainties  surrounding  AI  liability  are  especially  relevant  in  a  cross-border  context, 
because MS’ existing burden of proof rules are already very diverse.41 The economic impact of this 
legal  uncertainty  is  demonstrated  by  legal  experts.  When  asked  to  estimate  the  costs  of  claiming 
compensation based on national liability rules for damage caused by AI, they submitted wide ranges 
to account for the prevailing uncertainty.42 

The  public  consultation  confirmed  this  problem:  a  very  large  majority  of  responding  consumer 
organisations,  NGOs  academic/research  associations  (84%  or  27  out  of  32,  with  none 
disagreeing) and EU citizens (85% or 80 out of 94, with only one citizen disagreeing) agreed that it 
is  uncertain  how  national  liability  rules  will  apply.  The  same  respondents  agreed  equally  broadly 
with the uncertainty how national courts will address the challenges of AI (91% or 84 out of 92 of 
EU  citizens  agreed,  with  none  disagreeing;  81%  or  25  out  of  31  agreement  amongst  the 
aforementioned  organisations,  with  only  one  disagreeing).  A  slight  majority  of  business 
stakeholders disagreed (52% or 44 out of 85) with the uncertainty as to how national liability rules 
will apply, while still almost a third confirmed it (32% or 27 out of 85). Again a third of business 
respondents agreed with the uncertainty as to how national courts will react to the challenges of AI 
(34% or 29 out of 85) while a stronger share (43%, or 36 out of 85) disagreed. 

In the PLD context, unclear rules, including for AI-enabled products, have equally been identified 
as a problem driver. This mainly concerns the problem that the PLD is ill-adapted to products in the 
digital  age  in  general.  It  imposes  neither  liability  on  software  providers,  including  AI-system 
providers, nor liability for defects emerging after a product is placed on the market, such as through 
software  updates,  or  ancillary  digital  services.  Legal  uncertainty  applies  horizontally  to  defective 
intangibles like software, digital content and ancillary services. For producers’ liability for defective 
products, including with respect to AI-enabled products, this problem and the measures needed to 
address it are discussed in the parallel impact assessment on the PLD revision. 

2.4. Problem 2: Legal fragmentation 

In a cross-border context, the law applicable to a liability claim is by default the law of the country 
in which the damage occurs.43 This means that different liability regimes and burden of proof rules 
could  be  applied  to  the  same  kind  of  AI-enabled  product/service  deployed  in  several  MS  which 
causes the same kind of damage.44 Consequently, a company disseminating/operating the same AI-

40 Comparative Law Study, pp. 34 et seq. and 48 and Expert Group Report, pp. 50-55. 
41 For examples, see Comparative Law Study, pp. 32 et seq. 
42 Economic Study, pp. 51 et seq. 
43 Article 4(1) of the Rome II Regulation. For more details see Annex 6. 
44  Cost  of  non-Europe  in  artificial  intelligence  –  liability,  insurance  and  risk  management’,  Study  by  the  European 
Parliamentary Research Service, June 2019, p. 39-41. 

11 

 
 
                                                           
enabled  product/service  in  various  MS  could  face  different  liability  exposures  if  that  product  or 
service  causes,  for  the  same  reason,  the  same  type  of  damage  in  several  MS.45  Aside  from  the 
general burden of proof rules, there are also different rules about information disclosures in liability 
claims. This means that victims face greater information asymmetries in some MS.46 

In  addition,  national  AI  strategies  show  that  several  MS  are  considering,  or  even  concretely 
planning, legislative action on civil liability for AI.47 The fact that in the public consultation on the 
AI White Paper, more than 63 % of respondents were in favour of adapting national liability rules 
for  all  or  for  specific  AI  applications  suggests  that  MS  are  likely  to  face  pressure  to  address  the 
issue of liability for damage caused by AI.48 

While the fragmentation of MS’ existing liability and burden of proof rules is as such not limited to 
cases involving AI, the likely adoption of AI-specific liability rules by at least some MS exacerbates 
the  problem  of  fragmentation  with  respect  to  AI-enabled  products  and  services  in  particular. 
Outside  the  scope  of  the  PLD,  tort  law  is  currently  not  harmonised  and  MS’  rules  diverge 
substantially. This  may  create internal  market  obstacles also  for traditional products  and services. 
Nevertheless, it is crucial to address this issue specifically for AI, for the following reasons: 

-  The Commission aims to create the right conditions specifically for the roll-out of AI-equipped 
products  and  services.    According  to  the  Commission’s  ‘staged  approach’,  this  involves  two 
complementary  and  synergetic  initiatives:  in  a  first  step,  the  AI  Act  proposal  to  ensure  the 
effectiveness of safety and fundamental rights protection, and in a second step, the AI liability 
proposal to ensure effective protection when AI nevertheless causes harm. 

-  Stakeholders  active  in  the  field  of  AI  will  be  especially  affected  by  the  market  barriers 

associated with legal fragmentation (see 2.6.).  

-  As a key enabling technology, the importance of which can be compared with the introduction 
of electricity, AI bears a significant potential for economic growth and societal usefulness. It is 
therefore essential to address the obstacles entailed by legal fragmentation in order to  reap the 
full potential of AI within the internal market and for the digital transition. 

-  Given  the  entrenched  and  longstanding  differences  between  national  legal  traditions,  any 
attempt to address the problem of legal fragmentation by way of a broad stroke harmonisation 
of  tort  law  would  not  be  promising.  Focussing  on  AI  specific  problems  and  addressing  them 
with  targeted  adaptations  to  specific  aspects  of  national  laws  provides  an  opportunity  to 
approach  the  problem  of  fragmented  civil  liability  rules  in  an  effective  and  future-oriented 
manner.   

The  existence  of  diverging  approaches  to  easing  the  burden  of  proof  in  cases  involving  complex 
products  also  forms  part  of  the  problems  affecting  the  PLD,  as  it  leads  to  differing  levels  of 
consumer  protection  (section  2.2.2.  in  the  PLD  impact  assessment).  Since  the  PLD  problems 
concern not  only  AI-enabled products  but  also  other complex products,  they  are dealt with  in  the 
impact assessment on the PLD revision. 

2.5. Problem 3: Lack of compensation for victims leading to a lack of societal trust and 

hampering uptake of AI-enabled products and services 

45 This problem was confirmed by legal experts who, when consulted about the costs of claiming compensation for 
damage caused by AI, submitted widely differing estimates across MS, cf. Economic Study, pp. 51 et seq. 
46 See CSES, Impact assessment study on the possible revision of the Product Liability Directive, 2022, p. 54. 
47 For an overview see e.g. European Parliamentary Research Service, Civil liability regime for artificial intelligence, 
September  2020,  Table  18,  p. 45;  AI  Watch,  National  strategies  on  artificial  intelligence  –  a  European  perspective 
(JRC-OECD report), 2021. 
48 Around 80 % of those public authorities and SMEs that took a position on this question were in favour of adapting 
national liability rules. See Public consultation on the AI White Paper – Final Report, p. 14 (see here). 

12 

 
 
                                                           
As already explained (see 2.2.), some characteristics of certain AI systems challenge the application 
of  liability  rules  and  may  make  it  prohibitively  difficult  or  even  impossible  for  victims  to  get 
compensation. Victims are likely to incur significantly higher up-front costs and face significantly 
longer legal proceedings, when claiming compensation for damage caused by AI-enabled products 
or services, compared to cases not involving AI applications.49 Substantial burdens on victims are 
anticipated, due to the specific challenges of AI, for almost all national legal systems covered by the 
analysis  commissioned  for  this  impact  assessment,  often  going  beyond  a  100 %  increase  of  fees 
paid  for  legal  and  technical  expertise.50  This  could  deter  victims  from  claiming  compensation. 
Ultimately  victims  may  be  left  under-  or  even  uncompensated.  AI-specific  barriers  are  therefore 
likely to impinge on the right to an effective judicial remedy.51 As a consequence, the compensation 
function  of  liability  law  would  fail.  This  would  undermine  the  fundamental  concept  of  justice  as 
fairness  (‘distributive  justice’)  that  underpins  our  legal  order  and  citizens’  trust  in  the  justice 
system.  

If  victims  are  left  without  compensation  when  AI  is  involved,  they  would  find  themselves  at  a 
disadvantage compared to a situation where traditional technology would have caused the damage. 
Such an AI-induced lack of compensation would be particularly problematic if future autonomous 
technologies would create a significant risk of harm to life, health and significant property values, 
due  to  the  important  legal  interests  at  stake.  The  need  to  ensure  effective  compensation  is  all  the 
more  pressing  where  unwitting  third  parties  are  exposed  to  such  risks,  as  such  victims  cannot 
choose to accept or avoid the risk of damage. 

A behavioural  study  commissioned for this impact  assessment has shown that the expected lower 
likelihood  of  receiving  compensation  if  AI  applications  cause  damage  is  amongst  the  reasons  for 
low  levels  of  consumer  trust  in  such  applications.52  Conversely,  the  availability  of  effective 
remedies and compensation is one of the factors increasing the level of societal acceptance of, and 
consumer  trust  in,  AI-enabled  products  and  services.53  Another  recent  survey  confirmed  that 
consumers have clear concerns regarding the allocation of responsibility and liability if something 
goes wrong. It concluded that these concerns must be properly addressed to ensure strong protection 
for consumers and for them to trust this technology.54 A statistically representative Eurobarometer 
of 2019 points in the same direction: EU citizens’ most widespread concern about the use of AI was 
that it could cause situations where it is unclear who is responsible.55  

The aforementioned behavioural research demonstrated that a lower level of trust in AI applications 
and the perception that persons suffering damage caused by AI-enabled products and services are 
less likely to obtain compensation correlate with a lower willingness to take up such products and 
services.56  Businesses  also  consider  that  the  lack  of  citizens’  trust  is  one  of  the  most  relevant 

49 Economic Study, pp. 51 et seq.  
50 For example for the Austrian legal system, minimum lawyers’ fees were estimated to be ca. EUR 6 500 higher in 
liability cases involving  AI and  maximum lawyers’ fees ca. EUR 58 000 higher. For all legal systems covered, see 
ibid, Annex B, tables 13 et seq. The additional costs of technical expertise in cases involving AI compared to other 
cases was used as a proxy to quantify the costs linked to the burden of proof, see Annex 10, 2.1.3. (d). 
51 Ibid., p. 66; Expert Group Report, p. 35. 
52 Behavioural Economics Study, op. cit. p. 24-28. 
53 The difficulty to determine who is responsible in cases of damage and the perceived low likelihood of compensation 
count amongst the strongest reasons for a lack of trust in AI-enabled products and services, cf. ibid., pp. 24 et seq. and 
33. 
54  See  BEUC,  ‘Artificial  Intelligence:  what  consumers  say’,  http://www.beuc.eu/publications/beuc-x-2020-
078_artificial_intelligence_what_consumers_say_report.pdf  
55 43% of respondents in Standard Eurobarometer 92, Europeans and Artificial Intelligence, 12/2019, p. 16. 
56 Behavioural Economics Study, op. cit., p. 47 and 48. 

13 

 
 
                                                           
obstacles to the take-up of AI-technologies in Europe.57 Other factors, such as the perceived risk of 
accidents, were also shown to have a significant effect on the level of societal trust and consumer 
uptake. These findings are consistent with the complementary role of the AI Act / safety legislation 
and liability rules in creating an ecosystem of trust for AI. 

In  the  public  consultation,  a  clear  majority  of  overall  respondents  confirmed  that  the  lack  of 
adaptation of existing liability rules to AI may negatively affect trust in AI (60% or 135 out of 227) 
and  the  uptake  of  AI-enabled  products  and  services  (56%  or  126  out  of  227).  In  particular, 
responding  NGOs  consumer  organisations,  academic/research  institutions  and  EU  citizens 
overwhelmingly confirmed these problems: 81% (or 77 out of 95) of citizens and 89% (or 16 out of 
18)  of  these  organisations  anticipate  a  negative  effect  on  trust  in  AI;  79%  (or  74  out  of  94)  of 
citizens and 78% (or 14 out of 18) of these organisations expect a negative effect on AI uptake. By 
contrast,  58 %  (or  49  out  of  85)  of  responding  business  stakeholders  (business  associations  and 
companies/business organisations) did not uphold them, while still a quarter of them confirm them 
(27 % or 23 out of 85). 

2.6. The consequences: internal market obstacles and affected stakeholders  

(a) 

 Directly affected stakeholders 

Potentially  liable  companies:  The  representative  2020  IPSOS  survey  of  European  companies 
showed  that  liability  for  potential  damages  is  the  most  recurrent  barrier  to  the  adoption  of  AI 
technologies.58 The economic study found that a majority of businesses active in the market for AI-
enabled  products  and  services  perceive  their  liability  exposure  outside  the  scope  of  the  PLD  as 
uncertain to varying extents.59  

Where  companies  are  considering  a  cross-border  rollout  of  AI-enabled  products  or  services,  the 
costs and burden linked to legal uncertainty are amplified by fragmentation between MS’ liability 
rules. This means that such companies have to carry out complex assessments and quantifications of 
liability risks for each relevant MS. A significant share of businesses active in the AI market does or 
will  incur  costs  due  to  legal  fragmentation  and  uncertainty  regarding  the  application  of  national 
liability  rules  to  damage  caused  by  AI:60  legal  information/representation  and  compliance  costs, 
internal  risk  management  costs,  opportunity  costs  in  the  form  of  foregone  revenue  due  to 
companies’ hesitation to explore new markets.61  

Moreover, liability rules may sometimes have a bearing on which technological solution is favoured 
by  developers  of  novel  products  and  services62  and  may  influence  how  products  and  services  are 

57 71 % of businesses that have already adopted AI identified lack of trust as a barrier, cf. Ipsos 2020, op. cit., p. 57. 
Already  in  2017,  stakeholders  considered  public  attitude  towards  and  acceptance  of  robotics  and  AI  as  the  second 
most  relevant  obstacle  preventing  the  full  development  of  those  technologies,  cf.  the  summary  of  the  public 
consultation  of  the  European  Parliament  in  2017  at  https://www.europarl.europa.eu/cmsdata/130181/public-
consultation-robotics-summary-report.pdf. A Eurobarometer survey of the same year revealed that the unsatisfactory 
uptake  of  AI-enabled  products  and  services  over  the  preceding  years  coincides  with  a  decline  in  the  proportion  of 
respondents 
at: 
https://ec.europa.eu/jrc/communities/sites/jrccties/files/ebs_460_en.pdf. 
58 Cf. Ipsos, op. cit., p. 12. 
59 Economic Study, pp. 71 et seq. 
60 Economic Study, p. 91. 
61 Economic Study, pp. 113 and 114.  
62  Cf.  “Study  on  Safety  of  non-embedded  software;  Service,  data  access,  and  legal  issues  of  advanced  robots, 
autonomous, connected, and AI-based vehicles and systems”, SMART 2016/0071, TNO 2019-R10095 – Final Study 
Report regarding CAD/CCAM and Industrial Robots p. 31. 

available 

positive 

having 

robots 

view 

and 

AI, 

of 

a 

14 

 
 
                                                           
designed.63  Going  cross-border may therefore involve costly technological adaptations  that thwart 
economies of scale. Companies deterred from investing in AI-technologies miss out on benefits in 
terms of increased profits or efficiency gains.64  

The  economic  study  estimated  that  the  total  values  of  the  EU27  AI  market  affected  by  legal 
uncertainty and legal fragmentation ranges from EUR 1.739 billion to EUR 4.973 billion in 2021.65 
These shares of the total AI market in the EU27 were obtained by multiplying the value of the AI 
market  per  sector  with  the  respective  shares  affected  by  legal  uncertainty  and  fragmentation.  To 
ensure  consistency,  the  estimated  AI  market  sizes  assumed  for  the  impact  assessment 
accompanying the proposed AI Act were taken as a starting point, representing overall investments 
in  AI  technologies.66  For  the  sake  of  robustness  and  in  order  to  account  for  uncertainty,  these 
estimates  allow  for  a  wide  range  of  market  definitions  and  possible  future  developments.67  The 
shares affected by legal uncertainty and fragmentation were determined based on a sector-by-sector 
analysis,  taking  into  account  the  ability  of  the  AI  applications  deployed  in  the  respective  sector 
(now or in the foreseeable future) to cause harm and thus give rise to liability claims.68    

The  EP  concluded  in  2020  that  fragmentation  and  uncertainty,  relating  to  the  liability  provisions 
applicable across the EU, that may emerge in the absence of a common EU approach can provide 
negative incentives for innovation and diffusion of AI systems while also contributing to excessive 
costs  for  consumers.69  These  internal  market  obstacles  will  likely  contribute  to  slowing  down  the 
innovation that drives the overall digital transition.  Legal uncertainty and fragmentation regarding 
civil liability presents significant barriers across all sectors70 and both to products and services.71 

The  results  of  the  public  consultation  confirm  these  consequences.  A  clear  majority  of  overall 
respondents  expect  that  legal  fragmentation  and/or  the  lack  of  adaptation  with  respect  to  civil 
liability for AI will, for instance, entail at least to some extent additional costs for companies (e.g. 
legal  information  costs,  insurance  costs)  (75%  or  153  out  of  205)  and  the  need  to  adapt  AI 
technologies, distribution models and cost-management models (70% or 146 out of 205).  

63  See  e.g.  Galasso/Luo,  Punishing  Robots  –  Issues  in  the  Economics  of  Tort  Liability  and  Innovation  in  Artiﬁcial 
Intelligence,  in:  Agrawal/Gans/Goldfarb (eds.), The economy of artificial intelligence:  an agenda,  Chicago 2020, p. 
493(501). 
64 The McKinsey Global Institute found that uncertainty has a direct negative effect on the propensity to invest in AI 
technologies See ‘Notes from the AI frontier – Tackling Europe’s gap in digital and AI‘, op. cit., fn. 52. 
65  Economic  Study, p. 112.  These  numbers  refer  to  the  AI  market  sizes  as  estimated  for  the  purposes  of  the  IA 
accompanying the AI Act. 
66 Renda e.a. (2020), Study to Support an Impact Assessment of Regulatory Requirements for Artificial Intelligence in 
Europe. 
67 The lower estimate of the AI market size (EUR 5.404 billion in 2021) stems from  Allied Market Research (2018). 
Artificial  Intelligence  Market.  Artificial  Intelligence  Market  Size,  Growth 
|  AI  Market  Forecast  -  2025 
(alliedmarketresearch.com).  The  higher  estimate  (EUR 15.451  billion  in  2021)  stems  from  Grand  View  Research 
(2020).  Global  Artificial  Intelligence  Market.  Artificial  Intelligence  Market  Size  &  Share  Report,  2020-2027 
(grandviewresearch.com). 
68  For  example,  in  the  area  of  mobile  robotics,  a  risk  of  physical  harm  was  taken  into  account;  in  the  area  of 
recruitment services, a risk of damage caused by discrimination was assumed; in the health sector, a risk of physical 
harm exists, etc. 
69 EPRS, Civil liability regime for artificial intelligence  - European added value assessment, September 2020, p. 48: 
https://www.europarl.europa.eu/thinktank/en/document/EPRS_STU(2020)654178. 
70 Economic Study, pp. 104 et seq. For the shares of affected AI-enabled activities per sector, and absolute sizes of the 
affected  market  shares,  see  ibid.,  pp. 110-112  and  121.  In  absolute  terms,  the  market  shares  affected  by  legal 
uncertainty and fragmentation regarding civil liability are the biggest in the manufacturing sector (EUR 226-699 mio. 
in 2020 / EUR 1 925-4 026 mio. in 2025) and other technical or scientific sectors (EUR 100-309 mio. in 2020 / EUR 
924-1 932 mio. in 2025). 
71 Examples of affected activities include the  provision and use  of predictive  AI tools in  the agricultural sector, the 
operation of AI equipment in manufacturing, AI-enabled solutions to remotely monitor  work sites in the oil and gas 
sector, speech and text recognition tools in the IT sector, automated waste management, etc. 

15 

 
 
                                                           
SMEs in particular: Start-ups and other SMEs are likely to be significantly more affected by legal 
uncertainty and fragmentation than large companies: they cannot rely on comparable in-house legal 
expertise  and  an  established  cross-border  network.  As  smaller  companies  often  lack  sufficient 
external funding, the additional costs incurred due to legal uncertainty may be prohibitive for start-
ups and other SMEs, or at least reduce their competitiveness in relation to large companies.72 These 
effects are particularly weighty because the EU market is driven by SMEs developing, deploying or 
using AI technologies. It is estimated that SMEs account for more than 95 % of companies active in 
this market, with micro-enterprises representing over 80 % of all firms involved in AI research and 
software development.73 Yet it is estimated that only 19 % of micro-enterprises active in the internal 
AI market are involved in cross-border activities (as opposed to 68 % of large corporations).74 This 
points to a significant unused internal market potential.75 As most start-ups and other SMEs are not 
using the potential of the single market to scale up, the fragmentation of the AI landscape, notably 
in terms of regulations, impedes the creation of a regional ecosystem with bigger scale.76 

In  the  public  consultation,  business  associations  representing  SMEs  confirmed  the  relevant 
internal  market  obstacles  to  a  larger  extent  than  other  business  stakeholders.  The  responding 
individual SMEs also agreed that the lack of adaptation and fragmented AI-specific liability rules 
at  national  level  will  increase  companies’  costs,  insurance  premiums,  entail  higher  prices  of  AI-
enabled products and services and cause companies to refrain from using AI. 

Insurance companies: Legal uncertainty regarding the liability exposure of insurance holders can 
make  it  more  difficult  to  calculate  premiums  and  offer  coverage,  in  particular  for  cross-border 
activities and for situations covered by fault-based liability.77 Such difficulties affect the insurance 
companies  themselves.  But  they  are  also  of  concern  to  the  companies  facing  unpredictable  –  and 
potentially  very  high  –  liability  exposure  when  unable  to  procure  desired  insurance  coverage. 
Finally,  they  also  affect  the  victims  bearing  the  liable  party’s  insolvency  risk.  Moreover,  liability 
insurance  premiums  depend,  to  some  extent,  on  insurers’  prospect  of  successful  redress  claims 
allowing  them  to  claim  back  the  amounts  they  paid  out.78  Outside  the  scope  of  the  PLD,  such 
redress claims rely to a large extent on fault-based liability, which means that insurance companies 
will face significant uncertainty in estimating their own chances of success on the redress level for 
damage caused by AI-enabled products and services. 

Victims  of  damage  caused  by  AI-technologies  may  be  (i)  private  persons,  businesses  or  public 
entities  owning  AI-enabled  products  or  using  AI-enabled  products  and  services  and  (ii)  natural 
persons,  businesses  or  public  entities  without  any  link  to  those  products  and  services  (third-party 
damage). See 2.5. for detailed explanations on the difficulties faced by victims. 

(b)  

Indirectly affected stakeholders 

Consumers: As explained under 2.5., a lack of compensation of damage caused by AI is likely to 
contribute  to  low  levels  of  consumer  trust  and  demand  of  AI-enabled  products  and  services.79  If 
restrained consumer demand, coupled with legal uncertainty and fragmentation faced by businesses, 
delay the roll-out of novel AI-technologies in the internal market, consumers would, to some extent, 
be deprived of the benefits promised by AI. They could miss out on benefits like faster and more 

72 Economic Study, p. 122. 
73 Ibid, p. 115. 
74 Ibid., p. 117. 
75 Ibid., pp. 118 et seq. 
76 See ‘Notes from the AI frontier – Tackling Europe’s gap in digital and AI‘, McKinsey, op. cit., p. 47. 
77 ‘Access to insurance for services provided in another Member State’, SWD(2014) 130 final, 31/3/2014 p. 7. 
78 Economic Study, p. 142. 
79 Behavioural Economics Study, op. cit. p. 24-28. 

16 

 
 
                                                           
personalised  services,  innovative  and  performant  products  as  well  as  advances  in  the  fields  of 
health, safety, security, mobility, sustainability, media, etc. 

Companies  active  in  AI:  The  trust  deficit  and  the  consequential  lower  uptake  of  AI-enabled 
products and services is likely to affect all companies with a stake in the rollout of AI in the form of 
lost  market  opportunities.  This  includes  also  those  entities  in  the  AI  value  chain,  such  as 
researchers,  developers  and  providers  of  related  technologies  or  equipment  (e.g.  cloud  service 
providers), who are likely to face reduced demand. 

General economic consequences: If victims are left to bear the burden of damage caused by AI, the 
overall cost allocation of introducing this technology is inefficient, as this cost is borne by those that 
neither can control the risk nor benefit from it. Burdening victims disproportionately and allowing 
for  an  externalisation  of  costs  linked  to  the  roll-out  of  AI  would  create  inefficient  incentives.  It 
would  also  undermine  the  innovation  potential  linked  to  the  development  of  safe  AI-enabled 
products and services.80  

In an even broader sense, the existence of obstacles to the functioning of the internal market in AI-
enabled  products  and  services  affects  the  European  economy  and  society  as  a  whole.  The  cross-
sector relevance of AI was confirmed by a representative survey of 2020, showing that at least 35 % 
of EU companies from each of the sectors covered have adopted AI technologies.81 The identified 
liability-related obstacles are likely to have a negative impact on the overall growth and innovation 
capacity  of  the  European  market.  The  fact  that  EU  companies  cannot  take  full  advantage  of  the 
internal  market  prevents  them  from  becoming  more  competitive  on  a  global  scale,  and  may  thus 
further weaken the EU’s position vis-à-vis its competitors in the global AI race (primarily the US 
and China), as the EU already lags behind them82.  

By putting forward a balanced approach regarding liability for damage caused by AI, the EU has 
the opportunity to set a global benchmark and ultimately generate a competitive advantage for ‘AI 
made in Europe’. 

While  the  longer  term  impact  of  Covid-19  on  the  situation  of  the  internal  market  for  AI-enabled 
products  and  services  is  still  somewhat  uncertain,  the  pandemic  has  expedited  the  trend  towards 
increased digitalisation, and economic growth has picked up strongly in 2021 and 2022.83 The war 
in Ukraine is likely to have a significant negative effect on the EU economy, and forecasts will have 
to  be  reviewed  taking  into  account  his  factor.  However,  at  the  time  of  submitting  this  impact 
assessment no updated estimates were available yet. 

General societal consequences: A liability framework which is significantly more difficult to apply 
when  AI  is  involved  will  not  sufficiently  motivate  risk  takers  (producers,  service  providers, 
operators, users, etc.) to make efforts to avoid harm to others. In particular, inefficient liability rules 
will  not  sufficiently  motivate  compliance  with  safety  rules  or  with  the  ex-ante  requirements 
introduced by the AI Act.  

Compensation gaps could call into question citizens’ trust not only in AI, but also in the ability of 
the legal and judicial system to ensure fair and equitable results. 

2.7. Likely evolution of the problems without Union intervention 

Legal  uncertainty  regarding  the  application  of  national  liability  rules  might  to  a  certain  extent 

80 Economic Study, pp. 188 et seq. 
81 Ipsos 2020, op. cit. 
82 Economic Study, pp. 32 et seq. 
83 Cf. the Winter 2022 Economic Forecast.  

17 

 
 
                                                           
diminish  in  the  long  term  as  MS  adopt  AI-specific  liability  rules  or  national  courts  clarify  the 
interpretation of existing rules. However, the adoption of AI-specific liability rules at the national 
level  would  in  all  likelihood  exacerbate  the  problem  of  fragmentation  since  such  rules  would  be 
different and not benefit from a harmonised interpretation by the European Court of Justice.  

Judicial solutions adopted by national courts are by nature limited to the particular case at issue and 
in most cases do not bind other courts, which in turn can adopt different solutions. Therefore, legal 
uncertainty  for business  and victims’ difficulties  in  claiming  compensation will persist,  to  a  large 
extent, while fragmentation in the internal market will increase.  

The AI Act and various safety related rules84 aim to improve the availability of relevant information 
on, and the transparency and explainability  85 of high-risk AI systems, the safety of those systems 
and  the  effective  protection  of  fundamental  rights.  The  requirements  of  the  AI  Act  and  future 
detailed standards providing details on these requirements can help to determine the duties of care 
for  fault-based  claims.  The  information  to  be  documented  and  the  logged  records  of  the  system 
pursuant  to  the  AI  Act  can  be  useful,  if  the  victim  has  access  to  it,  for  establishing  liability. 
However, these proposals do not contain provisions designed to facilitate the victim’s claim, e.g. on 
the  burden  of  proof  or  access  to  information.  They  will  therefore  not  remove  the  AI-specific 
problem  drivers  in  the  context  of  liability.  For  this  reason,  the  impact  assessment  for  the  AI  Act 
underlined the importance of combining that initiative with specific liability-related measures.  

It is to be expected that the problem drivers of the difficulty of claiming compensation linked to e.g. 
the  autonomy,  opacity  and  complexity  of  certain  AI  systems  will  become  more  relevant  in  the 
future, as increasingly independent AI-systems will carry out ever more advanced tasks (see 5.1.).  

The  negative  effect  of  a  perceived  lack  of  compensation  on  societal  trust  is  likely  to  further 
increase,  if  the  risks  posed  by  AI-enabled  technologies  materialise  in  practice  and  the  public 
becomes  increasingly  aware  of  difficulties  in  claiming  compensation.  Accidents  involving  AI-
enabled products often receive a very large amount of public attention.86 Behavioural research has 
shown that the level of consumers’ anxiety towards the use of AI applications and their likelihood 
to  take  up  AI-enabled  products/services  is  correlated  with  their  exposure  to  media  coverage  of 
accidents  involving  such  products/services.87  This  effect  could  already  be  observed  e.g.  by  the 
change  in  public  perception  of  self-driving  vehicles  following  high-profile  crashes.88  It  is  very 
likely  to  become  more  relevant  with  the  increased  rollout  of  AI  applications  capable  of  causing 
harm.  

As the market for AI-enabled products and services is expected to grow substantially, and AI can be 
implemented in more and more situations and sectors,  the economic and societal relevance of the 
identified problems is also likely to increase in absolute terms.89 The economic study concluded that 
the  sum  of  the  AI  market  share  affected  by  liability  issues  will  increase  at  a  compound  annual 

84  See  Annex  7  for  a  more  detailed  analysis  of  how  the  proposed  AI  Act  is  taken  into  account  for  the  baseline 
scenario, and the interplay with the proposed AI Act.  
85  For  approaches  to  improve  explainability,  see  Joint  Research  Centre  Technical  Report,  Robustness  and 
Explainability of Artificial Intelligence – From technical to policy decisions, 2020, point. 3.2.2. et seq. and footnotes 
49 et seq. 
86  Cf.  e.g.  J.W.  Hong,  Why  Is  Artificial  Intelligence  Blamed  More?  Analysis  of  Faulting  Artificial  Intelligence  for 
Self-Driving  Car  Accidents  in  Experimental  Settings,  International  Journal  of  Human–Computer  Interaction,  2020, 
DOI: 10.1080/10447318.2020.1785693.  
87 Behavioural Economics Study, op. cit., pp. 43, 47, 87. 
88 See https://www.theverge.com/2018/5/22/17380374/self-driving-car-crash-consumer-trust-poll-aaa 
89  Any  suggestion  that  the  expected  increase  in  absolute  terms  of  the  market  value  potentially  affected  by  liability-
related  issues  might  be  outweighed  by  attenuating  factors  such  as  increased  safety  requirements  applicable  to  AI 
systems is not supported by evidence; cf. Economic Study, p. 113. 

18 

 
 
                                                           
growth rate of 44% – 56% until 2025.90  

3.  WHY SHOULD THE EU ACT? 

3.1. Legal basis 

The  initiative  constitutes  a  core  part  of  the  EU  AI  strategy.  The  problems  identified  in  section  2 
(legal  uncertainty,  legal  fragmentation,  lack  of  compensation  diminishing  consumer  trust)  hinder 
the  development  of  the  internal  market  and  thus  amount  to  significant  obstacles  to  cross-border 
trade in AI-enabled products and services.91  

On  the  one  hand,  these  obstacles  stem  from  the  fact  that  businesses  that  want  to  produce, 
disseminate and operate AI-enabled products and services across borders are uncertain whether and 
how existing liability regimes would apply to damage caused by AI. This uncertainty concerns not 
only their own MS but particularly other MS where they will export to or operate their products and 
services. This assessment was recently confirmed by the comparative law study.92 Certainty would 
be  necessary  for  these  businesses  to  know  the  relevant  liability  risks  and  to  be  able  to  insure 
themselves against them. 

On the other hand, there is concrete evidence showing that a number of MS would take unilateral 
legislative  measures  to  address  the  specific  challenges  posed  by  AI  with  respect  to  liability.  For 
example,  AI  strategies  adopted  in  Italy93,  Malta94,  Czech  Republic95,  Poland96  and  Portugal97 
mention initiatives to clarify liability. Given the already existing large extent of differences between 
MS’ existing civil liability rules, it is very likely that any national AI-specific measures on liability 
would follow existing different national approaches and therefore increase such differences.   

In  a  cross-border  context  the  law  applicable  to  a  non-contractual  obligation  arising  out  of  a 
tort/delict is by default the law of the country in which the damage occurs.98 Thus, adaptations of 
liability  rules  taken  on  a  purely  national  basis  would  increase  the  barriers  to  the  roll-out  of  AI-
enabled products and services across the internal market and contribute further to fragmentation.  

In light of the  existing situation and the content  and aim of the planned initiative, the appropriate 
legal  basis  is  Article  114  TFEU.  This  initiative  addresses  the  abovementioned  obstacles  by 
harmonising targeted aspects of MS’ existing civil liability rules applicable to AI-systems, in order 
to  improve  the  conditions  for  the  functioning  of  the  internal  market  in  AI-enabled  products  and 

e 

la 

del 

per 

2025 

Paese: 

Strategia 

tecnologica 

l’innovazione 

digitalizzazione 

90 Economic Study, p. 121. 
91 Economic Study, pp. 97, 114 and 121. 
92 See Comparative Law Study, Executive Summary. 
93 
https://assets.innovazione.gov.it/1610546390-midbook2025.pdf;   
94 Economic Study, p. 96. 
95 National Artificial Intelligence Strategy of the Czech Republic, 2019: https://www.mpo.cz/assets/en/guidepost/for-
the-media/press-releases/2019/5/NAIS_eng_web.pdf;  AI  Watch,  ‘National  strategies  on  Artificial  Intelligence  –  A 
report:  https://op.europa.eu/en/publication-detail/-
JRC-OECD 
European  perspective’,  2021  edition  –  a 
/publication/619fd0b5-d3ca-11eb-895a-01aa75ed71a1, p.  41 
96  See  Polityka  Rozwoju  Sztucznej.  Inteligencji  w  Polsce  na  lata  2019  –  2027  (Policy  for  the  Development  of 
Artificial  Intelligence  in  Poland  for  2019-2027)  (www.gov.pl/attachment/0aa51cd5-b934-4bcb-8660-bfecb20ea2a9), 
102-3. The policy document emphasised that the provisions of private law on liability for damages are not adapted to 
the challenges posed by AI, and it was pointed out that the resolving of the emerging problems could be attempted on 
a micro scale, trying to find temporary solutions beforehand. In the long term entirely new rules of civil liability for 
algorithms should be developed. The document emphasised that it would be optimal to establish these rules by way of 
international consensus. 
97 AI Portugal 2030: https://www.incode2030.gov.pt/sites/default/files/julho_incode_brochura.pdf; AI Watch, op. cit., 
p. 113. 
98 See 2.4. above. 

19 

 
 
                                                           
services. The choice of that legal basis is also supported by the EP which has called twice upon the 
Commission to use this legal basis for a legislative proposal.99 

3.2. Subsidiarity: Necessity and added value of EU action 

(a) Necessity of action at EU level 

The identified obstacles have significant impact  on cross-border trade of AI-enabled products and 
services. The negative effects of legal uncertainty – additional legal information/representation and 
risk management costs as well as foregone revenue – were found to be worse for companies trading 
cross-border.100  Likewise,  fragmentation  between  MS  regarding  the  conditions  under  which 
business  would  face  compensation  claims  for  damage  caused  by  AI  would  increase  transaction 
costs especially regarding cross-border trade, entailing significant internal market barriers.101  

The significance of these obstacles is reinforced by the fact that legal uncertainty and fragmentation 
disproportionately  affect  start-ups  and  other  SMEs,  which  account  for  the  large  majority  of 
companies  and the major share of investments  in  the relevant  markets.102 Smaller businesses lack 
the  necessary  financial  resources  as  well  as  the  specialised  legal  and  technical  expertise  to  cope 
with  the  identified  challenges.  Thus,  they  are  more  likely  to  be  deterred  from  rolling  out  their 
products  or  services  across  borders103,  incurring  lost  revenue.  This  situation  is  making  the  EU 
market overall less strong and innovative and European companies less competitive. 

MS could to some extent address the problem of legal uncertainty and improve the effectiveness of 
liability  claims  by  adapting  their  respective  rules.  However,  they  would  necessarily  do  so  by 
building on the approaches of their own, already very different liability regimes. This would lead to 
further fragmentation, with the negative effects outlined under 2.4. and 3.1. The relevant problems 
can therefore be addressed effectively only at EU level. 

(b) Added value of EU action 

The  conditions  for  the  roll-out  and  development  of  AI-technologies  in  the  internal  market  can  be 
significantly  improved  by  preventing  fragmentation  and  increasing  legal  certainty  through 
harmonised measures at EU level, compared to the baseline scenario involving possible adaptations 
of liability rules at national level.104 The economic study concluded – as a conservative estimate – 
that targeted harmonisation measures on civil liability for AI would have a positive impact of 5 to 
7 % on the production value of relevant cross-border trade as compared to the baseline scenario.105 
This  added  value  would  be  generated  notably  through  reduced  fragmentation  and  increased  legal 
certainty  regarding  stakeholders’  liability  exposure.  This  would  lower  stakeholders’  legal 
information/representation,  internal  risk  management  and  compliance  costs,  facilitate  financial 
planning  as  well  as  risk  estimates  for  insurance  purposes,  and  enable  companies  –  in  particular 
SMEs – to explore new markets across borders.106 Moreover, it is only through EU action that the 
desired  effect  of  promoting  consumer  trust  in  AI-enabled  products  and  services  by  preventing 
liability  gaps  linked  to  the  specific  characteristics  of  AI  can  be  achieved  consistently  across  the 
internal market. Harmonised adaptations of existing liability rules are needed to prevent AI-induced 

99  Resolution  of  16  February  2017  on  Civil  Law  Rules  on  Robotics  (2015/2103(INL));  legislative  own-initiative 
Resolution on a civil liability regime for AI (op. cit.) 
100 Economic Study, p. 114 
101 Ibid. 
102 Ibid., p. 117. 
103 Ibid., p. 122. 
104 See chapter 7 of the Economic Study, and section 6 for details. 
105 See Economic Study, pp. 196 et seq. 
106 For an overview of economic effects of harmonisation measures, see Economic Study, pp. 202 et seq. 

20 

 
 
                                                           
liability  gaps  across  all  MS,  ensuring  a  consistent  (minimum)  level  of  protection  for  all  victims 
(citizens and companies) and consistent incentives to prevent harm and ensure accountability. 

Given  the  long-standing  and  entrenched  divergences  between  national  tort  law  traditions,  any 
attempt  of  a  general  broad  stroke  harmonisation  of  tort  law  would  meet  with  strong  subsidiarity 
concerns  from  MS.  The  same  is  not  the  case  for  a  targeted  initiative  addressing  AI  specific 
challenges  at  EU level.  The pivotal  role of AI as a key enabling technology  distinguishes it from 
other products and services. This is why the Commission and MS have jointly committed to align 
their AI policy to remove fragmentation and address challenges globally107, recognising the need for 
coordinated measures to create the right conditions for the rollout of AI in the internal market. By 
putting forward targeted and harmonised adaptations of certain aspects of existing national liability 
rules, this initiative presents a clear added value, without putting into question MS’ traditional civil 
liability frameworks.  

(c) Stakeholder views 

A  public  consultation  conducted  in  2017  by  the  EP  showed  that  90 %  of  individual  stakeholders 
consider it necessary to regulate developments in both robotics and AI, and 96 % of those favouring 
a regulatory approach preferred action at EU or international level rather than action at MS level.108 
A  statistically  representative  Eurobarometer  of  2019  confirmed  that  half  of  EU  citizens  (50 %) 
agreed  that  public  policy  intervention  is  needed,  whereas  only  7 %  considered  that  no  specific 
action  is  needed,  and  16 %  considered  that  industry  providers  of  AI  can  deal  with  these  issues 
themselves.109  

These  results  were  clearly  confirmed  by  the  outcome  of  the  public  consultation  on  the  AI  White 
Paper in 2020. 63 % of respondents were in favour of adapting national liability rules for all or for 
specific  AI  applications  to  better  ensure  proper  compensation  and  a  fair  allocation  of  liability. 
Citizens were particularly supportive, with 72% of respondents calling for some adaptations. While 
the  overall  support  among  companies  was  45%,  it  was  higher  among  SMEs  with  60%  of 
respondents.  This  confirms  that,  as  explained  before,  the  effect  of  the  identified  legal  uncertainty 
and fragmentation is disproportionately larger on SMEs. 

The need for EU action was again confirmed by the results of the public consultation done in the 
context of this impact assessment. When asked to rank PO by preference, the baseline option ‘No 
EU  action  on  liability  for  AI’  had  clearly  the  lowest  average  preference  score  of  all  responses. 
Moreover,  ca.   60 %  of  respondents  think  that  EU  action  to  ease  the  victim’s  burden  of  proof  is 
necessary and justified, while only 25 % disagree with this.  

The  public  consultation  showed  particularly  strong  support  for  EU  action  amongst  EU  citizens, 
consumer organisations and NGOs; almost all of them chose ‘No EU action’ as their least preferred 
PO. Although business stakeholders tend to be more sceptical about EU action on liability for AI, 
there  was  support  also  from  the  business  side:  The  share  of  responding  companies/business 
organisations in favour of EU action to ease the victim’s burden of proof was the same as of those 
opposing it (ca.40 % respectively). SMEs are slightly more favourable towards EU action than other 
types  of  business  stakeholders:  4  of  the  responding  associations  representing  SME  interests 
supported  EU  action  to  ease  victims’  burden  of  proof,  while  3  did  not;  all  responding  individual 
SMEs were in favour of EU action to address difficulties of proof linked with AI. 

107 Coordinated Plan on Artificial Intelligence 2021 Review, p. 2. 
108  See  summary  of  the  public  consultation,  p. 5  (https://www.europarl.europa.eu/cmsdata/130181/public-
consultation-robotics-summary-report.pdf). 
109 See footnote 50. 

21 

 
 
                                                           
The comparably less pronounced support from businesses, in relation with all the other categories 
of stakeholders, is likely to have two main reasons.  

Firstly,  experience  seems  to  indicate  that  business  stakeholders,  when  contributing  to  public 
consultations  of the Commission, which is  considering a legislative intervention, usually  tend to 
do  so  from  the  perspective  of  a  future  subject  of  obligations  imposed  by  EU  law,  focusing  on 
potential burdens, and not from the perspective of a potential beneficiary. Accordingly, in the case 
of the public consultation on AI liability, businesses mainly  may have seen  themselves as liable 
person/entity,  and  not  as  a  potential  victim  of  damage.  The  experiences  in  the  context  of  the 
economic  study,  where  business  stakeholders  provided  input  on  the  policy  options  from  the 
perspective of potentially liable parties and not of potential victims of harm, seem to support such 
a  viewpoint.  In  line  with  the  above-mentioned  differences  in  results  when  looking  at  views  of 
businesses overall and SMEs specifically, the latter could however likely to be more able to view 
themselves  in  the  victim’s  position.  This  may  be  one  major  reason  why  respondents  of  this 
category had somewhat different views than large companies or business associations.  

Secondly, businesses may tend to focus mostly on how the legislation would apply to them directly 
and  not  to  consider  the  broader  effects  of  policy  measures,  from  which  they  would  benefit 
indirectly.  Businesses  might  tend  to  disregard  the  macroeconomic  effects  of  building  trust  in  a 
technology, while they are more aware of the effects of the lack of it. Therefore, most businesses 
replying  to  the  consultation  seemed  to  compare  a  situation  of  no  intervention  to  a  situation  of 
regulation at EU level: 

When  considering  no  legislative  intervention,  on  the  one  hand  significant  shares  of  business 
stakeholders acknowledged a link between a lack of adaptation of national liability rules applicable 
to  AI  and  legal  uncertainty  and  legal  fragmentation.  They  also  expect  to  incur  costs  due  to  these 
problems, especially when engaging in cross-border trade. On the other hand, many businesses may 
likely  tend  to  associate  the  lack  of  adaptation  of  national  liability  rules  with  an  indeed  high 
probability  that  even  if  companies  are  responsible  for  harm  caused  by  AI-enabled  products  and 
services they would not have to compensate the harm at all because potential victims would not be 
able to prove their otherwise justified compensation claim.  

By  contrast,  when  considering  a  situation  of  regulation  at  EU  level,  a  large  share  of  business 
stakeholders  may  focus  more  on  the  fact  that  harmonised  measures  to  prevent  AI-specific 
compensation  gaps  would  increase  the  chances  of  success  of  such  a  liability  claim  against  them. 
These considerations might be given more weight than the fact that the AI liability initiative aims to 
help also businesses that suffered harm to claim compensation, or the potential benefits in terms of 
increased legal certainty and reduced fragmentation.   

Authorities from only 5 MS participated in the public consultation, expressing split views on the AI 
liability  initiative.  A  dedicated  workshop  was  subsequently  organised  to  get  a  broader  picture  of 
Member  States’  opinions.  However  this  aim  was  not  achieved,  Member  States  were  in  their  very 
large majority not in a position to confirm either clear support or opposition. Member States seemed 
to adopt more a ‘wait-and-see’ stance. A reason may be that they may expect the Commission to put 
forward  a  legislative  proposal  and  may  not  have  wished  to  pre-empt  their  position  during  the 
legislative negotiations.   

22 

 
 
 
 
 
 
4.  OBJECTIVES: WHAT IS TO BE ACHIEVED? 

Specific 
Objectives 

Increase legal certainty about 
the liability risk exposure of 
business activities involving AI 

Prevent the emergence of 
fragmented AI-specific 
rules across the internal 
market 

Prevent a lack of compensation (ensure 
same  
level of protection in cases involving AI) 

General 
objectives 

Functioning of the internal market  
(reduce obstacles and prevent emergence of new obstacles) 

Ecosystem of trust  Promote the  
uptake of AI-enabled products and services 

Promote the roll-out of trustworthy AI in Europe (economic, social, environmental benefits of AI) 

4.1. General objectives 

In line with  the overall  Commission  strategy, the  primary  general objective of this  initiative is  to 
contribute to the roll-out of AI in order to reap its benefits as a set of enabling technologies for the 
growth of the digital economy and its societal advantages. The contribution of this initiative to this 
objective is twofold. 

Firstly,  it  would  improve  the  conditions  for  the  functioning  of  the  internal  market:  reducing 
existing obstacles and preventing the emergence of new obstacles to the cross-border trade in AI-
enabled products  and services.  This  would have  beneficial effects for the European economy and 
the competitiveness of the European AI-sector. 

Secondly,  in  line  with  the  Commission  human-centric  approach  and  the  White  Paper  on  AI,  this 
initiative  contributes  to  the  ‘ecosystem  of  trust’  in  AI  by  ensuring  that  persons  having  suffered 
harm  caused  with  the  involvement  of  AI  systems  enjoy  the  same  level  of  protection  as  persons 
having  suffered  harm  caused  by  other  technologies  and  that  efficient  redress  mechanisms  fairly 
distribute loss.110 Increased trust will lead to increased uptake of AI. 

4.2. Specific objectives 

(a) SO1: Increase legal certainty about the liability risk exposure of business activities involving 
AI  

- Description: This initiative aims at reducing legal uncertainty regarding companies’ liability risk 
exposure.111 Legal certainty is a key concern for investors.112  

By  clarifying  the  conditions  for  assessing  and  quantifying  liability  risks,  the  initiative  seeks  to 
create  investment  stability.  By  reducing  legal  information/representation,  risk  management  and 
litigation  costs,  companies  should  have  stable  and  favourable  conditions  to  produce,  market  and 
operate AI-enabled products and services. This initiative thus promotes the roll-out of AI-enabled 
products and services.  

110 AI White Paper, p. 15. 
111 Given the general nature of these liability rules (they can apply to any ‘wrongdoer’), a broad range of companies 
can fall under the scope, e.g. companies providing labelled training data, companies involved in the development of 
AI components, companies using AI to provide services, etc. 
112 Cf. ‘Notes from the AI frontier – Tackling Europe’s gap in digital and AI‘, McKinsey Global Institute Discussion 
Paper,  February  2019,  p. 47; Economic  Study,  p. 68. The  representative  IPSOS  survey  also  came  to  the  conclusion 
that reducing uncertainty can be highly beneficial for enterprises, as enterprises find liability for potential damages to 
be a major external challenge to AI adoption, see IPSOS, op. cit., p. 6. 

23 

 
 
 
 
 
 
 
                                                           
This  SO  applies  to  all  AI-related  business  but  is  particularly  relevant  for  cross-border  operations, 
where the negative effects of legal uncertainty are multiplied by the number of MS into which AI-
enabled products and services are exported to or are operating in. 

- Stakeholders targeted: Legal certainty is meant to benefit all companies active in AI. Increasing 
legal certainty would be beneficial to a higher degree for start-ups and other SMEs, which perceive 
uncertainty  to  a  greater  extent  and  are  disproportionately  affected  by  the  associated  costs  and 
burden.113  

By  reducing  legal  uncertainty,  the  initiative  aims  also  to  promote  a  wide  range  of  offers  in  the 
relevant insurance market and enable insurance companies to reduce risk premiums. Those taking 
up AI will be able to insure their liability risk, with the effect of limiting liability exposure to the 
insurance  premiums.  Such  insurance  decreases  possible  liability  costs  very  significantly.  It  also 
makes them predictable and therefore manageable. This benefits in particular start-ups with lower 
capital cover, because a liability  risk is more likely to be an existential threat for such companies 
than  for  large  companies.114  Insurance  coverage  is  also  advantageous  for  victims,  because  they 
benefit from faster and easier compensation, even in the event that the liable party is insolvent. 

(b) SO2: Prevent the emergence of fragmented AI-specific rules across the internal market 

- Description: The initiative aims to prevent the emergence of different national rules adapting in 
different ways national liability regimes to the specificities of AI. This would exacerbate the already 
existing legal heterogeneity and create more fragmentation. It would also slow down the roll-out of 
AI  as  a  set  of  enabling  technologies.  Legal  fragmentation  adds  to  the  burden  linked  to  legal 
uncertainty  when  operating  across  national  borders.  The  second  SO  is  thus  intertwined  with  the 
first.  Preventing  legal  fragmentation  in  form  of  diverging  adaptations  of  liability  rules  at  national 
level  alleviates  those  types  of  burden  that  apply  specifically  when  operating  across  borders.  For 
instance, the initiative would reduce the need for companies to adapt their business models and the 
technological  parameters  of  AI-enabled  products  and  services  in  light  of  diverging  proof-related 
liability rules.  

- Stakeholders targeted: Attaining this SO would benefit primarily companies, whose activities are 
linked  to  the  development  or  use  of  AI,  that  are  already  operating,  or  are  considering  to  expand, 
across  borders.  As  with  the  first  SO,  start-ups  and  other  SMEs  are  affected  to  a  markedly  higher 
degree  than  large  companies,  given  that  the  former  are  much  more  likely  to  be  deterred  by  legal 
fragmentation from exploring new geographical markets. This SO covers also insurance companies. 
More  consistent  extra-contractual  liability  rules  would  facilitate  it  for  them  to  offer  competitive 
coverage  of  AI-related  activities  in  several  MS,  notably  by  enabling  a  consistent  risk-assessment 
and insurance product design.  

(c) SO3: Prevent a lack of compensation in cases involving AI to promote societal trust and the 
uptake of AI-enabled products and services 

- Description: By preventing AI-specific liability gaps, the initiative promotes societal acceptance 
and consumer trust, both in AI as a new technology and in the justice system. This SO forms part of 
the EU’s human-centric approach to create an ‘ecosystem of trust’. 

-  Stakeholders  targeted:  This  initiative  aims  at  increasing  societal  trust  in  AI-technologies115  and 
access to an effective justice system. It ensures that civil liability is well functioning, adapted to the 

113 Economic Study, pp. 97 and 117. 
114 Ibid., pp. 142. 
115 The link between effective liability rules for AI and consumer trust was confirmed specifically by the behavioural 
study, see Behavioural Economics Study, op. cit.  

24 

 
 
                                                           
specificities of AI, and justified claims for compensation of harm are successful. It will reach this 
aim by facilitating claims where the specific characteristics of AI (see 2.2.) would otherwise make it 
difficult or prohibitively costly for victims to meet the burden of proof.  

Increasing societal trust would benefit all companies in the AI-value chain, because strengthening 
citizens’  trust  will  contribute  to  a  faster  uptake  of  AI,  and  create  a  competitive  advantage  for 
European  AI  companies.  Due  to  the  incentive  effect  of  liability  rules,  preventing  liability  gaps 
would  also  indirectly  benefit  all  citizens  through  an  increased  level  of  protection  of  health  and 
safety (Article 114(3) TFEU) and the obviation of sources of health risks (Article 168(1) TFEU). 

(d) Consistency with other Commission proposals concerning AI and the PLD revision116 

Not only is this proposal an important element of the overall Commission strategy to promote the 
roll-out  of  AI  and  its  human-centric  approach.  It  will  also  incentivise  compliance  with  safety 
requirements and requirements designed to safeguard fundamental rights, applicable to AI-enabled 
products and services.117 Beside its other main objective to achieve fair compensation, liability law 
acts  as  deterrent  to  unwanted  behaviours,  in  this  case,  a  non-compliance  with  safety  and 
fundamental  rights  requirements  (see  1.3.).  Potentially  liable  persons  will  be  more  diligent  in 
observing  rules  meant  to  prevent  harm  by  AI  systems.  This  comes  from  the  knowledge  that,  in 
addition to possible administrative sanctions (e.g. fines, withdrawal of products, and suspension of 
licences), they face the risk of having to pay compensation for a damage caused. 

The  general  and  SO  of  this  initiative  are  also  consistent  with  those  of  the  general  PLD  revision 
(PLD impact assessment section 4). The two initiatives are designed to complement one another. In 
their  combination,  they  will  create  a  clear  and  consistent  legal  framework  ensuring  effective 
protection  of  victims  of  AI.  Given  the  PLD’s  horizontal,  technology-neutral  approach,  the  PLD 
revision is not aimed at AI in particular but covers all kinds of products. This initiative addresses 
AI-specific challenges.118 The PLD revision pursues also additional objectives, such as making the 
PLD fit for the circular economy. 

(e) Contribution to achieving the Sustainable Development Goals 

By promoting the development and rollout of AI, this initiative indirectly supports several targets 
across all the Sustainable Development Goals (SDGs). TA more direct contribution of this initiative 
to  the  SDGs  is  towards  Goal  16,  especially  target  16.3.  This  initiative  will  ensure  that  victims  of 
harm  caused  by  AI-enabled  products/  services  have  effective  access  to  the  evidence  necessary  to 
build  a  case  in  court,  so  as  to  enjoy  the  same  chances  of  a  successful  claim  as  victims  of  harm 
caused by other technologies. By ensuring to the former the same level of protection as to the latter, 
this initiative promotes the goal of ‘equal access to justice for all’.  

5.  WHAT ARE THE AVAILABLE POLICY OPTIONS? 

5.1. What is the baseline from which options are assessed? 

(a)   Description 

116 For details about how the present initiative interacts with other relevant legislation and policies, see Annex 6. 
117See IA on the AI Act, SWD(2021 84 final (p. 88): “Effective liability rules will […] provide an additional incentive 
to  comply  with  the  due  diligence  obligations  laid  down  in  the  AI  horizontal  initiative,  thus  reinforcing  the 
effectiveness and intended benefits of the proposed initiative.” 
118 This does not imply that the AI liability initiative would take precedence (e.g. based on the lex specialis rule) over 
the  PLD.  The  respective  scopes  would  be  carefully  delineated  to  avoid  any  conflicts  or inconsistencies.  As  the  AI-
specific alleviations of the burden of proof are designed to apply in the context of national fault-based liability rules, 
claims falling under the scope of the PLD would not be affected by these alleviations. 

25 

 
 
                                                           
The baseline builds on the existing legal framework outlined under 1.2., which consists primarily of 
MS’ fault-based and strict liability rules as well as the PLD. It is also assumed that the proposed AI 
Act119, the Machinery Products Regulation (‘MPR’) and the General Product Safety Regulation will 
be adopted. The expected adaptation of national liability rules by a number of MS, likely leading to 
further  fragmentation,  is  taken  into  consideration  when  assessing  the  impacts  of  the  identified 
problems under the baseline. A time horizon of 10 years is envisaged for the baseline and for the 
assessment of the PO against that baseline.120  

Interplay  with  PLD  review:  This  same  baseline  is  used  in  the  impact  assessment  on  the  not  AI-
specific  measures  of  the  PLD  review.  In  particular,  the  Commission  is  considering  to  clarify 
liability under the PLD in respect of: 

software/digital elements (including AI) necessary for tangible products to operate,  

- 
-  who qualifies as producer for defective updates and upgrades,  
- 
-  how to ease the burden of proof in the case of complex products.121  

liability for the failure to provide a software update to keep the product safe and  

These measures could allow to a greater extent than before compensation under the PLD of harm to 
life, health and consumer property caused by defective AI systems related to products.  

The  measures  for  AI-induced  damage  looked  at  in  the  present  impact  assessment  would 
complement the PLD for those liability claims not covered by the PLD, in particular for:  

- 
- 

- 
- 
- 

claims based on the fault of a person that led the AI to cause harm (not on product defect); 
claims  against  other  entities  than  producers  (e.g.  users,  operators,  service  providers,  cyber 
attackers); 
claims for damage caused by AI-enabled services;  
claims to compensate damage suffered by businesses,  
claims to compensate types of damage not covered under the PLD (e.g. immaterial damage not 
resulting  from  personal  injury  or  damage  to  consumer  property,  damage  caused  by 
discrimination, etc.).  

Beyond this, for persons having suffered harm caused by AI systems the objective of the AI White 
Paper is that these victims have the same level of protection as persons having suffered harm caused 
by other technologies.  Both  impact  assessments together look  at all three pillars of liability  (fault 
based liability, PLD, strict  liability, see  1.2.) in  a holistic way.  As a result,  for all three pillars of 
liability  the  victims  should  not  have  less  routes  to  compensation  than  for  harm  caused  by  other 
technologies.  

For those two reasons, the measures examined in the impact assessment on the PLD review and the 
PO  defined  in  this  impact  assessment  present  a  synergetic  whole  for  damages  caused  by  AI-
systems.  

 (b) Impacts of problems under the baseline scenario 

-  Market  barriers  linked  to  legal  uncertainty  and  fragmentation  (problems  1  and  2):122  It  is 

119 See Annex 7 for details of the links with the AI Act. 
120  This  time  horizon  takes  into  account  the  usual  length  of  the  procedure  for  proposing,  negotiating,  adopting  and 
implementing the policy options as well as the future-oriented nature of this initiative, which is designed to provide 
the right conditions for the roll-out of innovative AI-enabled products and services. 
121  Namely  through  access  to  technical  information,  by  expressly  allowing  the  presumption  of  defectiveness  under 
certain  circumstances,  and  by  clarifying  the  ‘development  risk  defence’  to  ensure  that  producers  remain  liable  for 
undiscoverable defects in products designed with limited predictability. 

26 

 
 
                                                           
likely that at  least  some  MS will  adapt  their respective liability rules under the baseline scenario. 
Depending  on  their  respective  approaches,  this  may  increase  legal  certainty  to  a  varying  extent. 
However, legal uncertainty would not be addressed in a systematic and coherent manner across the 
internal market.  In those MS that will not adopt general legislative measures, only ad hoc judicial 
solutions  will  emerge,  based  on  a  case-by-case  interpretation  of  existing  liability  rules.  Affected 
businesses,  in  particular  SMEs,  are  therefore  likely  to  continue  facing  most  of  the  costs  of  legal 
uncertainty described under 2.6. In addition, the national legal or case-law solutions reacting to AI 
specific  challenges  would  increase  the  level  of  legal  fragmentation,  which  would  lead  to  higher 
costs for companies operating cross-border. 

With the expected roll-out  of increasingly  autonomous, opaque  and  complex AI-systems  over the 
coming  years,  AI  will  be  implemented  in  ever  more  sectors  of  the  economy  and  society.  It  is 
therefore highly likely that damages and ensuing liability claims involving AI-systems will become 
more frequent, and the economic relevance of the liability-related problems will increase under the 
baseline scenario. It was estimated that the total value of the EU27 market shares affected by those 
problems will increase almost seven- to ten-fold between 2021 and 2025 (from between EUR 1.739 
billion to EUR 4.973 billion to between EUR 10.204 billion and EUR 21.342 billion.123 The market 
shares  affected  by  legal  uncertainty  and  fragmentation  were  determined  by  a  sector-by-sector 
analysis  in  the  economic  study,  taking  into  account  the  current  and  forecast  take-up  rate  of  AI-
enabled technologies, the percentage of companies perceiving liability  as a barrier to  AI adoption 
and the ability of the AI applications deployed in the respective sector (now or in the foreseeable 
future) to cause harm and thus give rise to liability claims.124 For all of these factors, the study made 
reasoned assumptions based on data from the aforementioned representative company survey on AI, 
relevant  individual  stakeholder  feedback  as  well  as  a  detailed  market  analysis.  The  size  of  the 
market shares affected by legal uncertainty and fragmentation provides an indication of the order of 
magnitude  of  the  economic  relevance  of  the  problems  to  be  addressed  by  this  initiative.  This 
indicator  is  not  intended  to  provide  a  precise  quantification  of  the  economic  impact  of  the  legal 
uncertainty and fragmentation. Such quantification proved impossible due to a lack of data, as the 
AI-enabled products and services affected by the initiative are in the early stages of rollout or not 
yet  on  the  market.  It  is  also  acknowledged  that  the  factors  and  assumptions  supporting  these 
estimates  are  to  a  certain  extent  uncertain,  as  they  reflect  uncertain  projections  on  future 
technological  and  market  development.  In  particular,  the  assumption  that  the  size  of  the  market 
shares affected by legal uncertainty and fragmentation will increase proportionally to the increase in 
the overall size of the AI market depends on a number of yet unknown factors, such as the extent to 
which revised safety rules will decrease the instances of harm caused by AI and possible advances 
in  the  field  of  explainable  AI.  Taking  into  account  that  the  increasing  degree  of  complexity  and 
behavioural autonomy of AI systems is likely to exacerbate the problems to be addressed by the AI 
liability  initiative,  and  that  MS  will  over  time  become  increasingly  likely  to  adapt  their  liability 
rules  unilaterally  in  the  absence  of  EU  action,  the  assumption  of  a  proportional  increase  of  the 
market shares affected by legal uncertainty and fragmentation appears conservative. In any case, a 
maximum  of  robustness  is  ensured  by  a  allowing  for  a  wide  bracket  between  the  high  and  low 
estimates, which can accommodate differing market definitions and future developments. 

- Lack of compensation for victims leading to a lack of societal trust and hampering take-up 
by consumers (problem 3): In those MS that adapt their liability frameworks to the challenges of 

122 Due to the interconnected nature of the problems of legal uncertainty and legal fragmentation, those problems are 
considered together for the purposes of the baseline scenario. 
123 Cf. Economic Study, p. 112. For explanations about how the relevant shares of the AI market were determined, see 
point 2.6.(a) above. 
124  For  example,  in  the  area  of  mobile  robotics,  a  risk  of  physical  harm  was  taken  into  account;  in  the  area  of 
recruitment services, a risk of damage caused by discrimination was assumed; in the health sector, a risk of physical 
harm exists, etc. 

27 

 
 
                                                                                                                                                                                     
AI, claims for compensation could become similarly effective as in cases where no AI-technologies 
are involved. However, in those MS which will refrain from adapting liability rules reacting to AI 
specific  challenges  during  the  coming  years  it  will  often  be  prohibitively  difficult  or  even 
impossible for victims to claim compensation.  

Over the course of the baseline period, the increasing prevalence of AI-systems in more and more 
sectors  of  the  economy  and  society  will  mean  more  accidents  and  more  liability  claims  that  are 
likely  to  be  attract  significant  media  attention.  This  will  drive  consumers’  awareness  of  liability-
related  issues.  As  confirmed  by  dedicated  behavioural  research,  persisting  –  and  increasingly 
mediatised  –  safety  concerns will in  turn also  contribute to  a lack of societal trust  in  AI.125 Thus, 
liability-specific issues, if not addressed consistently at EU level, are expected to have a significant 
negative  effect  on  societal  trust  and  the  uptake  of  AI.126  That  is  because  lack  of  trust  means  less 
willingness  to  engage  with  the  technology.  Delayed  or  reduced  uptake  of  AI-technologies  in  the 
internal  market  will  affect  all  companies  in  the  AI-value  chain.  It  will  also  lead  to  reduced  or 
delayed benefits of AI for the society.  

The practical consequence of this situation is both unfair and economically inefficient. On the one 
hand,  certain  risk-takers  that  benefit  from  integrating  AI  into  their  activities  will  be  able  to 
externalise to the victims the costs linked with the use of AI.  On the other hand, victims – be they 
natural persons or companies – will be burdened with costs that they have no means to prevent. The 
liability  framework  would  thus  fail  to  fulfil  its  functions  of  preventing  unwanted  behaviours  and 
ensuring an efficient cost allocation of risks.  

Persistent  liability  gaps  are  also  likely  to  frustrate,  to  some  extent,  the  White  Paper  objective  of 
ensuring  the  safety  of  AI  for  the  benefit  of  all  EU  citizens,  as  liability  rules  will  not  incentivise 
potentially liable persons to prevent accidents. 

(c)  The  relevance  of  liability-specific  problems  given  the  AI  Act,  MPR  and  General 
Product Safety Regulation 

The legislative proposals  (in particular the  AI Act and MPR) were also  adopted as Commission’s 
follow-up  to  the  AI  White  Paper  to  address  specific  problems  of  AI  and  they  pursue  the  same 
overall objective as the present initiative.127  

These  proposals  introduce  requirements  to  be  met  before  AI  systems  placed  on  the  market,  to 
reduce  safety  risks  and  breaches  of  fundamental  rights.  They  also  provide  for  monitoring  of  how 
these systems operate in practice and reporting of incidents.. These provisions reflect that there is 
no  absolute  safety,  only  a  level  of  accepted  risk.  The  impact  assessments  accompanying  the 
proposed  AI  Act,  MPR  and  General  Product  Safety  Regulation  indeed  conclude  that  these 
initiatives  will  reduce  accident  rates,  while  quantified  estimates  of  the  expected  safety  gains 
regarding  AI-enabled  products  or  services  in  particular  are  not  available.  It  is  therefore  assumed, 
albeit  only  in  qualitative  terms,  that  safety  levels  will  increase  during  the  baseline  period,  an 
assumption that has been incorporated in this impact assessment. 

Hence, it is certain that these proposals, while reducing the risk for accidents, will not exclude harm 
caused by AI systems. The AI liability initiative will deal only with those accidents happening once 
the AI Act and other relevant safety legislation are adopted and applied. 

125 Behavioural Economics Study. pp. 33, 85. 
126 Ibid, pp. 24, 28, 32, 33, 35. 
127 Namely to promote the roll-out of trustworthy and safe AI in Europe, cf. the impact assessment accompanying the 
AI Act proposal, pp. 13, 24, 25, 29 and 33. 

28 

 
 
  
                                                           
Once adopted, the requirements that market players (e.g. professional users) have under the AI Act 
could  inform  the  standard  of  care  that  courts  will  have  to  assess  in  liability  claims  against  them. 
Similarly, the information to be documented/logged pursuant to the AI Act could be used in liability 
claims,  to  the  extent  victims  could  have  access  to  such  evidence  under  various  national  laws. 
However, in line with the staged approach of the Commission’s AI policy, and the complementary 
nature of safety and liability rules, the provisions of the AI Act  and the MPR are not designed to 
address  the  problem  drivers  and  problems  tackled  by  this  initiative.  Their  provisions  are  not 
designed  to  clarify  how  liability  rules  should  apply  to  AI  and  to  facilitate  the  individual 
compensation  of  victims  of  harm.  This  means  that,  under  the  baseline  scenario,  the  liability 
situation will remain subject to the same challenges as explained above. The Commission aims to 
address  these  challenges  and  achieve  synergies  between  these  proposals  and  the  envisaged  civil 
liability related measures (see s.1.3. and 2.8 and Annex 7).  

5.2. Description of the policy options 

Legal  instrument  for  implementing  the  PO:  Each  of  the  PO  could  be  implemented  either  by  a 
binding legislative instrument or by a non-binding recommendation addressed to MS to implement 
the preferred measures in their national law. The approach  for selecting the suitable instrument is 
6.128
set 

Chapter 

out 

in 

(a)  PO1: Easing the burden of proof for AI-related claims 

Description:  The  problems  are  primarily  linked  to  the  difficulty  of  the  victim  to  prove  the 
necessary facts due to the specific characteristics of certain AI systems. This PO would help the 

128 For explanations on how the policy options take into account the EP’s resolution on a civil liability regime for AI 
(2020/2014(INL), see point 4 of Annex 4.  

29 

 
 
 
 
                                                           
victim to handle its burden of proof in a way that justified liability claims can be successful.  

PO1  would  involve  three  complementary  measures.  Two  of  these  –  (i)  and  (ii)  –  are  closely 
aligned  with  the  approach  and  duties  set  by  the  AI  Act.  Definitions  of  the  AI  Act  (AI-system, 
provider,  user)  would  be  taken  over  to  ensure  consistency.  None  of  these  two  measures  would 
involve the introduction of any new risk categories; the risk-categorisation of the AI Act would be 
mirrored. They would be fully compatible with the confidentiality safeguards included in the AI 
Act.  Measure  (iii)  would  not  involve  any  differentiation  according  to  the  risk  profile  of  AI 
systems.  None  of  the  measures  of  PO1  would  provide  for  any  substantive  obligations  regarding 
the development or use of AI systems. 

PO1 would not define either what constitutes in national law ‘fault’ or ‘causality’. MS would be 
able to fit provisions into their national liability regimes without disrupting their legal traditions. 

(i)  The disclosure, for liability claims, of information that the user or provider of a high-

risk AI has to record or document pursuant to the AI Act.  

Obtaining information recorded or documented pursuant to the AI Act129 could help the victim to 
make  a  successful  liability  claim.130  For  instance,  it  could  help  to  provide  evidence  for 
demonstrating fault as one of the conditions of liability to be proven by the victim. This could be 
done for example by proving that the liable person did not comply with their obligations under the 
AI Act.  

Example: A highly autonomous cleaning robot causes harm (cf.  example under  2.2. and Annex 12). The 
victim claims compensation from the company operating the cleaning robot. For the purpose of the AI Act, 
this company is the ‘user’ of the robot’s AI systems. Access to relevant parts of the technical documentation 
(Annex IV of the AI Act), e.g. the instructions of use, would show the victim what the user was required to 
do to prevent such accidents. For example, the user may be required to ensure human oversight. The user 
might also be required to ensure that the AI systems are only used in certain operational environments (e.g. 
geographical  and  meteorological  conditions,  etc.).  This  information  can  help  the  victim  to  establish that 
the liable person has not complied with such requirements and therefore committed a fault. 

According to this measure, the victim could request from the possibly liable party the disclosure of 
such  information  concerning  the  victim’s  claim.  The  competent  national  court  could  order  that 
such disclosure would be subject to stringent safeguards to ensure proportionality and protect the 
legitimate  interests  of  all  parties  concerned,  for  instance  confidential  information,  intellectual 
property rights and trade secrets.  

If  the  possibly  liable  person  does  not  disclose  the  information,  the  victim  would  benefit  from  a 
presumption that the facts, which the withheld information might have proven actually, did occur. 
Such presumption would facilitate the burden of proof of the victim.  

The liable person would have the opportunity to rebut the presumption. This means that the liable 
person would have the opportunity to prove that the presumed facts did in reality not occur. That 
person  could  also  avoid  liability  by  refuting  any  of  the  liability  conditions  set  by  the  applicable 
liability  rules,  for  instance  demonstrate  that  the  relevant  AI  system  was  carefully  selected  and 
used, taking into account the requirements of the AI Act.   

Example:  In  the  cleaning  robot  example,  the  victim  requests  access  to  the  data  recorded  during  the 

129 Without prejudice to the possibility of disclosure of other information pursuant to national law or the PLD. 
130  Similar  approaches  have  been  successfully  implemented  at  EU  level  e.g.  in  the  ‘Damages  Directive’ 
(2014/104/EU)  facilitating  the  private  enforcement  of  EU  competition  law,  and  the  ‘Enforcement  Directive’ 
(2004/48/EC) enabling and effective civil enforcement of harmonised intellectual property rights. 

30 

 
 
                                                           
operation of the cleaning robot in order to prove that the robot’s sensors were not properly cleaned at the 
time  of  the  accident.  The  competent  court  can  order  necessary  safeguard  measures  to  ensure  that  all 
legitimate  confidentiality  interests  are  protected.  If  the  company  responsible  for  operating  the  cleaning 
robot (= the possibly liable party) nevertheless does not disclose this information, the victim could claim 
and  the  court  would  presume  for  the  purposes  of  the  victim’s  liability  claim  that  the  sensors  were  not 
properly cleaned. This may constitute a fault of the liable party. However, the liable party could rebut the 
presumption by showing that the sensors were actually clean. It could also avoid liability in other ways, 
e.g.  by  showing  that  there  is  no  causal  link  between  the  presumed  failure  to  clean  the  sensors  and  the 
damage. 

This measure would not only help the victim. Providers and users of AI systems would be given 
an additional incentive to comply with the documentation/logging requirements of the  AI Act. If 
they  had  complied,  they  could  disclose  the  information  (subject  to  necessary  confidentiality 
safeguards)  and  would  be  able  to  demonstrate  that  they  acted  in  accordance  with  legal 
requirements.  This  additional incentive  would support the  compliance with  the AI  Act.  Thereby 
this measure would achieve synergies with the AI Act. 

(ii)  If  the  victim  shows  that  the  liable  person  did  not  comply  with  AI  Act  requirements 
designed  to  prevent  damage,  the  second  measure  could  establish  a  rebuttable 
presumption that this non-compliance caused the damage.  

This measure would further facilitate the exercise of the burden of proof as regards the causality 
link  between  fault  and  damage  for  the  victim.131  The  victim  could  demonstrate  that  the  liable 
person did not comply with AI Act requirements. In that case the victim could claim and the court 
would  presume  for  the  purposes  of  the  victim’s  liability  claim  that  such  non-compliance  would 
have caused the harm. Again, such presumption would be rebuttable. This means that the possibly 
liable  person  could  avoid  liability  by  proving  that  their  non-compliance  with  the  AI  Act 
requirements did not cause the damage. 

Example: Under the AI Act the user of a high-risk AI system, like the company operating an AI-enabled 
highly autonomous cleaning robot, is obliged to use such systems in accordance with the instructions of use 
accompanying the system. These instructions of use could specify, for instance: 

-  the  type  of  input  data  the  system  should  be  exposed  to,  e.g.  meteorological  or  lighting  conditions 

perceived through the cleaning robot’s sensors;  

-  circumstances which may lead to risks to health, safety or fundamental rights, e.g. the type of terrain or 

surface the cleaning robot is capable to move on safely without causing harm. 

The victim may be able to prove that  the company operating the AI-enabled highly autonomous cleaning 
robot (= the possibly liable party) did not comply with such instruction, e.g. that the robot’s AI-enabled 
perception system was used in an environment where it was exposed to inadequate input data (e.g. poor 
lighting  conditions)  or  under  circumstances  which  may  lead  to  health  risks  (e.g.  on  uneven  terrain 
frequented by many human passers-by). Access to information in accordance with measure (i) could help 
the victim to prove such facts. The competent court would presume that it was the non–compliance of the 
liable party with its obligations that caused the damage. 

The  possibly  liable  party  could  rebut  this  presumption  by  demonstrating  that  there  is  no  causal  link 
between  the  non-compliance  and  the  damage,  e.g.  by  showing  that  someone  pushed  the  cleaning  robot, 
causing the accident. That party could also avoid liability by showing that they applied all the care that 
could reasonably be expected of them under the relevant circumstances, which means that they were not at 
fault. 

131 This measure would harmonise an approach known from some national tort laws, which was recommended by the 
Expert Group. 

31 

 
 
                                                           
In line with the AI Act, this measure would be primarily relevant for claims against providers and 
users of high-risk AI-systems. It would give them an additional incentive to comply with the AI 
Act,  because  in  case  of  non-compliance  it  would  be  presumed  that  their  behaviour  caused  the 
damage.  

However, this measure  is  not  be sufficient to  ensure that victims  always  have the same level  of 
protection when suffering harm caused by AI: 

-  Firstly, the information needed by the victim to prove non-compliance may for instance not be 
recorded.  In  such  cases,  the  victim  may  not  be  able  to  establish  the  liable  party’s  non-
compliance with the AI Act (and thus to trigger the presumption). For example, the victim may 
not  be  able  to  know  what  kind  of  input  data  the  AI  system  was  exposed  to  or  what 
circumstances of use the AI system was designed for.  

-  Secondly, the presumption can apply only if a ‘high risk’ AI-system as defined in the AI Act is 
at  stake.  It  would  therefore  not  apply  to  AI  systems,  like  for  instance  autonomous  drones, 
which are not covered by the AI Act requirements, but can cause harm. 

-  Thirdly,  the  presumption  would  apply  only  if  the  requirement  that  the  liable  party  did  not 
comply with, was intended to prevent harm of a type suffered by the victim. For example, if the 
victim  seeks  compensation  for  damage  caused  by  discrimination,  but  can  only  establish  non-
compliance  with  a  requirement  meant  to  prevent  physical  harm,  this  would  not  trigger  the 
presumption.  

(iii) Targeted alleviation of the exercise of the burden of proof, so that the victim does not 

have to explain the inner workings of the AI.  

As  explained  under  2.2.,  the  specific  characteristics  of  the  AI-system  could  make  the  victim’s 
burden of proof prohibitively difficult or even impossible to meet. There will be cases when the 
victim will not be able to identify or explain how something that the liable person did (or failed to 
do) was ‘translated’ by the AI into the output that caused the damage.  

Example:  In  the  cleaning  robot  example  (2.2.),  several  potentially  liable  persons  are  responsible  for 
different input parameters. Such alternative causes for the harm caused to the victim could lie, for example, 
in a failure by a human supervisor to react to a distress signal, flawed training or testing data, exposure to 
input data not in conformity with the instructions of use, failure to provide or install a necessary update or 
an external attack (e.g. jamming, spoofing, adversarial machine learning)132. Each of these inputs could be 
the reason of the damage caused by the highly autonomous AI system. However, the opacity of that system 
can  make  it  prohibitively  difficult  or  even  impossible  for  the  victim  to  prove  which  of  these  persons 
effectively caused the damage.  

The victim may be able to establish facts such as that the liable party used the AI system in circumstances 
or conditions that were inappropriate or failed to take instructions of use into account. If the victim can 
prove such facts, the court can relieve  the victim from the obligation to prove  that these facts led the AI 
system to arrive at the specific output that caused the damage. 

As with the other measures, the potentially liable persons could prove an alternative cause for the damage 
or show that they acted diligently and thus avoid liability. 

The  existing  legal  framework  offers  a  limited  set  of  alternative  tools  to  help  victims  overcome 
these proof-related difficulties: 

132 These examples of potential causes of the damage are  mentioned to illustrate the victim’s difficulties of proving 
what triggered the harmful output of the relevant AI system. The company using the AI system would not be liable for 
flawed training / testing data or an external attack (unless the latter was enabled by a failure by that company to take 
appropriate cybersecurity measures). 

32 

 
 
 
                                                           
-  Rebuttable presumptions: If the victim meets a reduced burden of proof, e.g. by demonstrating 
the plausibility or likelihood of certain facts, it is presumed that those facts occurred. In order to 
avoid having to pay compensation, it is then for the liable party to demonstrate that these facts 
did  in  reality  not  occur  or  that  other  facts,  for  which  the  liable  party  is  not  responsible, 
occurred.  This  tool  leaves  the  basic  distribution  of  the  burden  of  proof  intact,  but  makes  it 
easier for the victim to discharge that burden. 

-  Shifts/reversals  of  the  burden  of  proof:  This  tool  departs  from  the  general  principle  that  the 
party  invoking  a  legal  provision  has  to  prove  that  the  conditions  for  the  application  of  that 
provision are fulfilled. The victim hence no longer bears the burden of proof, but it is for the 
liable person to prove that one or all conditions of liability are not fulfilled. Such a solution was 
proposed by the European Parliament for fault-based liability. According to that resolution, the 
operator of an AI system would by default be liable for harm or damage caused by that system, 
unless they can prove that the harm or damage was caused without his or her fault. 
Irrebuttable presumptions: This tool goes even further than a reversal of the burden of proof, as 
it posits certain conditions or facts as given, without allowing for the possibility to proof that 
this presumption is wrong. 

- 

All  of  these  possible  approaches  have  been  analysed  when  designing  measure  (iii),  giving 
particular  consideration  to  the  suggestions  put  forward  by  the  European  Parliament.  For  the 
following  reasons,  only  the  tool  of  a  rebuttable  presumption  has  been  retained  in  the  PO  for 
detailed assessment: 

-  MS’ civil laws are based on the principle that the victim has to prove all the facts necessary to 
support their claim. A rebuttable presumption would leave this fundamental principle in place. 
This approach would make it easier for MS to integrate the harmonised AI-specific adaptations 
of  liability  rules  into  their  respective  legal  systems  without  friction.  It  also  preserves  a 
maximum  of  consistency  between  the  rules  applicable  in  cases  involving  AI  and  the  general 
rules applying in other cases. 

-  The  AI  liability  initiative  seeks  to  help  victims  meet  their  burden  in  the  most  targeted,  i.e. 
proportionate  manner  possible.  A  rebuttable  presumption  is  the  least  interfering  way  of 
alleviating the burden of proof. 

-  A reversal of the burden of proof regarding fault and causation, as suggested by the European 
Parliament,  or,  to  an  even  greater  extent,  an  irrebuttable  presumption  represent  a  more  far-
reaching exception from MS’ general civil liability rules, which puts into doubt their political 
feasibility.  

-  Such  approaches  would  create  a  situation  where  victims  of  damage  caused  with  the 
involvement  of  AI  would  systematically  be  in  a  more  favourable  situation  than  victims  of 
damage  caused  by  other  technologies.  They  would  thus  overshoot  the  Commission’s  policy 
objective defined in the AI White Paper, and risk dis-incentivising the uptake of AI. 

-  The public consultation showed strong opposition from business stakeholders against a shift of 
the burden of proof: 63 % of these respondents (54 out 86) disagreed with such an option, and 
only 24,4 % (21 out of 86) agreed. Taking into account the initiative’s objective to promote the 
rollout  of  AI,  this  result  militates  in  favour  of  alleviating  the  burden  of  proof  through  less 
interfering measures, that is to say in favour of a rebuttable presumption instead of a reversal of 
the burden of proof or a irrebuttable presumption.            

The  rebuttable  presumption  to  implement  measure  (iii)  is  a  procedural  tool,  to  help  in  court 
proceedings, that would intervene only in the context of existing fault-based liability claims.133 If 

133  While  this  measure  is  inspired  by  similar  tools  available  to  national  courts  in  many  MS,  the  respective  national 
rules and approaches currently diverge significantly and their applications in AI-related cases is highly uncertain (cf. 
Comparative Law Study, pp. 26-29 and 32-37). 

33 

 
 
                                                           
the victim makes it sufficiently plausible that an action or omission of the potentially liable party 
led the AI system to arrive at the damaging output, the court could presume that this was indeed 
the  case.  The  potentially  liable  party  still  has  the  possibility  to  rebut  such  presumption.  The 
measure  would  not  involve  the  creation  of  new  risk  categories,  substantive  obligations  or 
liabilities. 

This measure would not be relevant where AI systems are used to provide advice or information to 
human decision-makers (e.g. medical analysis AI informing the diagnosis and treatment decisions 
of human physicians). In such cases the AI system is not interposed in the causal chain between 
the relevant human action and the damage. It will thus not be necessary for the victim to establish 
what triggered a specific output of the AI system.  

Scope of PO1: Which AI systems would be concerned?  

Measures (i), (ii) and (iii) would apply where an AI system as defined in the AI Act  has caused 
harm.  While  the  specific  challenges  of  AI  to  liability  rules  are  driven  by  the  peculiar 
characteristics of certain AI systems, it is appropriate to define AI systems by reference to the AI 
Act’s  broad  concept  of  AI  rather  than  limiting  its  application  to  a  subset  of  AI  systems.  As 
measures (i) and (ii) are directly linked to the AI Act, their scope must be aligned to it, in any case, 
so  they  would  apply  to  high-risk  AI  systems,  as  defined  by  the  AI  Act.  In  addition,  victims  of 
harm should not have to demonstrate that an AI system has specific characteristics or belongs to a 
specific  category  in  order  to  benefit  from  the  envisaged  measures.  The  application  of  PO1, 
including  measure  (iii),  should  therefore  not  hinge  on  such  criteria.  Otherwise  the  objective  of 
preventing a lack of compensation and ensuring legal certainty would be in doubt.  

However,  the  impact  of  the  measures  will  vary  depending  on  the  specific  characteristics  of  AI 
systems.  Deterministic,  transparent  and  relatively  simple  AI  systems  entail  fewer  difficulties  of 
proof  so  the  possibility  of  rebutting  the  presumption  will  be  in  practice  particularly  relevant  for 
those  AI  systems.  The  beneficial  effect  of  the  rebuttable  presumption  alleviating  the  victim’s 
burden  of  proof  will  be  thus  particularly  relevant  for  those  AI  systems  which  show  the  specific 
characteristics of opacity, autonomous behaviour, complexity or limited predictability. 

Addressing  legal  uncertainty  and  legal  fragmentation  and  related  internal  market  obstacles 
(2.2., 2.3., 2.6.): PO1 would provide uniform rules clarifying how national burden of proof rules 
would be applied to the specific challenges of AI. This would avoid that national courts need to 
employ  ad-hoc  solutions  in  each  case  before  them,  to  achieve  equitable  solutions  for  adequate 
victim  compensation.  It  would  also  prevent  that  MS  see  a  need  to  adapt,  based  on  their  own 
different  approaches,  the  relevant  burden  of  proof  rules,  to  react  to  the  characteristics  of  AI 
challenging the application of those rules. 

PO1  will  provide  the  legal  certainty  needed  by  businesses  –  in  particular  SMEs  –  to  foresee 
possible liability risks and insure themselves against them. Accordingly, they will have investment 
stability to roll-out AI-enabled products and services in the internal market. Clear conditions for 
meeting  the  burden  of  proof  would  also  facilitate  ex-ante  risk-assessments  by  insurers  offering 
coverage of civil liability for damage caused by AI.  

Addressing the possible lack of compensation for victims leading to a lack of societal trust and 
hampering take-up by consumers (2.5., 2.6.): Measures designed to alleviate the exercise of the 
burden  of  proof  by  the  victim  allow  victims  to  have  successful  liability  claims  obtaining 
compensation in cases of damage created by AI. By avoiding that victims are less protected due to 
the involvement of AI than victims of traditional technologies, PO1 contributes to the creation of 
an ecosystem of trust for AI. It thereby promotes the uptake of AI-enabled products and services 

34 

 
 
in the EU134. All companies in the AI value chain would benefit from increased demand.  

Incentive effects: Finally, PO1 would have the ancillary purpose of incentivising compliance with 
the  AI  Act  and  damage  prevention.  It  will  thus  increase  the  level  of  protection  for  all  persons 
affected by the use of AI. 

In  the  public  consultation,  responding  EU  citizens,  consumer  organisations,  academic/research 
institutions and NGOs in their very large majority supported the measures envisaged under PO1, 
namely:  

-measures regarding the disclosure of information, support by 87% among EU citizens (80 out of 
92)  and  support  by  94%  among  those  organisations  (30  out  of  32),  with  none  of  the  latter 
disagreeing;  

- inferring facts from the refusal to disclose information, support by 84% among EU citizens (77 
out  of  92)  while  only  13%  disagreed  (12  out  of  92),  and  support  by  68%    amongst  those 
organisations (21 out of 31);  

- presuming causality in the case of non-compliance by AI providers with their safety obligations, 
support by 90% among EU citizens (82 out of 91) and support by 81% among those organisations 
(26 out of 32) with only one disagreeing;  

-  presuming  causality  in  the  case  of  non-compliance  by  AI  users  with  their  safety  obligations, 
support  by  59%  among  EU  citizens  (54  out  of  92)  while  only  9%  disagreed  (8  out  of  92)  and 
support by 65% among those organisations (20 out of 31) while 23% (7 out of 31) disagreed;  

- an alleviation of the burden of proof regarding the functioning of AI systems, support by 88%  
among EU citizens (81 out of 92)and support by  91% among those organisations (29 out of 32) 
with only one disagreeing. 

Among  business  stakeholders  the  responses  on  the  disclosure  of  information  were  relatively 
evenly split (33% or 28 out of 86 agreed, while 31% or 27 out of 86 disagreed).  

A majority was against inferring facts from the refusal to disclose information (63% or 54 out of 
86, while 22% or 19 out of 86 supported such a presumption), presuming causality (41% or 36 out 
of 86, while 30% or 26 out of 86 supported such a presumption) or shifting the burden of proof 
(63% or 54 out of 86, while 24% or 21 out of 86 supported such a shift)). The fact that 63 % of 
business  stakeholders  disagreed  was  one of the reasons  why the policy options  do not  include a 
reversal of the burden of proof, but only targeted alleviations in from of rebuttable presumptions. 
While29 % (25 out of 86) of business stakeholders disagreed, 35% (30 out of 86) agreed to apply 
such a presumption vis-à-vis the user of the AI-system.  

Responding  individual  SMEs  approved  of  those  measures.  The  views  of  business  associations 
representing (primarily) SMEs were in most cases roughly evenly split. 

However, interests of possible liable parties in preserving compensation gaps to the detriment of 
victims  who  would  have  to  bear  the  cost  of  the  damage  they  have  suffered  should  not  define 
public policy. This would be against the principles of justice and fair compensation that underlie 
all national liability laws. It would also be against the policy objectives of the AI White Paper to 
ensure an equal – and technology agnostic – level of protection for victims of harm.  

134  See  2.5.  and  2.6.  for  explanations  on  the  link  between  effective  liability  rules  for  AI  and  consumer  trust  and 
willingness to take up AI-enabled products and services, as confirmed in particular by the behavioural study. 

35 

 
 
                                                           
How does this PO interact with the measures envisaged for the PLD revision?  

In line with the AI White Paper objective, all PO need to ensure that victims having suffered harm 
caused by AI systems enjoy the same level of protection as those having suffered harm caused by 
other technologies. PO1 will therefore introduce measures to  ensure that  victims of AI can have 
effective claims based on fault, despite the specific characteristics of AI that make the burden of 
proof very difficult. 

In claims against the producer, the PLD revision will ease the burden of proof for damages caused 
by defects  in  complex  products,  including  AI-enabled products, by  rules  on when producers are 
obliged to disclose technical information and rebuttable presumptions. Moreover, the development 
risk defence, which exempts producers from liability when a  product’s  defective nature was not 
scientifically  discoverable  at  the  moment  it  was  put  into  circulation,  would  be  adapted  to  take 
account of the dynamic nature of products with digital elements, such as AI-enabled robots. If the 
defective  nature  becomes  scientifically  discoverable  while  the  producer  retains  control  of  the 
product’s safety features (e.g. through software updates), the defence would not be available. 

PO1 uses very similar tools to ensure that a fault based claim for compensation for harm induced 
by AI is as effective as for harm induced by other technologies. Compared to the PLD, PO1 covers 
additional forms of damages, additional victims and additional liable persons as well as different 
sources  of  damage.  Thus,  alleviations  of  burden  of  proof  are  complementary  between  the  two 
initiatives, as the following examples illustrate: 

- 

- 

the producer’s obligation to give the victim access to information for claims under the PLD 
is  complemented  by  the  disclosure  of  information,  based  on  this  initiative  about  liability 
for  AI,  in  claims  based  on  the  user’s  fault  or  in  claims  against  the  producer  for  damage 
suffered by business,  
presumptions  of  defectiveness  and  causality  under  the  PLD  are  complemented  by  a 
targeted presumption of causality, for the purposes of other civil liability claims under this 
initiative, when the liable person does not comply with the AI Act. 

In  their  combination  these  measures  will  ensure  that  victims  can  use  effectively  all  available 
routes  to  compensation.  Thereby  they  enjoy  the  same  level  of  protection  compared  to  damage 
created by other technologies. They will also provide the right incentives for all entities involved 
in the ‘life cycle’ of AI-enabled products and services to follow obligations meant to prevent harm 
from  happening.  This  way,  the  liability  risks  are  distributed  over  the  AI  value  chain  to  the 
appropriate liable person. 

(b)  PO2: PO1 + strict liability for AI use-cases with a special risk-profile 

PO2  combines  the  alleviations  of  the  burden  of  proof  envisaged  under  PO1  with  a  harmonised 
risk-based  strict  liability  regime  applicable  to  professional  operators/users  of  certain  highly 
autonomous AI-enabled products or services with a special risk profile.  

In order to ensure, in line with the AI White Paper objective, that victims have equally effective 
compensation paths for damages caused by AI and other technologies, all pillars of liability need 
to  be considered. All  MS  currently have, in  addition to  fault-based and PLD,  also  strict liability 
regimes. While they vary in scope and conditions, they usually cover risk of harm to high-ranking 
legal  interests  such  as  life,  health,  and  property.  These  regimes  assign  liability  to  the  person 
benefitting from the source of risk (usually owners, users, operators). In strict liability claims, the 
victim  only  has  to  prove  that  the  risk  ‘materialised’.  As  a  consequence,  the  liable  person 
determined  by  law  has  to  compensate  the  damage.  Thus,  strict  liability  is  an  easier  path  to 

36 

 
 
compensation for victims. 135 

Following  a  targeted  and  risk-based  approach,  PO2  is  based  on  the  consideration  that  the 
operator/user  is  the  one  that  decides  on  and  benefits  from  the  use  of  AI-enabled  products  or 
provision of AI-enabled services in practice.136 The operation of certain AI-enabled products and 
services  creates  a  risk  for  the  general  public  and  high-ranking  legal  interests.  For  instance, 
innocent bystanders, who do not choose to submit themselves to such risks and are mostly even 
unaware of them, should in such cases have an uncomplicated way of obtaining compensation for 
the harm suffered. Strict liability could be that solution.  

Such  harmonising  measure  would  be  limited  to  strict  liability  of  those  persons  operating  AI-
enabled products and services as part of their professional activity. This personal scope is justified 
by the fact that it is more likely that professionals would be able to control the operation of such 
products and services as part of their business activities, while consumers typically have little or 
no influence on how highly autonomous  AI systems  function and therefore no means to  control 
the  specific  risk  presented  by  such  AI  systems..  Moreover,  this  scope  is  consistent  with  the 
approach  of  the  AI  Act  and  existing  and  future  safety-relevant  legislation,  which  impose 
obligations on professionals only.  

Limiting the personal scope to professional users/operators is justified by the fact that consumers 
typically  have  no  influence  on  how  highly  autonomous  AI  systems  function  and  therefore  no 
means  to  control  the  specific  risk  presented  by  such  AI  systems.  For  this  reason,  it  is  doubtful 
whether the operation of AI-enabled products  with  a potential to  cause significant  harm to  legal 
interests of high value will at all be entrusted to consumers in the future, in particular in scenarios 
where such products  could cause harm to  unwitting third parties.  Indeed,  the AI Act  shows that 
professional users have a key role in  controlling the risks linked to the operation of AI systems. 
Should cases emerge where consumers play the role of a user/operator of AI technologies with a 
‘strict  liability  profile’,  these  cases  are  highly  likely  to  be  already  covered  by  strict  liability  in 
almost  all  MS,  as  exemplified  by  the  use-case  of  autonomous  vehicles.  The  risk  that  the 
harmonisation of strict liability for professional users/operators would create situations where the 
level of protection varies arbitrarily, from the perspective of the victim, compared to cases where 
the same type of AI system was used by a consumer, is therefore very small.   

Similar to a common approach in MS137, this limited strict liability regime could be coupled with 
an obligation for liable persons to insure themselves. This corresponds e.g. to the present situation 
for  car  accidents,  as  harmonised  by  the  Motor  Insurance  Directive.  In  addition  to  the  measures 
outlined for PO1 (measures i-iii), PO2 would thus comprise: 

(i)  A harmonised strict liability regime applicable to the person exercising control over the 
operation of the relevant highly autonomous AI technology (operator/user) in the course of 
their  professional  activity.  The  professional  in  whose  name  the  relevant  product  is 
registered or  who is  registered as service provider could  be presumed to  be the operator. 
Like the approach of the AI Act, the relevant products and services could be defined by a 
list  in a technical  Annex, complemented by the possibility for the Commission  to update 
that  list  by  delegated  acts  to  technological  and  market  developments.  The  AI  use-cases 
included  in  the  scope  of  the  strict  liability  regime  would  be  identified  on  the  basis  of 
criteria consistent with MS’ legal traditions, such as the ability to cause frequent or severe 
harm,  in  particular  to  high-ranking  legal  interests  like  life,  health  or  property,  and  the 

135 Cf. Expert Group Report, pp. 39 and 40. 
136 The  fact that someone benefits  from the  use of a  certain technology is a common criterion under  many existing 
national strict liability regimes (cf. Expert Group, pp. 35 and 39.  
137 Cf.  Comparative Law Study, p. 61. 

37 

 
 
                                                           
likelihood  that  the  public  at  large  or  at  least  unwitting  third  parties  are  exposed  to  such 
risks. These criteria could, for instance, be met by technologies like autonomous drones or 
vehicles.  In  contrast,  strict  liability  would  likely  not  be  appropriate,  for  example,  for 
merely  stationary  robots  operating  exclusively  in  confined  environments  and  presenting 
risks to only a narrow, pre-defined range of people.   
In addition, only the grounds for excluding or reducing strict liability would be harmonised 
(e.g. force majeure and own fault), while other modalities would be left to national law.  
(ii)  As  a  cumulative  sub-option,  an  insurance  obligation  for  the  strictly  liable  persons, 
combined  with  a  right  for  the  victim  to  make  a  direct  claim  against  their  insurance 
undertaking. The insurer who paid compensation would be entitled to claim it back from 
any  other  party that could be  liable  for the damage under other compensation paths  (e.g. 
PLD or fault-based). 

Because  of  the  high  interests  at  stake  and  the  victim’s  particularly  vulnerable  position,  strict 
liability  ensures  that  compensation  is  promptly  and  easily  granted  to  the  victim.  However,  this 
does not imply that somebody else might not be at fault for the damage or, in the case of products, 
that the AI-enabled product was not defective. Therefore, it is relevant for the strictly liable person 
and  insurance  of  that  person  to  have  effective  liability  claims  for  eventually  recovering  the 
compensation paid to the victim from the person ultimately responsible for the damage. 

The envisaged harmonised measures would not create additional layers of regulation. Where MS 
already  provide  for  strict  liability  for  the  cases  covered  by  the  harmonising  measure,  mainly 
because that type of activity or goods are already subject to strict liability (e.g. cars in almost all 
MS),  those  MS  would  have  already  transposed  the  measures  under  PO2.  When  EU  law  already 
provides for respective insurance requirements (e.g. mandatory insurance for motor vehicles or for 
drones above 20 kg), that requirement would continue to apply. It would neither be replaced nor 
superimposed by an additional insurance requirement for certain AI systems.  

Approach to addressing legal uncertainty and legal fragmentation and related internal market 
obstacles  (2.3.  +  2.4.):  By  specifying  the  AI-enabled  technologies  subject  to  strict  operator 
liability  at  EU level,  PO2 aims  to  increase legal  certainty  and predictability of liability risks  for 
potentially  liable  parties.  It  also  prevents  the  emergence  of  divergent  AI-specific  strict  liability 
regimes at national level. The approach underpinning PO2 relies on a similar logic, a targeted and 
risk-based approach, as PO1. However, it goes further into MS’ liability systems and into the risk 
distribution between stakeholders because of the special risk profile of the AI technologies subject 
to  strict  liability.  The  possibility  for  operators  to  cover  their  liability  risk  with  (mandatory  or 
voluntary)  insurance,  thus  reducing  their  risk  exposure  to  the  annual  premium,  is  an  important 
component of this PO. 

Approach  to  addressing  the  possible  lack  of  compensation  for  victims  leading  to  a  lack  of 
societal  trust  and  hampering  take-up  by  consumers  (2.5.):  Strict  liability  of  the  operator/user 
addresses the proof-related challenges posed by AI. The victim only has to prove that a certain risk 
materialised in the sphere of the liable person, instead of having to establish a misconduct.138 The 
sub-option of mandatory insurance would relieve the victim of the liable party’s insolvency risk. 
These  features  of  the  PO  would  increase  societal  trust  in  AI,  even  when  it  has  a  specific  risk 
profile. It would thereby promote its roll-out in the internal market. 

In the open public consultation NGOs, academic/research institutions, consumer organisations and 
EU citizens supported either full or minimum harmonisation of strict liability. In particular, 77%, 
or 68 out of 88, of EU citizens favoured a ‘full harmonisation’ approach to strict liability while the 

138 Expert Group Report, p. 21 

38 

 
 
                                                           
7  responding  consumer  organisations  supported  only  a  minimum  harmonisation  approach. 
Business stakeholders tend to oppose the harmonisation of strict liability. Opposition was stronger 
regarding  a  minimum  harmonisation  (70 %,  or  59  out  of  84,  while  only  14%,  or  12  out  of  84, 
support) than a full harmonisation approach (42%, or 35 out of 84, disagreement while 30%, or 28 
out of 84 support). Interestingly, almost all responding individual SMEs (9) approved of this PO, 
and  business  associations  representing  (primarily)  SMEs  were  evenly  split  regarding  (both  the 
minimum and full) harmonisation of strict liability.  

How  does  this  PO  interact  with  the  measures  under  the  PLD  revision?  The  existing  liability 
system offers victims different avenues to seek compensation from various liable persons, under 
different instruments and for different types of harm (defect-based liability of producers under the 
PLD; national fault-based liability of any ‘wrongdoer’; national strict liability for certain specified 
risks). It is therefore necessary to work on all the existing avenues of compensation to ensure the 
same  level  of  victim  protection.  These  avenues  attach  liability  to  different  grounds  and  the 
proposed measures will ensure coherence.  

The preferred option for the PLD revision would extend strict liability to the person who produces 
software (including AI) or provides digital services necessary to make a product work. PO2 would 
harmonise  strict  liability  of  the  person  who  operates  products  equipped  with  AI  or  provides 
services equipped with AI under particular circumstances of risk. 

It  can, of  course,  happen that the  same person is  linked to  a damage in  different  capacities, and 
therefore  fulfils  the  conditions  of  liability  under  different  instruments.  Given  that  this  is  a  well-
known and accepted situation under the existing national liability systems and an expression of the 
complementary nature of the pillars of liability, it is not expected to negatively affect innovation 
or uptake of AI-enabled products compared to the baseline scenario. For example, a producer of 
autonomous  vehicles  providing  transport  services  with  a  fleet  of  such  vehicles  would  be  liable 
under the PLD for defects of the vehicles in their capacity as producer and under the harmonised 
strict liability regime for AI, in their capacity as user/operator.  

(c) 

PO3:  PO1  +  targeted  review  regarding  strict  liability  and  mandatory  insurance 
measures 

PO3 follows a staged approach, consisting in the implementation of PO1 in a first step, followed 
in a second step by a targeted review and re-assessment of the need for harmonising strict liability 
of the operator/user and mandatory insurance elements of PO2. Going beyond an evaluation of the 
already adopted measures, the targeted review would involve a dedicated mechanism designed to 
inform  the  future  policy  decisions  on  those  additional  elements.  While  the  usual,  backward-
looking  evaluation  of  the  effectiveness,  efficiency  and  relevance  of  already  enacted  EU  rules 
would  be  done  also  under  PO1  in  line  with  general  better  regulation  rules,  the  targeted  review 
under PO3 takes  a different, forward-looking perspective and  provides for additional substantial 
steps  to  be  taken  by  the  Commission.  Although  PO3  would  not  (yet)  implement  all  elements 
suggested  by  the  European  Parliament’s  legislative  own-initiative  resolution,  an  EU  instrument 
including the targeted review mechanism would nevertheless reflect the approach suggested by the 
Parliament, that is  to  say  a combination of measures to  ease the burden of proof  for fault-based 
claims with a harmonised strict liability regime for certain specific AI applications. It would also 
demonstrate  that  the  Commission  is  ready  to  act  on  the  opinions  expressed  by  non-business 
stakeholders in the public consultation, who showed clear support for strict liability. The targeted 
review  provides  a  framework  and  prepares  the  ground  for  the  Commission  to  take  a  policy 
decision  specifically  on  the  harmonisation  of  strict  liability  and  mandatory  insurance,  at  a  later 
point in time when all the information for that decision will be available.    

The review would notably cover the following points: 

39 

 
 
- 

- 

- 

- 

the market developments regarding the rollout of products and services enabled by AI-systems 
with  the  characteristics  that  challenge  existing  (primarily  fault-based)  liability  rules:  a  high 
degree  of  behavioural  autonomy,  opacity  (complexity  +  lack  of  transparency),  continuous 
adaptation and limited predictability; 
the risk-profile and operating environment  of the products  and services, in  particular whether 
they can cause harm to high-ranking legal interests of the public at large; 
the  incidence  rate  of  accidents  caused  by  AI  systems,  in  particular  those  involving  harm  to 
high-ranking  legal  interests  of  unwitting  third  parties  in  the  course  of  the  operation  of  the 
relevant products or provision of the relevant services; 
any AI-specific liability gaps regarding damage caused by the identified products and services, 
despite the alleviations of the burden of proof implemented under PO1. 

Aside from assessing  to  what  extent PO1, in  combination with the  regulatory measures taken to 
prevent  highly  autonomous  AI-enabled  technologies  from  causing  harm  (e.g.  the  AI  Act,  the 
MPR,  the  General  Product  Safety  Regulation,  future  measures  under  the  ‘old  approach’  safety 
legislation)  achieves  the  Commission’s  policy  objectives  effectively,  the  review  will  allow  to 
conclude  if  the  harmonisation  of  strict  liability  and  mandatory  insurance  elements  of  PO2  are 
needed as well.  

The  staged  approach  would  also  allow  the  European  insurance  industry  to  acquire  sufficient 
actuarial data on the realisation of AI-specific liability risks, which was a point consistently raised 
by insurers in the consultation activities. PO3 would enable the Commission to take into account 
how  the  insurance  market  for  AI  has  evolved,  when  re-assessing  the  need  for  AI-specific 
mandatory insurance during the targeted review. Future regulatory developments at EU or national 
level could also be taken into consideration, namely on AI-enabled products and services with  a 
relevant  risk  profile  (e.g.  autonomous  vehicles  and  unmanned  aircraft).  For  measures  to  ensure 
that the targeted review will be based on a sufficient evidentiary basis, see Section 9 and Annex 12 
on monitoring and evaluation. 

How does this PO interact with the PLD revision? At the first stage, the interplay with the PLD 
revision is the same as under PO1. In case of the introduction of strict liability as the outcome of 
the targeted review, the interplay would be as described under PO2. 

Minimum  harmonisation  approach:  All  of  the  PO  would  follow  a  minimum  harmonisation 
approach. This  means that  MS could  maintain or  – outside of the scope of the PLD  – introduce 
rules that are more favourable for victims. For example, MS could have more general reversals of 
the burden of proof regarding fault or causation. They could also maintain general strict liability 
regimes  (e.g.  for  ‘dangerous  things  or  activities’)  applicable  also  to  damage  caused  with  the 
involvement of AI systems. 

In  contrast,  full  harmonisation  could  lead  to  a  lower  level  of  protection  compared  to  situations 
where Member States provide already for a more general strict liability regimes in their national 
law  which  could  also  apply  to  AI  technology.  In  the  light  of  the  long-standing  and  entrenched 
divergences  between  national  tort  law  traditions,  an  attempt  of  a  full  harmonisation  blocking 
maintaining existing further-reaching national rules would meet with strong opposition from MS It 
would  also  be  opposed  by  certain  stakeholders  as  the  position  of  consumer  associations 
participating in the public consultation shows. The same is not the case for an initiative based on 
minimum  harmonisation.  Such  an  approach  would  ensure  that  the  new  rules  can  be  integrated 
without frictions into the existing legal civil liability framework within each MS. 

This approach based on a realistic assessment of political feasibility  is also reflected in the draft 
regulation proposed by the EP, which, after political discussion on this subject, ultimately opted 
for a minimum harmonisation approach.  

40 

 
 
While  protecting  the  victim, it is  acknowledged  that  minimum harmonisation does not  create an 
entirely level playing field. This is reflected in the reactions of business stakeholders in the public 
consultation.  While  business  stakeholders  overall  in  their  majority  resist  strict  liability,  the 
opposition to minimum harmonisation of strict liability is stronger than for full harmonisation. 

However,  for  considerations  of  political  feasibility  within  both  branches  of  the  European 
legislator, subsidiarity  and proportionality, the  measures on AI liability  should interfere in the  – 
very heterogeneous – national tort law traditions only to the unavoidable extent. 

5.3. Measures discarded at an early stage 

(a)   Harmonisation of risk-based liability for damage caused by all AI-enabled products 
or  services,  irrespective  of  their  risk-profile,  possibly  coupled  with  mandatory 
insurance 

A horizontal strict liability regime applicable to users/operators of AI-applications irrespective of 
the  risk  linked  to  their  activities  would  be  neither  proportionate  nor  suitable  to  achieve  the 
objectives  of  this  initiative.  While  such  an  approach  may  increase  legal  certainty  and  prevent 
fragmentation  between  possible  future  AI-specific  national  liability  rules  to  the  highest  extent, 
possible benefits for companies seeking to invest in AI would be negated by the far-reaching shift 
of  liability  risks  and  associated  compliance  costs.139  This  discarded  PO  would  entail  high 
adaptation costs for companies. Moreover, it is uncertain whether the insurance industry would be 
able to cover the new strict liability risks across the broad range of AI-enabled technologies.140  

The  combined  effects  would  likely  dis-incentivise  investments  in  such  technologies,  stifling 
innovation  and  slowing  down  the  take-up  of  AI  by  European  businesses,141  having  an  overall 
negative effect on cross-border trade in AI-enabled products and services.142  

This  discarded  PO  would  go  beyond  the  objective  to  ensure  that  persons  harmed  by  AI  enjoy  a 
similar  level  of  protection  as  persons  having  suffered  harm  caused  by  other  technologies. 
Prescribing strict liability whenever AI-systems are involved would, in many cases, treat victims 
of AI-induced damage better than victims of traditional technologies.143  

(b)   Harmonisation of the types of – in particular immaterial – harm giving rise to civil 

liability claims when caused by AI 

In  its  own-initiative  resolution,  the  EP  requested  the  Commission  to  analyse  in  depth  the  legal 
traditions in all MS and their existing national laws that grant compensation for immaterial harm, 
in order to evaluate if the inclusion of immaterial harm in AI-specific legislative acts is necessary 
and if it contradicts the existing Union legal framework or undermines the national law of the MS. 
The Commission’s analysis has shown that the legal situation in MS regarding the compensability 
of non-material  harm, as well as pure economic  loss, is  fragmented.144 The  matter is  marked by 
differing  long-standing  legal  traditions.  It  is  not  an  AI-specific  question  but  rather  a  horizontal 
one.  

Creating  a  situation  whereby  victims  might  be  able  to  claim  compensation  for  a  certain  type  of 
harm,  for  instance  immaterial  harm,  only  if  it  was  caused  by  AI  would  not  be  in  line  with  the 

139 Cf. Economic Study, pp. 170 et seq. 
140 Ibid., pp. 183 et seq 
141 Ibid., pp. 170 et seq. 
142 Ibid., pp. 197 and 198. 
143 Economic Study, p. 137. 
144 See Annex 9 for details. 

41 

 
 
                                                           
Parliament’s and the Commission’s common objective to ensure that victims have the same level 
of  protection.  Such  an  approach  would  give  victims  of  harm  caused  by  AI-systems  a  better 
protection compared to harm caused by other technologies.  

It  is  therefore  appropriate  not  to  include  a  harmonisation  of  the  compensable  harm  in  the 
envisaged  AI-specific  instrument  on  civil  liability.  By  ensuring  the  effectiveness  of  existing 
liability rules, the envisaged policy measures will nevertheless help victims to claim compensation 
also for other types of harm such as immaterial harm or pure economic loss, to the extent that such 
harm is compensable under national liability rules. 

6.  WHAT ARE THE IMPACTS OF THE POLICY OPTIONS? 

The  impacts  of  the  policy  options  are,  for  the  most  part,  economic  in  nature.  However  the 
initiative  will  also  have  relevant  social  impacts,  mainly  related  to  victim  compensation  and 
incentivising  the  prevention  of  harm.  A  deeper  assessment  of  impacts  was  focused  on  the  most 
significant ones, identified based on stakeholders’ views. 145 

The quantification of data was to a large extent not possible, due to the novelty of the technology 
and the scarcity of products and cases of damage or compensation. Mitigation actions were taken 
that led to the use-case modelling of costs of insurance, macro-economic approaches to estimate 
impact  of  legal  fragmentation,  as  well  as  micro-economic  approach  for  a  specific  use  case.  The 
available quantified estimates (production value affected by the obstacles of legal uncertainty and 
fragmentation of liability rules of the use-cases, overall market value affected by these obstacles, 
costs of claiming compensation) were then used to extrapolate quantified estimates of the impacts 
of policy options on the costs linked to the burden of proof, on the EU AI market value and of the 
incremental  change  in  insurance  premiums.  In  light  of  the  scarcity  of  quantitative  evidence,  the 
costs and benefits of the policy options were largely assessed using qualitative scales, while taking 
into account the available quantified estimates. 

For the cost quantification, administrative costs are less significant under this initiative compared 
with  other  sectoral  legislation  because  there  are  no  specific  obligations  or  information 
requirements imposed on economic operators. There will be some adjustment costs for potentially 
liable parties, related to liability insurance premiums.146 

6.1. PO1: Easing the burden of proof for AI-related claims 

(a)  Effectiveness 

-  Degree  to  which  SO1  and  2147  would  be  achieved:  Targeted,  risk-based  alleviations  of  the 
burden  of  proof  aligned  with  the  AI  Act  would  address  the  major  sources  of  legal  uncertainty 
identified in the problem analysis. It would thereby achieve the more general objective to improve 
conditions for cross-border business activities involving AI.  

-  Degree  to  which  SO3  would  be  achieved:  The  envisaged  alleviations  of  the  burden  of  proof 
would  effectively  prevent  AI-induced  compensation  gaps,  and  thus  be  suitable  to  ensure  that 
victims suffering harm caused by AI – whether they are consumers or businesses – have the same 
level  of  protection  as  victims  harmed  by  other  technologies.  Victims  would  be  relieved  from 

145  For  an  overview  of  the  success  criteria,  relevant  stakeholders  and  respective  scores  of  the  PO  with  respect  to 
effectiveness, and detailed explanations on the comparison of PO, see Annex 10, Section A. 
146  For  the  interplay  and  synergies  between  the  policy  options  under  this  impact  assessment  and  the  PLD  impact 
assessment see section 8.2. 
147  Given  the  close  links  and  interactions  between  legal  uncertainty  and  legal  fragmentation  (see  2.6.  and  2.7.),  the 
efficiency of PO is assessed together for SO 1 and 2. 

42 

 
 
                                                           
having  to  overcome  the  characteristic  opacity  of  certain  AI-systems  to  prove  their  claims.148 
Consequently, they would have to spend less on technical expertise and have better prospects of 
making a successful claim.149  

The perceived low likelihood of compensation and the difficulty to determine who is liable count 
amongst the most relevant reasons for low levels of consumer trust in and societal acceptance of 
AI.150  Consumers  who  perceive  liability  rules  as  appropriate  to  protect  victims  of  harm  are 
significantly more willing to take up such products and services.151 A liability regime where the 
burden  of  proof  has  been  adapted  in  favour  of  the  victim  ranks  higher  in  the  perception  of 
consumers  than  a  regime  where  the  victim  bears  the  full  burden  of  proof.152  PO1  is  therefore 
expected  to  contribute  –  together  with  the  already  proposed  adaptations  of  safety  rules  –  to 
increasing  the  level  of  societal  trust  in  AI-enabled  products  and  services  and  consumers’ 
willingness to take up such products and services.  

-  Indirect  social  impacts:  By  preventing  liability  deficits,  PO1  would  provide  an  effective 
incentive  to  prevent  harm  and  thus  drive  safety-enhancing  innovation.  It  would  thus  contribute 
indirectly to people’s overall level of safety.153 This mechanism would apply firstly to businesses 
subject  to  specific  safety  requirements  –  in  particular  the  user  and  provider  under  the  AI  Act. 
Secondly, by ensuring the effectiveness of general liability rules under national law, the incentive 
effect of PO1 could extend to any stakeholders whose actions or omissions may have contributed 
to the causation of damage, such as  e.g. providers of labelled training or testing data. Moreover, 
behavioural  research has shown that  adapting the burden of proof in favour of the injured party 
makes  people  more  likely  to  consider  that  victims  receive  just  compensation  and  that  the  legal 
framework is reasonable, predictable and transparent.154 By promoting effective access to justice, 
PO1 is hence likely to maintain or increase societal trust in the justice system. 

(b)  Efficiency (costs and benefits for affected stakeholders) 

- Impacts on potentially liable parties (businesses and natural persons): The reduction of legal 
uncertainty  and  fragmentation  would  generate  direct  regulatory  benefits  for  businesses,  namely 
through  lower  legal  information/representation,  internal  risk  management  and  other  compliance-
related costs, as well as additional cross-border revenue.155 By clarifying the kind of information 
and evidence potentially liable parties may be required to submit in civil proceedings, PO1 would 
help  them  to  choose  more  efficiently  between  different  technological  options156,  namely  by 
favouring  more  transparent  and  explainable  solutions.  As  start-ups  and  other  SMEs  are 
significantly  more  affected  by  the  internal  market  barriers  created  by  legal  uncertainty  and 
fragmentation  (see  2.6.),  this  stakeholder  group  would  also  benefit  to  a  higher  degree.157  The 
expected positive impacts of PO1 on societal trust in AI and consumers’ willingness to take up AI-
enabled products and services, as well as the improved competitiveness of the European AI sector 

148 Economic Study, p. 133. 
149 Ibid. 
150 Behavioural Economics Study, op. cit., pp. 24, 25 and 33. 
151 Ibid., p. 47. 
152 Ibid., p. 75. 
153 Economic Study, p. 164. 
154 Behavioural Economics Study, executive summary, p. iii. 
155 Cf. Economic Study, pp. 113 et seq., 161 and 163. 
156  For  explanations  on  how  liability  rules  may  influence  how  products  and  services  are  designed,  see  e.g. 
Galasso/Luo, Punishing Robots  - Issues in the Economics of Tort Liability and Innovation in Artiﬁcial Intelligence, 
in: Agrawal/Gans/Goldfarb (eds.), The economy of artificial intelligence: an agenda, Chicago 2020, p. 493(501). 
157 Cf. Economic Study, pp. 122 and 161. 

43 

 
 
                                                           
would directly or indirectly benefit all companies in the AI value chain.158  

These  benefits,  which  have  been  approximated  in  terms  of  increases  in  the  AI  market  size  by 
EUR 500mln  to  EUR 1.1bln159,  are  likely  to  outweigh  the  following  adaptation  (substantive 
compliance) costs and redistribution effects linked to PO1. The business-as-usual costs under the 
baseline  scenario,  related  to  the  uncertainty  to  assess  what  liability  rule  would  apply  to  AI  and 
what  burden-of-proof  rule  a  court  would  apply  in  a  concrete  case,  are  higher  than  any  potential 
adjustment  costs  borne  by  potentially  liable  parties.  It  will  be  easier  for  companies  to  estimate 
liability risks and related costs. While under the baseline scenario courts might apply on an ad-hoc 
basis alleviations of the burden of proof to remedy what they consider an unequitable result, clear 
and harmonised alleviations of the burden of proof will help liable parties to know what to expect 
in  case  AI  is  involved  both  domestically  and  cross-border.  Companies  operating  cross-border 
would benefit from reduced compliance costs compared to the very fragmented baseline scenario. 
Such clarity might also help companies get appropriately priced liability insurance coverage. 

In cases where the specific characteristics of AI would not have allowed the victim to prove the 
necessary facts under the baseline scenario, PO1 would shift the cost of compensating the relevant 
damage from the victim to the liable person, increasing the latter’s liability exposure. This result is 
not  regarded as an  undesirable impact  or undue burden.  It  is  in  line with the policy objective to 
ensure that victims of damage caused with the involvement of AI systems have the same level of 
protection as victims of damage caused by other technologies and in general with the purpose of 
liability law. It also achieves a more efficient cost-allocation to the person best placed to prevent 
damage from occurring. For the impact analysis, it is taken into account as a re-distribution effect. 
These  effects  are  inherent  in  the  Commission’s  objective  of  avoiding  that  victims  are  less 
protected due to the use of AI. In practice, the added burden on potentially liable parties is likely 
not to be substantial because:  

- 

a major share is likely to have professional liability insurance160, which covers also risks linked 
to business activities involving AI161. It allows to limit costs to the annual insurance premium, 
making it even more predictable and easier to manage;  

-  PO1 does not involve a general reversal of the burden of proof, but only targeted adjustments to 
counter-balance  the  specific  challenges  of  AI.  The  negative  economic  impacts  for  potentially 
liable parties are likely to be very marginal162;  

-  PO1  is  likely  to  achieve  an  overall  more  efficient  allocation  of  the  burden  of  proof,  as  the 
difficulty  to  establish  how  or  why  an  AI  system  arrived  at  a  harmful  output  is  typically  less 
burdensome  for  potentially  liable  parties  having  influenced  the  operation  of  that  AI-system 
(e.g. developers, users) than for victims;  
as PO1 does not introduce new grounds of liability and keeps the basic allocation of the burden 
of proof intact, it is not expected to lead to a major increase in the number of civil actions – or 
an  associated  increase  of  insurance  premiums  –  compared  to  the  baseline,  nor  to  significant 
familiarisation and implementation costs for businesses.  

- 

Especially  the  instrumental  role  of  insurance  in  distributing  and  managing  the  impacts  of  the 

158 Ibid., p. 165. 
159 See below in ‘indirect economic impacts’ for details 
160  Ibid.,  pp. 141,  142,  163,  164.  The  study  supporting  the  evaluation  of  the  PLD  found  that  around  80%  of 
manufacturers  hold  liability  insurance  (cf.  Ernest  &  Young,  Evaluation  of  Council  Directive  85/374/EEC,  2018, 
p. 19).  While  insurance  uptake  may  not  be  identical  amongst  operators/users  of  AI  technologies  subject  to  strict 
liability, it can nevertheless be deduced that voluntary insurance coverage is widespread, at least amongst businesses 
whose activities involve the risk of causing substantial damage. 
161 This  was confirmed by input received from the insurance  industry during the  consultation activities feeding into 
this IA. 
162 This is indicated by the quantified estimates set out under the relevant quantification box. 

44 

 
 
                                                           
envisaged  policy  measures  needs  to  be  underlined.  Ultimately,  insurance  coverage  will  allow 
potentially  liable  parties  to  spread  liability  costs  across  the  community  of  all  insured.  This 
mechanism  limits  the  economic  burden  on  each  individual  insurance  holder  to  the  premium, 
preventing  a  possible  deterring  effect  of  liability  risks  and  keeping  market  entry  barriers  low, 
which facilitates the roll-out of AI in particular by start-ups and other SMEs.163 A large portion of 
potentially  liable  parties  concerned  will  likely  procure  insurance  coverage  voluntarily  to  benefit 
from this cost-limiting effect.164  

In this context, the regulatory framework established by the AI Act for the development and use of 
high-risk AI systems  is  likely to  improve the conditions  for AI risk assessment by insurers over 
the coming years. In addition, the proposed Data Act will promote access to data generated by a 
user’s product and thus facilitate the provision of services that depend on or can be improved by 
such  data,  including  insurance  and  data  analytics.165  Moreover,  the  Commission  services  will 
respond to the EP’s call to work closely with the insurance market to develop innovative insurance 
products166 and facilitate a  dialogue between the insurance industry and  companies active in  the 
AI market (in particular SMEs). 

Consequently,  while  a  lack  of  actuarial  data  at  both  the  current  and  the  initial  stage  of 
implementation  is  likely  to  influence  to  some  extent  the  premiums  of  AI-specific  insurance 
products, for a transitional period, and make these premiums more volatile, this effect is expected 
to dissipate quickly as the data generated during the operation of these technologies will allow risk 
estimations to converge faster towards the optimum than in the case of ‘traditional’ technologies. 

Hence, a development of a competitive insurance market for AI-related liability risks may safely 
be expected; and such development will provide the necessary conditions for effective coverage at 
moderate  prices.167  This  expectation  is  supported  by  the  fact  that  the  insurance  industry  is 
forcefully pursuing the development of innovative AI-specific insurance products, to explore the 
substantial new opportunities linked to this growth market.168 First insurance policies designed to 
cover specifically AI-enabled technologies have already been rolled out.169 AI liability insurance 
may also be incorporated as an additional feature into existing general policies.170  

163 Economic Study, p. 163. 
164 Ibid., pp. 163 and 164. 
165 See the proposal for a Regulation on harmonised rules on fair access to and use of data (Data Act), COM(2022) 68 
final ; cf. also Economic Study, p. 201. 
166 EP, Resolution 2020/2014(INL) on a civil liability regime for artificial intelligence, recital 22. 
167 Ibid., pp. 148 et seq., 164. 
168 Cf. e.g. Harvard Business Review, The Case for AI Insurance, April 2020. 
169 E.g. Ensure AI‘ by MunichRe, enabling AI providers to guarantee the performance of their AI systems, including 
for AI-systems controlling physical products (e.g. robots). Novel technologies are being developed through statistical 
research (e.g. ‘distribution-free uncertainty quantification’ including ‘conformal inference’), which will enable a more 
standardised failure rate assessment.  
Cf. also  La  Playa, offering brokerage of  specialist  AI insurance. Experts  working on  AI  insurance  for a  leading re-
insurance company confirmed in bilateral contacts with the Commission services that the insurance industry expects 
an increasing demand for specific AI liability coverages and are developing capabilities to assess AI risks with a view 
to developing new products, including for the coverage of third-party losses. 
170 See e.g. Insurance Europe’s reply to the public consultation: “…insurance can lessen the negative consequences of 
accidents  involving  AI  by  ensuring  that  the  victim  receives  compensation.  There  are  already  many  such  insurance 
solutions available in the European insurance market. Protection against material damage incurred by AI generally 
falls  within  the  remit  of  general  liability  insurance  policies,  which  are  sold  on  an  all-risks  basis.”  Similarly,  the 
German Insurance Association (GDV) submitted that “the voluntary insurance market works well in providing actors 
of  all  kinds  with  appropriate  liability  insurance.”  Likewise,  a  leading  European  insurance  company  explained  in  a 
bilateral exchange with the Commission services that AI liability will be likely be incorporated as additional feature 
into existing insurance policies, and that the increasing integration of AI in business activities is not expected to lead 
to an increase of premiums on a general level.  

45 

 
 
                                                           
The  results  of  the  public  consultation  confirmed  that  insurance  solutions  could  ensure  that  the 
victim  receives  compensation  (64%  agreement  v.  only  5 %  disagreement)  and  limit  the  costs  of 
potential damage for the liable person to the insurance premiums (49 % agreement v. only 19 % 
disagreement).  Even 
(business  associations  + 
companies/business  organisations)  who  confirmed  the  latter  effect  (38 %)  was  more  than  three 
times as large as the share of those not agreeing with it (12 %). 

share  of  business 

stakeholders 

the 

All business stakeholders that agreed with the harmonisation of strict liability also agreed with the 
benefits  of  insurance  (limiting  cost,  ensuring  compensation).  Specifically,  they  agreed  that 
mandatory  insurance  coverage  ultimately  spreads  –from  a  moderate  to  a  very  large  extent-  the 
liability costs over everyone taking out insurance, avoiding too high and burdensome one-off costs 
for  the  liable  party  and  that,  by  limiting  such  costs  to  the  insurance  premiums,  it  facilitates 
business  planning  and  lowers  market  entry  barriers,  especially  across  borders.  In  this  respect, 
Insurance  Europe  submitted  that  “liability  insurance  plays  a  vital  role  by  transferring  liability 
risks from companies and consumers to insurers and thereby, protecting the insureds’ economic 
position as well as ensuring that injured persons are compensated for loss or damage.” 

 Along  the  same  lines,  33%  of  business  stakeholders  expect  that  insurers  will  increase  risk 
premiums  (at  least  to  a  small  extent)  due  to  a  lack  of  predictability,  if  the  current  liability 
framework is not adapted to the characteristics of AI, while 36% of business stakeholders do not 
expect  this  at  all.  At  the  same  time,  50%  of  them  expect  an  increase  of  insurance  premiums, 
compared to only 14% that do not expect this at all, if MS adopt fragmented liability rules for AI 
at national level. Namely, business stakeholders acknowledge that while EU action might lead to a 
transitional,  moderate  increase  of  the  insurance  premiums,  the  absence  of  it  would  have  a  more 
negative  impact  on  the  market.  In  addition,  approximately  31%  of  business  respondents  agreed 
that in cases where possible facilitations of the burden of proof would apply, the potentially liable 
party would likely be covered by (voluntary or mandatory) liability insurance, as opposed to only 
11% who disagreed. Given that this statement, interestingly enough, also received a share of 31% 
‘neutral’ responses, i.e. almost equal to those in favour of the statement, these results point clearly 
to PO3 - adopting both such facilitations and a staged approach towards insurance - as the option 
that is most representative of the views of the market and thus, the preferred one, as compared to 
the other two options. 

Quantification:  Given  the  future-oriented  nature  of  this  initiative  the  lack  of  quantified  data 
meant that, regarding liable parties, the re-distribution effect of preventing compensation gaps was 
approximated based on reasoned assumptions on the insurance cost linked to AI liability risks and 
the  incremental  change  PO1  might  entail  in  the level  of  claims  and  insurance  premiums171.  The 
absolute overall amount of annual liability insurance premiums in the EU is estimated to increase 

171 This assumption is based on the following considerations: 
-  In  many  cases,  national  courts  already  have  similar  tools  (disclosure  orders,  presumptions)  at  their  disposal 
under  the  baseline  scenario,  although  it  is  highly  uncertain  whether  and  how  these  tools  would  be  used  in 
practice. 
- Under the baseline, some MS might take partly similar measures in their national legal systems to address the 
specific  challenges  of  AI.  However,  it  is  uncertain  how  many  would  do  so  and  what  precise  shape  these 
measures  would  take.  National  initiatives  would  in  all  likelihood  not  be  aligned  and  thus  entail  further  legal 
fragmentation. 
-  The  increased  legal  certainty  and  reduced  fragmentation  delivered  by  PO1  will  have  a  premium-lowering 
effect on insurance, which will partly offset the premium-driving effect of preventing AI-induced compensation 
gaps. 

46 

 
 
 
                                                           
moderately by EUR 5.35mln-16.1mln due to this PO.172  

This  estimate  was  based  on  available  information  on  annual  premiums  paid  for  general  liability 
insurance (EUR 42bn in 2019173), estimates of the shares of these premiums linked to AI-related 
economic  activities  (between  EUR 10.57mln  and  EUR 32.21mln,  based  on  the  low  and  high 
estimates  of  AI  market  sizes),  input  from  insurance  stakeholders  to  the  effect  that  AI-related 
liability  risks  can  largely  be  covered  by  existing  general  liability  insurance  policies174,  and 
estimates of the extent to which PO1 could shift the burden of compensating damage caused by AI 
from the victim to the party responsible for that damage. The quantified estimates also take into 
account  that,  during  an  initial  transitional  period,  the  scarcity  of  relevant  actuarial  data  on  AI 
liability risks will make it more difficult for insurers to calculate premiums compared to insured 
activities not involving AI. It was notably estimated that alleviations of the burden of proof as per 
PO1 would cause an increase by 15 % of the general liability insurance premiums attributable to 
AI liability risks.  

With  respect  to  high-risk  AI-systems,  PO1  could  indirectly  entail  some  minor  administrative 
burden, namely to the extent that it prompts the disclosure, in particular in the framework of civil 
proceedings,  of  information  documented  pursuant  to  the  AI  Act.  However,  PO1  relates  only  to 
information that had to be logged, documented, and stored, in any case, for possible disclosure to 
supervisory authorities pursuant to the AI Act. This measure would thus firstly not affect private 
individuals  or companies  not  falling in  the scope of the AI Act.  In addition, providing the same 
information,  in  particular  also  in  the  context  of  civil  proceedings,  is  not  expected  to  entail  a 
significant added burden. Moreover, the harmonised disclosure rules would require national courts 
to ensure proportionality, i.e. to avoid any excessive administrative burden.  

- Impacts on victims of damage caused by AI (natural persons and businesses): PO1 would 
relieve  the  victims  of  the  burden  of  bearing  the  damage,  to  the  extent  that  their  claims  for 
compensation  would  have  failed  under  the  baseline  due  to  the  specific  challenges  of  AI.  This 
burden would be re-distributed to the person responsible for causing the damage. This applies not 
only in respect of material damage, but also pure economic loss and non-material harm (such as 
psychological harm and damage caused by discrimination) to the extent that these types of harm 
are compensable under existing rules. PO1 would also reduce victims’ costs linked to the burden 
of  proof  (e.g.  for  expert  analysis),  by  ensuring  access  to  relevant  information  and  ensuring  that 
victims do not have to prove how or why an AI-system arrived at a certain output. 

Quantification: Based on conservative assumptions regarding the costs to be advanced by victims 
to meet the burden of proof under existing liability rules, and the effect of PO1 on this cost factor, 
it is estimated that this PO would reduce this cost by ca. EUR 2000 on average per case in which 

Europe, 

Insurance 

172  See  Annexes  3,  4  and  10  (A.2.1.3.(e)  and  B.2.1.(a))  for  further  detailed  information  on  the  quantification 
challenges  and  steps  undertaken  to  remedy  the  scarcity  of  quantified  data,  as  well  as  the  methodology  and 
assumptions underlying the quantified estimates. 
173 
Insurance 
European 
https://www.insuranceeurope.eu/publications/689/european-insurance-in-figures-2019-data/download/EIF+2021.pdf. 
174 See e.g. Insurance Europe’s reply to the public consultation: “…insurance can lessen the negative consequences of 
accidents  involving  AI  by  ensuring  that  the  victim  receives  compensation.  There  are  already  many  such  insurance 
solutions available in the European insurance market. Protection against material damage incurred by AI generally 
falls  within  the  remit  of  general  liability  insurance  policies,  which  are  sold  on  an  all-risks  basis.”  Similarly  the 
German Insurance Association (GDV) submitted that “the voluntary insurance market works well in providing actors 
of  all  kinds  with  appropriate  liability  insurance.”  Likewise,  a  leading  insurance  company  explained  in  a  bilateral 
exchange  with  the  Commission  services  that  AI  liability  will  be  likely  be  incorporated  as  additional  feature  into 
existing insurance policies, and that the increasing integration of AI in business activities is not expected to lead to an 
increase of premiums on a general level. 

Figures, 

p. 48, 

2019 

data, 

in 

47 

 
 
                                                           
the targeted alleviations of the burden of proof apply.175  

This  quantification  builds  on  the  following  estimates  and  assumptions  (explained  in  detail  in 
Annex 10):  

- Based on expert estimates provided in the framework of the economic study176, the average costs 
to  be  advanced  by  victims  for  technical  expertise  in  cases  where  AI  was  involved  in  causing 
damage were assumed to be EUR 4149 higher than the same average in cases not involving AI. 
These costs for technical expertise are used as a proxy for the costs linked to the burden of proof. 

- It was further assumed that the targeted alleviations of the burden of proof envisaged under PO1 
would reduce the costs to be advanced by victims due to the AI-specific difficulty of meeting the 
burden of proof by at least 50 % (i.e. ca. EUR 2000)  

It is important to stress that the estimated cost reduction for victims should not be misconstrued as 
a quantification of the AI-specific difficulty of meeting the burden of proof, because it does not 
take  into  account  cases  in  which  liability  claims  would  not  be  pursued  at  all  based  on  current 
liability rules (because the victim either cannot identify the liable party or considers the prospect 
of a successful claim insufficient to justify legal action). The preferred PO will help victims also in 
the latter cases, by overcoming the compensation gaps induced by the specific characteristics of 
AI. 

- Consumers: A faster roll-out of AI-technologies under PO1 would benefit consumers, e.g. in the 
form  of  faster  and  more  personalised  services,  innovative  and  performant  products  as  well  as 
advances in the fields of health, safety, security, mobility, sustainability, circular economy, media, 
etc.  Given  the  overall  positive  economic  impacts  also  on  businesses,  it  is  not  expected  that  the 
envisaged measures would lead to costs being passed on through increased consumer prices.  

-  Insurance  companies:  PO1  may  marginally  increase  the  take-up  of  insurance  by  potentially 
liable  parties  –  provided  the  insurance  coverage  is  not  already  included  in  existing  all-risks-
policies177. Increased legal certainty and reduced fragmentation create more favourable conditions 
for  offering  insurance  coverage  and  awareness  of  liability  risks  may  slightly  rise  due  to  this 
initiative.  An  increased  coverage  rate  would  benefit  victims  of  damage  as  insurance  claims 
provide an easier path to compensation and relieve victims of the liable party’s insolvency risk. 

-  Indirect  economic  impacts  and  impacts  on  the  competitiveness  of  the  internal  market: 
Through the avoidance of liability gaps, PO1 would contribute to an efficient cost allocation. Its 
combined  impacts  are  expected  to  have  a  positive  effect  on  cross-border  trade  in  AI-enabled 
products and services and the development of the European AI-sector as a whole.178 The economic 
study  estimated  that  a  combination  of  alleviations  of  the  burden  of  proof  (as  per  PO1)  and 
measures to harmonise strict liability for certain AI-enabled products and services (cf. PO2 and 3) 
would increase the cross-border trade in the AI-enabled goods and services falling under the six 
use-cases  analysed  in  depth  for  that  study  by  about  5 %.  While  PO1  does  not  include  all  of  the 
assumptions made for that estimation, it is nevertheless relevant because the decisive drivers of the 
expected  economic  benefits  –  increased  legal  certainty,  reduced  fragmentation  and  increased 
consumer trust – are likely to materialise under PO1.179 

175  For  detailed  explanations  regarding  the  methodology  and  assumptions  underlying  this  estimate,  see  Annex  10, 
A.2.1.3.(d) and B.2.1.(b). 
176 For detailed explanations regarding these cost estimates, see Annex 10 and the Economic Study, pp. 48 et seq. 
177 AI liability risks are likely to be covered by existing all-risk policies in many cases, cf. fn. 157. 
178 Economic Study, p. 196 and 197. 
179 Ibid., pp. 196-198. 

48 

 
 
                                                           
Quantification:  

(1)  Based  on  the  overall  value  of  the  EU  AI  market  affected  by  the  liability-related  problems 
addressed by this initiative, and reasoned assumptions regarding the incremental impact of PO1 on 
this market, it is estimated that the increased legal certainty, reduced fragmentation and increased 
level of consumer uptake will generate an additional market value between ca. EUR 500mln and 
ca. EUR 1.1bln.180 These values are obtained by multiplying the estimated shares of the AI market 
affected by legal uncertainty and fragmentation regarding civil liability in 2025 under the baseline 
scenario (low and high scenarios assumed by the economic study ) with the estimated impact of 
the policy options (+5%). This percentage was determined conservatively, taking into account the 
estimated  impact  generated  by  a  combination  of  measures  to  ease  the  burden  of  proof  with  a 
harmonisation of strict liability limited to certain AI applications181.    

(2)  The  Joint  Research  Centre  has  provided  a  complementary  micro-economic  quantification  of 
the impacts of PO1, based on the use-case of robotic vacuum cleaners. This analysis reaches the 
conclusion that PO1 would generate an increase in consumer welfare of EUR 11.5-19.12mln and 
in total welfare182 of EUR 30.11-53.74mln for this product category alone in the EU-27.183  

(3) Previous attempts by the EP to quantify the benefits of a clear and coherent EU civil liability 
regime  for  AI  remained  inconclusive.184  Nevertheless,  its  preliminary  analysis  suggests  that  the 
added  value  of  EU  action  on  liability  could  generate  EUR 54.8  billion  by  2030  for  the  EU 
economy in terms of acceleration of the level of research and development in AI, and in the range 
of EUR 498.8 billion if other impacts, including reductions of accidents, health and environmental 
impacts and user impacts are also taken into consideration.185 As these numbers were not linked to 
a  clearly  defined  set  of  PO,  they  cannot  be  readily  applied  to  the  PO  described  in  this  impact 
assessment.  However,  they  provide  a  reasoned  view  on  the  order  of  magnitude  of  potential 
economic benefits linked to a clear and consistent civil liability framework for AI. 

- Only small incremental impacts on enforcement, adjudication and litigation costs, borne by 
MS  and  parties  to  the  proceedings  respectively,  are  expected  under  PO1.  The  envisaged 
alleviations of the burden of proof are not likely to entail a substantial increase in the number of 
civil  actions,  as  they  are  designed  to  apply  only  in  the  confined  cases  where  the  specific 
characteristics  of  an  AI  system  make  it  unduly  difficult  to  meet  the  default  burden  of  proof. 
Moreover,  the  burden  of  proof  will  be  distributed  more  efficiently  overall,  as  potentially  liable 
parties  must  by  definition  be  capable  of  influencing  the  operation  of  AI-systems.  They  are 
therefore typically in a position to more easily discharge the burden of proof, with respect to how 
or why such systems arrived at a certain harmful output. This has a cost-cutting effect on overall 
enforcement, adjudication and litigation costs.  

Quantification: Where the measures envisaged under PO1 apply, they could lead to an increase 
between  ca.  EUR 200  and  ca.  EUR 1600  of  the  cost  to  be  advanced  by  potentially  liable 
businesses  to  defend  against  claims  for  the  compensation  of  damage  caused  by  AI.186  This 
estimate takes as a starting point the average costs that victims would no longer have to advance 

180 For detailed explanations on the methodology and assumptions underlying this estimate, see Annex 10, A.2.1.3.(e). 
181 Economic Study, pp. 195 et seq. and the explanations on the evolution of policy options in section 6.4. 
182  Sum  of  vacuum  cleaners  consumer  surplus,  vacuum  cleaner  sellers‘  profits  and  third  party  victims  receiving  a 
compensation). 
183 See Annex 11 for details on the methodology and results of this quantification effort.  
184 EPRS, European added value assessment, September 2020. 
185 Ibid.  
186 For detailed explanations on the methodology and assumptions underlying this estimate, see Annex 10, A.2.1.3.(d) 
and B.2.1.(g). 

49 

 
 
                                                           
due  to  PO1  (EUR 2000,  see  above).  These  are  litigation  costs  that  arise  in  the  framework  of 
pending  litigation,  if  the  defendant  needs  to  provide  or  procure  technical  expertise  in  order  to 
defend against claims for damages. 

Further reasoned assumptions were made regarding the extent to which each policy option would 
lead to a transfer of the cost linked to the burden of proof to the defendant (i.e. the allegedly liable 
party).It is assumed that a fraction ranging from 10 % to 80 % of the amount saved by victims due 
to the alleviations of the burden of proof under PO1 will have to be advanced by potentially liable 
businesses. This broad assumed range is based on the following considerations: 

- In certain cases, for instance where the defendant is a provider of a high-risk AI systems falling 
under the AI Act, they will have optimal information on and understanding of the functioning of 
the  relevant  AI  system.  They  will  thus  not  need  to  procure  any  external  technical  expertise  to 
discharge the burden of proof. 

- On the other end of the spectrum of conceivable cases, the defendant may not have any advanced 
understanding of the functioning of the relevant AI system, nor easy access to detailed information 
on  that  system.  This  may  for  instance  be  the  case  where  the  defendant  is  an  SME  using  an  AI 
system not falling under the transparency and documentation requirements of the AI Act. As this 
type of defendant is nevertheless in a better position than the victim for establishing the trigger of 
the damage, it is assumed that even in such cases the amount saved by victims would not be re-
distributed to the defendant in its entirety (but only to a large extent, e.g. by 80%). 

The estimates  and  assumptions underpinning this  quantification are based on  expert assessment, 
stakeholder feedback and legal-economic analysis. They are hence necessarily subject to a degree 
of uncertainty which is reflected by the broad range of estimates. 

(c)   Coherence – in particular with the PLD review 

As  explained  under  5.2.(a),  PO1  would  be  coherent  with  the  –  not  AI-specific  –  measures 
envisaged  in  the  framework  of  the  PLD  review.  These  instruments  are  complementary  as  they 
address  challenges  posed  by  emerging  digital  technologies  with  respect  to  claims  based  on 
different grounds, directed against different liable persons and covering compensation for different 
victims and types of harm. The respective instruments use a consistent approach and similar tools 
(access  to  information,  alleviations  of  the  burden  of  proof)  to  ensure  in  their  combination  that 
products or services using AI or other digital technologies do not make it more difficult under any 
of  the  existing  pillars  of  liability  to  get  compensation  compared  to  traditional  products.  The 
envisaged  measures  will  together  contribute  to  creating  a  more  consistent  and  adequate  liability 
framework, without upsetting the balance established by the existing rules. 

PO1 would also be coherent with the proposals already adopted as part of the follow-up to the AI 
White Paper, in particular the AI Act. It would namely take over definitions of key concepts such 
as  ‘AI-system’,  ‘provider’  and  ‘user’,  although  additional  criteria  would  be  necessary  to  ensure 
that the envisaged alleviations of the burden of proof apply only where the specific characteristics 
of AI effectively challenge existing liability rules. The provisions on disclosure of information and 
presumptions of causality would build specifically on the requirements of the AI Act. PO1 would 
thus complement this act providing an additional incentive for ensuring the safety of AI-enabled 
products and services as well as the respect of fundamental rights. 

Finally, MS could fit the envisaged alleviations of the burden of proof into their national liability 
regimes  without  disrupting  their  respective  legal  traditions.  This  PO  allows  for  sufficient 
flexibility and is based on tools that are already well-known in MS’ civil liability systems. 

(d)   Proportionality 

50 

 
 
PO1 is limited to the measures strictly necessary to address the AI-specific problems identified. In 
particular, it would not touch upon the substantive conditions of liability like fault or causality, but 
focus on targeted proof-related measures ensuring that victims have the same level of protection as 
in cases not involving AI.  

(e)   Considerations regarding the choice of instrument (binding v. non-binding) 

A directive would be the most suitable instrument, as it could provide the desired harmonisation 
effect  and  legal  certainty,  while  also  being  the  adequate  instrument  to  enable  MS  in  a  flexible 
manner to fit the harmonised measures without friction into their national liability regimes.  

A  mandatory  instrument  would  prevent  protection  gaps  stemming  from  partial  or  no 
implementation.  It  would  thus  likely  ensure  higher  and  more  harmonised  protection  of  victims 
compared  to  the  baseline  scenario.187  While  a  non-binding  instrument  presents  a  less  intrusive 
approach,  it  is  unlikely  to  address  the  identified  problems  in  an  effective  manner.  The 
implementation  rate  of  non-binding  instruments  is  difficult  to  predict  and  there  is  no  sufficient 
indication  that  the  persuasive  effect  of  a  recommendation  would  be  strong  enough  to  produce 
consistent adaptations of national laws. This effect is even more unlikely for measures concerning 
private law, of which extra-contractual liability rules form part. This area is characterised by long-
standing legal traditions, which makes MS reluctant to pursue harmonised reform unless driven by 
the  clear  prospect  of  internal  market  benefits  under  a  binding  EU  instrument188  or  the  need  to 
adapt to new technologies in the digital economy. Moreover, the significant existing divergences 
between  MS’  liability  frameworks  (see  2.4.),  are  another  reason  why  a  recommendation  is 
unlikely to be implemented in a consistent manner.  

Divergences  and  reduced  legal  certainty  in  cross-border  cases  would  thus  persist  even  to  the  – 
likely  very  limited  –  extent  that  MS  choose  to  implement  a  recommendation.  A  selective  and 
inconsistent  implementation  would  solve  neither  the  problem  of  legal  uncertainty  nor  that  of 
impending fragmentation and it would not ensure consistent victim protection throughout the EU. 
The  economic  study  confirmed  that  a  non-binding  initiative  would  not  address  the  identified 
internal market obstacles effectively, as the underpinning problems will likely be perpetuated to a 
substantial extent.189 It concluded that a non-binding instrument would not achieve any increase in 
cross-border trade.190 Due to this likely lack of effectiveness, choosing a non-binding instrument 
would not  be in  line with the principle of proportionality, which  requires the choice of the least 
burdensome PO, which is still suitable to achieve the policy purpose.  

While  the  above  considerations  clearly  militate  in  favour  of  implementing  PO1  by  way  of  a 
binding legal instrument, that instrument has to allow Member States to integrate the harmonised 
measure into their traditional legal systems without creating frictions. A directive would therefore 
be  the  most  appropriate  tool  to  achieve  the  policy  objectives  while  maintaining  the  necessary 
flexibility  to  preserve  the  various  established  national  approaches  and  legal  traditions  in  the 
politically sensitive field of civil law. 

6.2. PO2:  PO1  +  strict  liability  for  AI  use-cases  with  a  specific  risk-profile,  possibly 

coupled with mandatory insurance 

PO2 differs from PO1 as regards the strict liability regime applicable to users of AI technologies 
with  a  special  risk-profile,  possibly  coupled  with  a  mandatory  insurance  regime.  The  following 

187 Economic Study, p. 133 and 134. 
188  Cf.  e.g.  Caruso,  The  Missing  View  of  the  Cathedral:  The  Private  Law  Paradigm  of  European  Legal  Integration, 
European Law Journal, Vol. 3, No. 1 March 1997, p. 3. 
189 Economic Study, pp. 131, 132, 158-160 and 195. 
190 Ibid., p. 196. 

51 

 
 
                                                           
impact analysis therefore focuses on these elements. 

(a)  Effectiveness 

-  Degree  to  which  SO1  and  2191  would  be  achieved:  A  harmonised  strict  liability  regime, 
possibly coupled with mandatory insurance, is a suitable instrument to ensure legal certainty and 
prevent  fragmentation,  as  MS  would  have  to  implement  a  consistent  minimum  strict  liability 
standard with respect to the covered activities. Provided that the AI-enabled technologies covered 
by that regime can be specified with precision, the companies operating/using those technologies 
could have an even clearer and consistent basis for assessing their liability risk. However, the time 
horizon of the roll-out of technologies with a relevant risk profile and degree of autonomy as well 
as  their  features  which  allow  to  define  them  in  a  legal  act  with  sufficient  certainty  are  not  yet 
known. It would therefore, at this point in time, be to a certain degree  difficult to define the risk 
profile  of  those  technologies  with  the  desirable  degree  of  precision,  and  to  specify  them  in  a 
legislative instrument in a way that ensures the legal certainty necessary for the definition of the 
scope of the harmonised strict liability regime. This consideration would apply also to a possible 
mandatory insurance regime covering strict liability. The obligation to ensure insurance coverage 
is a market entry requirement. This means that it is crucial to enable market participants to assess 
with a high degree of certainty whether they fall under this requirement or not. 

Moreover,  because  of  the  uncertainty  related  to  the  risk  profiles  explained  above,  it  cannot  be 
easily  assessed  at  this  stage  if  having  an  alleviation  of  the  burden  of  proof  (PO1)  would  not  be 
sufficient to address the identified problems. This means that the effectiveness of the strict liability 
element of PO2 is also reduced. 

For these reasons, an instrument including strict liability for the use/operation of certain types of 
AI-enabled technologies would likely be less effective than it should be, at this point in time, in 
achieving specific objectives 1 and 2 than PO1. 

- Degree to which SO3 would be achieved: A harmonised strict liability and possible mandatory 
insurance  regime,  as  the  distinguishing  features  of  PO2,  could  prevent  a  lack  of  compensation 
even  more  effectively  than  the  alleviations  of  the  burden  of  proof  common  to  PO1  and  2.  The 
expected effects on societal trust follow a similar pattern as under PO1, as strict liability represents 
simply another – potentially even more effective – way of ensuring an effective compensation of 
victims. As with the alleviations of the burden of proof, this is likely to have a positive effect on 
consumers’  perception  of  the  appropriateness  of  liability  rules,  as  well  as  their  trust  in  AI 
applications.192 This in turn is likely to increase their willingness to take up AI-enabled products 
and  services.193  At  the  same  time,  it  would  need  to  be  acknowledged  that  pending  the  market 
maturity  of  relevant  AI-enabled  products  and  services,  the  specific  modalities  of  their  operation 
and the degree of control exercised respectively by the relevant stakeholders over the risks linked 
to the operation are at this point in time not known yet. 

- Indirect social impacts: The ability of strict liability rules to incentivise safety efforts depends 
to  a  large  extent  on  whether  the  strictly  liable  person  has  cost-efficient  means  to  prevent 
damages.194  The  control  criterion  envisaged  for  assigning  strict  liability  to  professional 
users/operators is therefore conducive to the desired incentive effects.  

Moreover, behavioural research has shown that a strict liability framework for AI is more likely to 

191  Given  the  close  links  and  interactions  between  legal  uncertainty  and  legal  fragmentation  (see  2.6.  and  2.7.),  the 
efficiency of PO is assessed together for SO1 and 2. 
192 Behavioural Economics Study, pp. 67 and 68. 
193 Ibid, p. 47. 
194 Economic Study, p. 142. 

52 

 
 
                                                           
be perceived as predictable and transparent than fault-based liability.195 The strict liability element 
is thus likely to increase societal trust in the justice system. 

(b)   Efficiency 

-  Impacts  on  businesses  (in  particular  as  potentially  liable  parties):  Provided  that  the  AI-
enabled technologies covered by the harmonised strict liability can be determined with a sufficient 
degree of precision, this element of PO2 would be suitable to increase legal certainty and reduce 
fragmentation  by  reducing  legal  information/representation,  internal  risk  management,  and  other 
compliance-related costs, and generate additional cross-border revenue.196 Moreover, the expected 
positive impacts of PO2 on consumers’ willingness to take up AI-enabled products  and services 
(see below) would directly or indirectly benefit all companies in the AI value chain. Start-ups and 
other  SMEs  would  benefit  to  a  higher  degree  from  the  overall  positive  economic  impacts  of 
PO2.197 

In  certain  cases,  beyond  merely  harmonising  existing  strict  liability  rules,  PO2  would  entail  the 
application of strict liability and possibly an insurance obligation to users of AI technologies that 
would  otherwise  be  subject  only  to  fault-based  liability  under  national  law.  In  such  cases,  PO2 
could be a dis-incentivising factor for businesses choosing between AI-enabled technologies and 
functionally equivalent alternatives. However, this effect is likely offset by the cost reduction and 
internal  market  opportunities  generated  through  harmonised  liability  rules.  The  economic  study 
found  that  the  moderate  compliance  costs  linked  to  PO2  would  be  outweighed  by  cost  savings 
thanks to higher legal certainty, saved resources on compliance, and higher revenue enabled by a 
clearer and less fragmented legal framework.198  

Moreover, the role of insurance solutions, as described under PO1, is instrumental in this respect, 
as  it  limits  potentially  liable  parties’  costs  to  the  insurance  premiums,  keeping  market  entry 
barriers  low.199    In  principle,  this  mechanism  holds  true  whether  the  relevant  risk  is  covered  by 
voluntary  (market-driven)  insurance  or  a  mandatory  insurance  regime  (either  harmonised  or 
regulated at national level).200 In the public consultation, insurance stakeholders have pointed to a 
lack of statistical data on accidents and damages, which could initially drive risk margins and thus 
insurance  premiums.  Some  SME  stakeholders  have  also  raised  concerns  about  high  insurance 
premiums due to the difficulties in assessing the covered liability risks. On the other hand, the fact 
the  insurance  industry  is  already  proactively  developing  new  insurance  products  for  AI  risks201 
and  that  this  field  is  considered  as  the  next  big  growth  market202  confirm  the  expectation  that 
coverage  will  be  offered  at  competitive  prices.  An  additional  important  factor  is  the  parallel 
initiative adapting the PLD to the digital age. Based on a (statutory or contractual) subrogation of 
the victim’s claim to the insurer, it will facilitate it for insurers to take recourse against producers,  
where  defective  AI  software  caused  or  contributed  to  the  insured  damage.  This  would  allow 
insurers  having  compensated  the  victim’s  damage  to  claim  the  compensation  back  from  the 
insurer,  thus    also  contributing  to  keeping  premiums  low.203  In  any  event,  the  Commission 

195 Behavioural Economics Study, op. cit., executive summary, p. iii. 
196 Cf. Economic Study, pp. 113 et seq., 161 and 163, and Executive Summary, p. ii. 
197 Ibid, pp. 122 and 161. 
198 Ibid, p. 164. 
199 Ibid., p. 163.  
200 Ibid., 141. 
201 E.g. ‚Ensure AI‘ by MunichRe, enabling AI providers to guarantee the performance of their AI systems. Cf. also 
La Playa, offering brokerage of specialist AI insurance. 
202 Cf. e.g. Harvard Business Review, The Case for AI Insurance, April 2020. 
203 Cf. Economic Study, pp. 153. 

53 

 
 
                                                           
services  will  respond  to  the  Parliament’s  call  to  work  closely  with  the  insurance  market.204  The 
Commission  will  notably  facilitate  a  dialogue  between  the  insurance  industry  and  companies 
active in the AI market (in particular SMEs). 

Quantification:  Where  strict  liability  applies,  the  effect  of  redistributing  the  burden  of  damage 
from the victim to the responsible party is stronger compared to mere alleviations of the burden of 
proof  under  fault-based  liability  rules.  This  effect  is  approximated  by  the  expected  impact  on 
insurance  premiums.  If  PO2  would  be  limited  to  a  combination  of  alleviations  of  the  burden  of 
proof under fault-based liability rules (=PO1) with a limited strict liability regime, it is estimated 
to entail an increase of the overall absolute amount of annual liability insurance premiums in the 
EU  by  25 %,  that  is  to  say  by  EUR 8.89mln–26.82mln.  In  this  scenario,  the  voluntary  or 
mandatory  nature  of  insurance  regimes  would  be  the  same  as  under  the  baseline  scenario, 
mandatory insurance regimes existing under EU or national law would continue to apply, but PO2 
would not introduce any additional mandatory insurance requirements.  If PO2 would include, in 
addition,  an  insurance  obligation  covering  strict  liability,  the  increase  of  the  overall  amount  of 
annual  liability  insurance  premiums  paid  in  the  EU  is  estimated  at  35 %,  i.e.  ranging  between 
EUR 12.44mln  and  EUR 37.57mln.205  These  assumptions  are  based  on 
the  following 
considerations: 

-  Where  strict  liability  applies,  the  effect  of  re-distributing  the  burden  of  bearing  the  damage  is 
significantly stronger, as the liable party cannot avoid liability even if not at fault but this regime 
would apply only with respect to a small number of AI-enabled products and AI-enabled services, 
and would therefore change the estimated impact on insurance premiums only to a limited extent.  

-  Coupling  strict  liability  with  an  insurance  obligation  would  entail  an  incrementally  bigger 
increase of insurance premiums, as PO2 would preclude to some extent the possibility for insurers 
to manage risks through contractual exclusions and limitations of coverage. Mandatory insurance 
in particular can have a premium-driving effect for a transitional period marked by scarce actuarial 
data,  but  is  expected  to  dissipate  gradually  as  data  becomes  available.  Even  during  the  initial 
stage,  the  harmonised  safety  framework  provided  by  the  AI  Act  and  sectoral  safety  legislation 
proposed at EU level will help insurers to assess the risks linked to the operation of relevant AI 
systems.  In  addition,  the  proposed  Data  Act  will  promote  access  to  data  generated  by  a  user’s 
product and insurance provision will be facilitated by such data. 

-  Victims  of  damage  caused  by  AI  applications  with  a  special  risk profile  (natural  persons 
and  businesses):  A  harmonised  strict  liability  regime  would  facilitate  victims’  access  to 
compensation  to  an  even  greater  extent  than  alleviations  of  the  burden  of  proof.  A  possible 
mandatory  insurance  regime  would  relieve  victims’  of  the  liable  parties’  insolvency  risk  and 
provide them with an even cheaper, faster and easier path to compensation.206  

Quantification: Based on conservative assumptions regarding the costs to be advanced by victims 
to meet the burden of proof under existing liability rules, and the effect of PO2 on this cost factor, 
it  is  estimated  that  this  PO  would  reduce  this  litigation  cost  by  at  least  60 %,  that  is  to  say  ca. 

204 EP, Resolution 2020/2014(INL) on a civil liability regime for artificial intelligence, recital 22. 
205 For details regarding methodology and assumptions underlying these estimates, see Annex 10, A.2.1.3.(e) and 
B.2.2.(a). 
206 This liability risk can be substantial, in particular in the case of robotics start-ups. Cf.  e.g. Schmelzer,  Why  Are 
Robotics Companies Dying?, 2018; Fresh Consulting, Why Robotics Companies Fail, 2021. 

54 

 
 
                                                           
EUR 2 500,  on  average  per  case  in  which  the  envisaged  measures  apply.207  This  assumption  is 
based on the consideration that victims would not bear any significant costs linked to the burden 
of proof in cases where they can invoke strict liability. The added benefit for victims compared to 
PO1 concerns however only the relatively limited number of cases involving certain specific AI-
enabled technologies, where strict liability would apply.  

-  Consumers:  The  expected  benefits  in  terms  of  a  faster  roll-out  of  AI-enabled  products  and 
services  are  likely  to  be  similar  under  PO2  as  under  PO1,  because  their  common  element  – 
alleviations of the burden of proof under fault-based liability rules – applies to the major part of 
AI-enabled  products  and  services  affected  by  the  initiative.  The  strict  liability  and  mandatory 
insurance  elements  of  PO2  are  much  more  limited  in  scope.  In  respect  of  these  elements,  the 
difficulty  linked  to  specifying  already  at  the  present  point  in  time  the  AI-enabled  technologies 
with  a  ‘strict  liability  profile’  could  lead  to  a  certain  degree  of  legal  uncertainty,  which  could 
potentially dis-incentivise AI rollout in certain cases. 

- Insurance companies:  The harmonisation  of strict liability under  PO2 is  likely to  entail  some 
additional demand for insurance, in particular when coupled with a compulsory insurance regime, 
which generates new market opportunities for insurance companies.  

-  Indirect  economic  impacts  and  impacts  on  the  competitiveness  of  the  internal  market: 
While the envisaged strict liability regime may be the most certain and easiest way to ensure that 
the victim does not bear the cost of the damage, it may not in all cases lead to the cost allocation 
on  the  party  which  was  at  the  origin  of  the  damage.  However  if  it  is  coupled  with  mandatory 
insurance, such a cost-allocation will be achieved through subrogated recourse claims.208 In case 
the insurance company of the strictly liable person compensates the victim, a damage claim of the 
victim based on fault or product liability would be re-assigned to the insurance company. On the 
basis of this claim, the insurance company could have recourse against the person at the origin of 
the  damage,  for  instance  against  the  producer  if  the  product  was  defective.  Thereby  the  most 
efficient cost allocation would be achieved. This scenario could be extended along the value chain. 
For example, if the producer has a liability claim against the developer, the insurance company of 
the producer would have a recourse possibility on the basis of this contractually subrogated claim 
against  the  developer.  In  the  end,  the  insurance  of  the  developer  would  pay.  At  the  same  time, 
insurance  coverage  of  liable  persons  would  limit  the  economic  costs  to  the  annual  insurance 
premium  and  keep  the  market  entries  for  AI  producers  and  operators  low,  while  it  would  be 
ensured at the same time that the victim’s harm would be compensated in a smooth way. 

-  For  similar  reasons  as  under  PO1,  only  small  incremental  impacts  on  enforcement, 
adjudication and litigation costs are expected under PO2.  

Quantification: Where the measures envisaged under PO2 apply, they could lead to an increase 
between ca. EUR 100 and ca. EUR 1500 of the litigation costs to be advanced by potentially liable 

207 For further detailed explanations regarding the methodology and assumptions underlying this estimate, see Annex 
10, A.2.1.3.(d) and B.2.2.(b). This estimate should not be misconstrued as a quantification of the AI-specific difficulty 
of  meeting  the  burden  of  proof,  because  it  does  not  take  into  account  cases  in  which  liability  claims  would  not  be 
pursued at all based on current liability rules (because the victim either cannot identify the liable party or considers the 
prospect of a successful claim insufficient to justify legal action). The preferred policy option will help victims also in 
the latter cases, by overcoming the compensation gaps induced by the specific characteristics of AI. 
208 Economic Study, pp. 142, 154 and 156. 

55 

 
 
                                                           
businesses  to  defend  against  claims  for  the  compensation  of  damage  caused  by  AI.209  This 
quantified  estimate  is  based  on  the  assumption  that  a  fraction  ranging  from  5 %  to  60 %  of  the 
amount saved by victims due to the eased burden of proof under PO2 will be re-distributed to the 
defendants. The following factors were taken into consideration in this respect: 

-  the  cost  savings  for  victims  due  to  the  fact  that  they  do  not  have  to  prove  fault  would  not  be 
shifted to the defendant because the latter cannot avoid liability by establishing, possibly with the 
help of costly technical expertise, that the damage was not caused by their fault. 

- By dispensing with the need to establish fault and a causal link between fault and damage, strict 
liability  considerably  reduces  the  overall  evidentiary  complexity  and  need  for  costly  technical 
expertise. 

To  the  extent  that  risks  within  the  scope  of  the  strict  liability  element  of  PO2  are  not  already 
covered  by  a  national  strict  liability  regime  under  the  baseline,  the  easier  and  more  predictable 
path  to  compensation  afforded  by  this  measure  may  lead  to  a  slight  increase  in  the  number  of 
claims made. Any such possible increase is likely to be marginal. As strict liability would apply 
only in cases involving significant risks to important legal interests (life, health, property), victims 
would in such cases be likely to seek compensation also under the baseline, despite the challenges 
and costs linked to fault-based claims.210 

(c)   Coherence – in particular with the PLD review 

PO2  would  complement  the  measures  envisaged  in  the  PLD  review.  In  particular,  the  liable 
persons  defined  by  law  are  different  and  their  liability  is  based  on  a  different  relationship  they 
have to the risk. The law would attribute liability to the operator/user under the harmonised strict 
liability regime because the operation of the AI system would realise the risk for the victim. At the 
same time, the operator draws a benefit from the operation. The law also attributes liability to the 
producer under the (revised) PLD, despite the fact that the producer is a step more removed from 
the contact with the victim. The producer is made liable because of producing a defective product 
(including the AI that is necessary for it to work).  

In cases where the producer is nevertheless  at the same time also the user/operator, the fact that 
they would be subject to the revised PLD and the harmonised strict liability regime for AI would 
not  lead  to  inconsistent  results  because  these  liabilities  would  intervene  in  different  capacities. 
Such  scenarios  already  exist  in  the  national  laws  where  national  strict  liability  schemes  and 
product liability apply in parallel. 

As regards national law, the coherence of PO2 with the existing legal framework could be 
questioned  in  cases  where  the  use  of  AI-enabled  technologies  would  be  subject  to  the 
harmonised  strict  liability  regime  while  the  use  of  functionally  equivalent  non-AI 
technologies is not subject to strict liability under national law.  (d)   Proportionality 

At  this  point  in  time,  it  is  not  clear  whether,  despite  the  alleviations  of  the  burden  of  proof 
envisaged under PO1, , there is  a liability gap which would need to be filled with a  harmonised 
strict liability regime for AI operators. It is possible that the less interfering measures under PO1 
will  be  sufficient  to  achieve  the  policy  objectives.  Moreover,  at  this  point  in  time  there  is  still 
some uncertainty regarding the precise characteristics, risk-profile  and conditions  of deployment 
of AI-enabled technologies with a potential ‘strict liability profile’. Therefore the proportionality 

209  For  details  regarding  methodology  and  assumptions  underlying  these  estimates,  see  Annex  10,  A.2.1.3.(d)  and 
B.2.2.(g). 
210 Economic Study, p. 61. 

56 

 
 
                                                           
of PO2 cannot be affirmed with certainty at this point in time. 

(e) Considerations regarding the choice of instrument (binding v. non-binding) 

The considerations made in the context of  PO1 apply to an even larger extent: As a harmonised 
strict  liability  and  mandatory  insurance  regime  involves  more  far-reaching  adaptations  of  MS’ 
liability  rules,  a  non-binding  instrument  is  not  suitable  to  ensure  a  consistent  implementation  of 
PO2.  For  the  same  reasons  as  set  out  under  PO1,  a  Directive  seems  to  be  the  most  appropriate 
legislative tool.  

6.3. PO3: PO1 + targeted review regarding the strict liability and mandatory insurance 

elements of PO2 (staged approach)  

(a) Effectiveness 

During the first stage, PO3 would be equally effective to PO1. The second stage, i.e. the targeted 
review mechanism, would systematically re-assess the need for the strict liability and mandatory 
insurance  elements  of  PO2.  PO3  would  ensure  that  the  necessary  information  for  this  re-
assessment is available. The review would assess if the technological and market developments as 
well  as  empirical  evidence  on  AI  liability  cases  confirm  the  need  for  such  measures.  This 
mechanism does not pre-empt the necessary future policy decision on the harmonisation of strict 
liability and mandatory insurance. But if there were a policy decision to  that effect, the targeted 
review mechanism would have prepared the groundwork for realising the potentially even greater 
effectiveness  described  under  PO2.  The  second  stage  would  also  gain  more  certainty  about  the 
technological and regulatory context defining the risk-profile and conditions of deployment of the 
AI-enabled technologies with a potential strict liability profile. This would thus allow to specify 
the material and personal scope of a possible strict liability regime with greater precision. It would 
therefore be conducive to ensuring legal certainty and uniform implementation (SO1 and 2).  

(b) Efficiency 

During the 1st stage, PO3 would have the same impacts as PO1. As MS anyway have to report on 
the implementation of the initiative in line with better regulation requirements on evaluation and 
monitoring, targeted reporting requirements supporting the review  mechanism under PO3 would 
not entail a significant burden for them.  

Quantification:  The  decision  on  a  possible  harmonisation  of  strict  liability  and  mandatory 
insurance for certain AI-enabled products and services with a specific risk profile would be taken 
only  at  the  stage  of  the  targeted  review.  Thus,  the  same  macro-economic  benefits  (added  AI 
market value in the EU of ca. EUR 500mln to ca. EUR 1.1bln) and the same incremental increase 
of  insurance  premiums  (an  additional  EUR  5.35mln-EUR  16.1mln  liability  insurance  premiums 
paid  in  the EU per  year) are assumed as for PO1.  Likewise, the same  litigation cost  savings for 
victims linked to the alleviated burden of proof (ca. EUR 2 000 per case on average) and the same 
incremental  cost increase linked to that burden for potentially liable businesses (ca. EUR 200 to 
EUR 1 600 per case) are expected. 

The  potential  benefits  of  a  harmonised  strict  liability  regime  would  materialise  later  than  under 
PO2.  If  there  is  a  liability  gap  which  would  have  needed  to  be  filled  with  a  harmonised  strict 
liability  regime for AI  operators, the disadvantages of such liability  gap  would have their  effect 
during a length of time corresponding to the period of data collection and assessment of the need 
for  strict  liability.  However,  the  following  factors  will  improve  the  efficiency  of  the  measures 
potentially taken at the second stage:  

-  By ensuring that the assessment of a possible strict liability regime for the use of AI-enabled 

57 

 
 
technologies can rely on a more developed factual basis, the staged approach minimises the risk 
of creating an uneven playing field for AI-enabled technologies. 

-  The  staged  approach  allows  the  development  of  tailored  market-driven  insurance  solutions, 
which can be taken into account at the second stage when assessing the need for and effects of 
a mandatory insurance regime. Moreover, AI-technologies potentially subject to such a regime 
will  be  rolled  out  over  the  coming  years  and  statistical  accident  data  will  be  accumulated. 
Therefore  the  lack  of  actuarial  data  as  main  potential  cost-driver  of  AI-specific  liability 
insurance will have become considerably less relevant by the time of the targeted review under 
PO3.  

-  As relevant safety standards for the AI-enabled technologies will be available by the time of the 
targeted review, the conditions for assessing liability risks will have improved for both insurers 
and liable users/operators. 

(c)   Coherence 

PO3 would insert itself without friction into the existing liability system and be consistent with the 
other strands of the Commission’s AI policy. The staged approach would allow the Commission to 
take  stock  of  the  practical  effect  of  the  planned  adaptations  to  the  PLD,  in  particular  as  regards 
providers  of  safety-relevant  AI-systems.  The  targeted  review  mechanism  would  allow  the 
Commission also to ensure coherence with future AI-related policy measures beyond the proposed 
AI  Act.  This  may  for  instance  concern  future  safety  rules  tailored  to  AI-systems  with  a  ‘strict 
liability profile’ like autonomous vehicles and highly autonomous AI-enabled drones. 

(d)   Proportionality 

PO3 would ensure that future technological, regulatory and jurisprudential developments will be 
systematically taken into account to verify the need to harmonise strict liability for certain uses of 
AI. The staged approached is thus strictly based on the principle of proportionality. 

(e)   Considerations regarding the choice of instrument (binding v. non-binding) 

6.4.For  the  same  reasons  as  set  out  under  PO1  and  PO2,  a  binding  legal  instrument  –  a 
Directive  –  would  be  the  most  appropriate  tool  to  implement  PO3.  Evolution  of  policy 
options 

It is important to note that the policy options  retained for detailed assessment have evolved 
due  to  the  conclusions  of  the  economic  study,  as  well  as  due  to  the  results  of  the  public 
consultation and following discussions with stakeholders.  

The  economic  study  assumed  two  different  combinations  of  alleviations  of  the  burden  of 
proof under fault-based liability rules with a harmonised strict liability regime  
by 
consequence, did not explicitly assess the economic impacts of alleviations of the burden of 
proof, as per PO1, taken in isolation.  

and, 

However,  it  proved  important  to  consider  alleviations  of  the  burden  of  proof  on  their  own 
(PO1), as an alternative to introducing these alleviations together with strict liability (PO2). 
We  consider  that  the  assessments  made  by  the  economic  study  are  still  largely  relevant 
because: 

-  According  to  the  study,  the  economic  benefits  of  an  EU  initiative  on  AI  liability  are 
primarily  attached  to  the  expected  gains  of  legal  certainty  and  reduced  legal  fragmentation. 
These effects are expected to materialise also with respect to the alleviations of the burden of 

58 

 
 
proof  taken  in  isolation  (PO1  and  first  stage  of  PO3),  which  will  clarify  in  a  harmonised 
manner how the burden of proof is to be handled in cases involving AI. 

- The measures that PO1 shares with two of the policy options assumed for the purposes of 
the economic study are relevant for the major share of AI-enabled products and services, and 
thus decisive for the economic impacts on most stakeholders. This is because only a small set 
of  AI-enabled  technologies  would  have  a  risk  profile  warranting  the  application  of  strict 
liability.   

6.5.UN Sustainable Development Goals and indirect environmental impacts 

The policy options are expected to contribute to the rollout of AI and thus to achieving the related 
SDGs  and  targets211.  They  would  also  impact  positively  on  them  by  contributing  to  the 
enforcement  of  the  AI  Act,  because  effective  legislation  on  transparency,  accountability  and 
fundamental  rights  will  direct  AI’s  potential  among  other  benefits  for  individuals  and  society 
towards achieving the SDGs. 

As regards indirect environmental impacts, all policy options are expected to contribute – albeit to 
a non-quantifiable extent – to the uptake of AI applications that are beneficial for the environment. 
For  instance,  AI  systems  used  in  process  optimisation  make  processes  less  wasteful  (e.g.  by 
reducing  the  amount  of  fertilizers  and  pesticides  needed,  decreasing  the  water  consumption  at 
equal output, etc.). However, there is no sufficient basis for assessing the environmental impact, 
since this basis could only relate to actually marketed AI-enabled products/services. However, this 
initiative  will  improve,  to  a  great  extent,  the  market  roll-out  conditions  of  AI-enabled 
products/services, which are not yet on the market.  

In  addition,  AI  systems  supporting  improved  vehicle  automation  and  traffic  management 
contribute to the shift towards cooperative, connected and automated mobility, which in turn can 
support more efficient and multi-modal transport, lowering energy use and related emissions. On 
the other hand, possible unintended effects cannot be excluded. For instance, an increase in traffic 
may  be  possible  that  could  partly  offset  the  lower  energy  use  and  emissions  achieved  through 
more efficient and multi-modal transport. 

7.  HOW DO THE OPTIONS COMPARE? 

The  PO  were  compared  by  way  of  a  multi-criteria  analysis  (MCA)  taking  into  account  their 
effectiveness, efficiency, coherence and proportionality.212 The following impact matrix compares 
the scores of the options for all of the main impact assessment criteria, using simple aggregation 
and  assuming  an  equal  weight  of  each  individual  criterion.  The  respective  scores  flow  from  the 
expected impacts as presented under heading 6 above. This ranking is linked primarily to the fact 
that  PO2  includes  a  strict  liability  element  which,  for  the  reasons  explained  in  section  6.2.  a), 
lowers the effectiveness of this option at this point in time. Therefore,  PO2 scores slightly lower 
in  terms  of  its  effectiveness  to  achieve  specific  objective  1  (increase  legal  certainty  regarding 
liability for AI), coherence and proportionality, leading to a marginally lower overall score. 

MCA (simply aggregation) 

Score net of the baseline (scale of -5 to +5) 

211 Cf. section 1.1. above and see Annex 3 for further details. 
212 The detailed assessment of POs (cf. Annex 10) has shown that the sub-option of implementing the policy measures 
through a non-binding instrument (recommendation) consistently scored lower across the IA criteria. This sub-option 
was therefore discarded for the purposes of the comparison. The scores in the table relate to the option to implement 
the respective policy measures through a binding legislative instrument (Directive). 

59 

 
 
 
 
                                                           
Criterion 

Option 1 

Option 2 

Option 3 

Effectiveness 
Specific objective 1 
Specific objective 2 
Specific objective 3 
Efficiency 
Coherence 
Proportionality 

 Simple sum 
 Ranking based on simple aggregation 

4 
4 
4 
4 
4 
4 

24 
1 

3 
4 
4 
4 
3 
3 

21 
3 

4 
4 
4 
4 
4 
4 

24 
1 

As three out of these six individual impact assessment criteria would come under the umbrella of 
effectiveness,  effectiveness  is  de  facto  given  more  importance  than  the  other  IA  criteria. 
Therefore, sensitivity analysis was carried out.   The results of the MCA and sensitivity analysis 
are summarised in the following table213 which shows that PO1 and PO3 rank highest. This result 
is consistent with the fact that they involve the same harmonising provisions at the present stage. 
The feature distinguishing these options, the targeted review mechanism, does not lead to different 
impacts of the implemented measures to be implemented at this first stage.  

Policy Option 

PO1 
PO2 
PO3 
Ranking of POs 

Simple sum of 
scores 

24 (1st) 
21 (3rd)  
24 (1st) 
1. PO1 and PO3 

Score based on equal 
weight of impact 
assessment criteria 

Score based on 
individually weighted 
impact assessment criteria 

4 (1st) 
3,42 (3rd)  
4 (1st) 
3. PO2 

4 (1st) 
3,3 (3rd) 
4 (1st) 

8.  PREFERRED OPTION 

8.1. Selection of the preferred option: PO3 (staged approach) 

While liability rules are instrumental in controlling the risk associated with the development and 
use  of  AI,  the  findings  of  the  research  and  the  results  of  the  public  consultation  informing  this 
impact assessment have shown that the specific features of the AI will challenge their application. 
They  also  point  towards  a  staged  approach,  as  the  best  way  to  serve  the  objectives  of  this 
initiative,  accommodate  the concerns of the public and the industry, and  at  the same time allow 
Member States to integrate the adaptations into their existing traditional systems without frictions.  

Specifically,  a  clear  majority  of  business  stakeholders  opposed  strict  liability  in  the  public 
consultation.  In  addition,  the  relevant  insurance  market  is  still  under  development  due  to  the 
currently lower predictability of AI-related risks linked to the lack of sufficient data.  

In  this  respect,  the  multi-criteria  analysis  provides  a  clear  ranking  between  PO1/PO3  and  PO2, 
confirming  that  it  is  preferable  not  to  lay  down  a  harmonised  strict  liability  regime  for  certain 
types  of  AI-enabled  products  and  services  at  the  present  time,  whether  or  not  coupled  with 
mandatory insurance.  

In addition and in order to inform the necessary political decision between PO1 and PO3, it is also 
important  to  consider  how  well  these  options take  into  account  the  suggestions  of  the  European 

213 See Annex 10 for detailed explanations on the assessment and comparison of the POs. 

60 

 
 
 
 
 
 
 
 
 
 
 
                                                           
Parliament as well as stakeholder opinions. In this respect, firstly, only the staged approach (PO3) 
incorporates both main elements suggested by the Parliament – a facilitated burden of proof under 
fault-based liability rules and a limited strict liability regime for certain AI-enabled technologies – 
explicitly  into  the  proposed  legislative  instrument.  While  the  strict  liability  element  is  not  yet 
implemented at the present stage, the targeted review mechanism provides a dedicated framework 
preparing  the  ground  for  the  future  policy  decision  on  this  element.  Secondly,  by  explicitly 
acknowledging  the  possible  need  for  a  harmonised  strict  liability  regime  in  the  proposed 
legislative  provisions,  PO3  also  satisfies  to  a  greater  extent  the  opinions  expressed  by  non-
business stakeholders, a large majority of whom supported the harmonisation of strict liability.  

If, at the stage of the review, a political decision is taken to propose a harmonised strict liability 
regime,  this  measure  could  be  designed  in  a  way  to  meet  concerns  expressed  by  business 
stakeholders, which are at present sceptical towards it. 

In  light  of  these  considerations,  the  staged  approach  (PO3)  is  the  most  balanced,  politically 
feasible, proportionate and yet effective option. It is most suitable to deliver the desired economic 
benefits  in  terms  of  roll-out  of  AI-enabled  products  and  services  in  the  internal  market,  and  to 
increase  citizens’  trust  in  AI  by  ensuring  that  victims  who  suffered  harm  caused  with  the 
involvement  of  AI  systems  enjoy  the  same  level  of  protection  as  victims  who  suffered  harm 
caused  by  other  technologies.  It  is  also  most  adapted  to  the  political  context  of  the  AI  liability 
initiative,  including  the  Parliament’s  legislative  own-initiative  resolution,  and  stakeholder 
feedback. 

8.2. Rationale and main impacts of the preferred PO  

PO3 would achieve the SO of reducing legal uncertainty, preventing fragmentation and protecting 
victims by ensuring an effective path to compensation. The measures included in PO3 would also 
incentivise  compliance  with  safety  requirements  and  requirements  designed  to  safeguard 
fundamental  rights,  applicable  to  AI-enabled  products  and  services  and  thus  reinforce  the  eco-
system of trust in AI. 

The main impacts concern potentially liable persons and potential victims. On the one hand,  the 
internal  market  obstacles  identified  will  be  reduced  and  this  will  lead  to  reduced  costs  for 
business,  which  would  outweigh  any  possible  adaptation  (substantive  compliance)  costs.  On  the 
other hand, PO3 will reduce victim’s costs and re-distribute the burden of bearing the damage to 
the person responsible for causing it.  

The choice of PO3 as preferred option is a result of the application of the proportionality principle. 
This  PO  will  allow  to  reassess  the  need  for  harmonising,  in  addition  to  aspects  related  to  the 
burden of proof, also situations with specific risk profile where the use of AI would warrant strict 
liability. At that point in time, the price of insurance will likely be more stable because of more 
available data.   

8.3. The  interplay  with  the  PLD  review:  consistency  and  complementarity  of  the 

package of measures for the compensation of damage caused by AI 

As explained above, the measures  from the preferred PO of the two impact assessments address 
challenges  posed  by  emerging  digital  technologies  with  respect  to  claims  based  on  different 
grounds,  covering  different  types  of  damages  and  victims  and  against  different  liable  persons. 
Both  preferred  PO  have  a  consistent  approach  and  use  similar  tools  (access  to  information, 
adaptations of the burden of proof) to ensure, in their combination, that victims of damage caused 
by  AI-systems  have  the  same  level  of  protection  compared  to  damage  caused  by  traditional 
products, no matter the liability path taken.  

61 

 
 
8.4. One-In-One-Out 

The preferred PO involves small  incremental  adjustment costs for liable parties. These costs are 
linked  to  a  transfer  of  costs  of  compensation  from  victims.  The  envisaged  alleviations  of  the 
burden of proof will allow victims to claim compensation successfully in cases where they would 
have  been  unable  to  prove  a  justified  claim  under  the  baseline  scenario  due  to  the  AI-specific 
difficulties  of  proof.  This  transfer  is  fully  in  line  with  the  policy  objectives  and  the  spirit  and 
purpose of liability rules.  Its  effect  on liable parties has been approximated in  terms  of possible 
increases of general liability insurance premiums for a transitional period, where data is lacking. 
Also, potentially liable entities are in many cases likely to be covered by voluntary insurance, so 
that their exposure –which will be in any case done at their own interest- would again be limited to 
the  premiums.  Overall,  it  is  expected  that  the  annual  volume  of  premiums  paid  for  liability 
insurance will increase by EUR 5.35mln - 16.1mln due to the preferred option. This incremental 
increase  does  not  represent  a  significant  burden  for  stakeholders  active  in  the  AI  market,  given 
that the overall amount of these premiums is over EUR 42bln per year, since it only represents an 
increase of 0,013% - 0,04%  

This cost transfer will be mostly relevant for businesses as potentially liable parties rather than for 
natural  persons.  This  is  because  the  AI-specific  liability  gaps  addressed  by  the  preferred  policy 
option  are  more  likely  to  affect  the  liability  exposure  of  actors  with  an  active  influence  on  the 
functioning of the relevant AI systems. The benefits of the policy options for companies in terms 
of increased legal certainty and reduced legal fragmentation outweigh these marginal adjustment 
costs,  because  the  business-as-usual-costs  under  the  baseline  scenario  related  to  the  uncertainty 
about  what  liability  would  apply  to  AI  and  what  burden-of-proof  rule  a  court  would  apply  in  a 
concrete case are higher. 

The preferred PO will not introduce any administrative requirements (e.g. reporting, registration, 
monitoring)  for  any  of  the  entities  within  its  scope,  i.e.  potentially  liable  parties  or  victims.  A 
possible incremental increase in  judicial  claims  and the  related costs borne by the parties or the 
courts  are  not  included  in  the  ‘administrative  costs’  for  the  purposes  of  the  One-In-One-Out 
approach, as they are not directly linked to the compliance with a law.  

The  preferred  policy  option  will  not  entail  costs  incurred  in  related  markets  or  experienced  by 
stakeholders  that  are  not  directly  targeted  by  the  initiative.  In  particular,  as  the  initiative  is 
expected  to  generate  net  cost  savings  for  businesses  active  in  AI,  it  is  not  expected  to  lead  to 
increased consumer prices. 

9.  HOW WILL ACTUAL IMPACTS BE MONITORED AND EVALUATED? 

In order to ensure that the targeted review  mechanism under the preferred PO (staged approach) 
can rely on a sufficient evidentiary basis, this mechanism could: 

-  provide for reporting and information sharing by MS regarding the application of the measures 

under PO 1 in national judicial or out-of-court settlement procedures; 

-  use information  collected by the Commission  or market  surveillance authorities under the AI 

Act (in particular Article 62) or other relevant instruments; 

-  use  information  and  analyses  supporting  the  evaluation  of  the  AI  Act  and  the  reports  to  be 

- 

prepared by the Commission on the implementation of that act; 
take  into  account  any  information  and  analyses  supporting  the  assessment  of  relevant  future 
policy measures under the ‘old approach’ safety legislation214; 

214 Cf. Section B of Annex II to the AI Act. 

62 

 
 
                                                           
-  rely on the information and analyses supporting the Commission’s report on the application of 
the  Motor  Insurance  Directive  with  regard  to  technological  developments  (in  particular 
autonomous and semi-autonomous vehicles) pursuant to its Article 28c(2)(a). 

To  evaluate  the  effectiveness  of  the  preferred  PO,  success  criteria  and  data  sources  have  been 
defined on a provisional basis for each of the SO.215 For instance: 

-  for SO1, effectiveness  will  be evaluated based on  the level  of legal  certainty  as perceived by 

business stakeholders, compared to the results of the public consultation; 

-  for  SO2,  the  adoption  of  any  diverging  measures  on  AI  liability,  as  reported  by  MS,  will  be 

taken into account to evaluate the initiative’s effectiveness; 

-  for SO3, the degree to which compensation gaps have been prevented and consumer trust has 

increased will be assessed based on expert and behavioural analysis, and MS reporting. 

Efficiency would be evaluated against specific operational objectives, for example: 

-  With  respect  to  companies,  cost  reductions  due  to  increased  legal  certainty  and  reduced 

fragmentation could be used as benchmark, based on stakeholder feedback. 

-  Regarding victims, the level of difficulty encountered when claiming compensation for damage 

caused by AI could be compared to the experts’ estimates in the Economic Study. 

-  For insurers, the liability-related conditions for offering insurance coverage could be assessed, 

based on feedback from a stakeholder survey and/or a dedicated workshop. 

In addition, criteria for evaluating the proportionality, coherence and continued relevance as well 
as the EU added value of the policy measures have been developed. For instance, the emergence of 
any  more  effective  or  efficient  means  to  achieve  the  policy  objectives  would  be  assessed  by 
Commission  services  taking  into  account  stakeholder  feedback  and  information  reported  by  MS 
on relevant legal cases. Moreover, the synergetic interplay between this initiative and the AI Act 
as well as the revised PLD would be assessed. 

215 For details on how the impacts will be monitored and evaluated for all the relevant categories, see Annex 12. 

63 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
Annex 1: Procedural information 

1.  LEAD DG, DeCIDE PLANNING/CWP REFERENCES 

DG Justice and Consumers, PLAN/2020/9848 - Adapting liability rules to the digital age and 
Artificial Intelligence, CWP 2020 

2.  ORGANISATION AND TIMING 

Roadmap consultation period – 30 June to 28 July 2021 

Open public consultation period – 18 October 2021- 10 January 2022 

ISSG meeting on the Impact Assessment – 18 February- participants: SG, LS, JUST, GROW, 
CNECT, JRC, SANTE, EMPL, COMP, FISMA, AGRI, ECFIN, ENV 

3.  GLOSSARY 

Term or acronym 

Meaning or definition 

AI 

AI Act 

IPR 

MID 

MPR 

MS 

PLD 

PO 

SO 

Artificial intelligence 

Artificial Intelligence Act: Proposal for a Regulation of  
the European Parliament and of the Council laying down 
harmonised rules on artificial intelligence (Artificial 
Intelligence Act) (COM/2021/206 final) 

Intellectual Property Right(s) 

Motor Insurance Directive: 
Directive 2009/103/EC of the European Parliament and 
of the Council of 16 September 2009 relating to 
insurance against civil liability in respect of the use of 
motor vehicles, and the enforcement of the obligation to 
insure against such liability 

Machinery Products Regulation:  
Proposal for a  
Regulation of the European Parliament and of the  
Council on machinery products (COM(2021) 202 final). 

Member State(s) of the European Union 

Product Liability Directive:  
Council Directive 85/374/EEC of 25 July 1985 on the 
approximation of the laws, regulations and administrative 
provisions of the Member States concerning liability for 
defective products 

Policy Option(s) 

Specific Objective(s) 

64 

 
 
 
 
4.  CONSULTATION OF THE RSB 

4.1. Upstream meeting with the RSB – 30 March 2020 

The  guidance  and  advice  provided  by  the  RSB  was  implemented  in  this  impact  assessment,  in 
particular: 

-  The interplay  of this planned initiative with  other relevant  policy  measures  (in particular 
the PLD review and the AI Act) were addressed in detail in close cooperation with other 
Commission  services,  to  ensure  that  related  initiatives  fit  into  a  coherent  overall 
intervention logic, serve consistent objectives and achieve synergies. Explanations on these 
aspects are provided throughout the impact assessment as well as in Annexes 6, 7 and 8. 
-  The measures suggested in the European Parliament’s resolution on a civil liability regime 
for  AI  were  to  a  large  extent  incorporated  into  the  assessed  policy  options.  For  detailed 
explanations  regarding  the  degree  of  alignment  with  the  Parliament’s  resolution,  see 
Annex 4, section 4. 

-  The lack of quantified data (mainly due to  the fact  that the relevant  AI-enabled products 
and services are for the most part not  yet on the market) is acknowledged. The measures 
undertaken to remedy the scarcity of data and to nevertheless generate quantified estimates 
are described. See in particular Annexes 4 and 10. 

-  The specific challenges of AI regarding existing civil liability rules are explained in detail 
in  the  impact  assessment  and  Annex  5,  and  illustrated  with  examples.  Concrete  use-case 
scenarios  demonstrating  these  challenges  have  been  elaborated  together  with  AI  experts 
from  the  Joint  Research  Centre  (Annex  13).  In  this  respect,  the  communalities  and 
differences  between  the  Product  Liability  Directive  and  national  civil  liability  rules  are 
explained. 

-  The  role  of  insurance  solutions  has  been  emphasised  and  addressed  in  detail  in  the 
assessment  of  policy  options,  taking  into  account  detailed  discussions  with  insurance 
experts and stakeholders, as well as the feedback received from these stakeholders during 
the public consultation and other consultation activities. 

-  The  preferred  option  was  conceived  specifically  to  enable  an  effective  monitoring  and 
evaluation of the envisaged measures, and to ensure that policy decisions on the more far-
reaching measures considered (strict liability, possibly coupled with mandatory insurance) 
will be taken, at the stage of the targeted review, on an even more comprehensive evidence 
base.  

4.2. Opinion of the RSB and responses 

The  Impact  Assessment  report  was  reviewed  by  the  Regulatory  Scrutiny  Board.  It  received  a 
positive opinion with reservations. The Impact Assessment has been revised to take into account 
the Board’s comments:  

Comments by the RSB 

(1) The report should explain clearly, why the 
initiative cites fragmentation of national rules 
as the main justification for the proposed single 
market legal base, yet limits the scope of the 
initiative to AI alone, given the highly 
fragmented state of tort law covering other 
products and services between different 
Member States. It should better justify how, in 
the specific case of AI, the variety of national 
rules on burden of proof differs from other 

Description of how and where comments were 
addressed 
The factors distinguishing AI from other 
technologies and the reasons for proposing AI-
specific measures regarding civil liability have 
been clarified in the problem definition and the 
subsidiarity analysis. In particular, the pivotal 
importance of AI for the internal market as a 
set of crucial enabling technologies and the role 
of the liability initaitive as a key component of 
the Commission’s overall AI policy have been 
explained in this context. Additional 

65 

 
 
Comments by the RSB 

types of products or services. The subsidiarity 
assessment should be strengthened, given the 
initiative’s aim to create harmonised AI 
liability rules in deeply embedded and diverse 
national liability systems. The initiative should 
also present evidence on the perception and 
level of support from businesses, Member 
States and the European Parliament. 
(2) The likely evolution of the problem and the 
baseline should better incorporate the likely 
positive effects of the proposed EU legislation 
on AI, as it should reduce the risks for damage 
from AI and the need for liability 
compensation. Given their timing, it is not 
clear whether or to what extent the supporting 
studies and consultations incorporate the 
expected positive effects of the proposed AI 
legislation. 
(3) The report should analyse a more complete 
set of options. The report needs to discuss the 
reasons why it does not consider as an option 
the European Parliament’s Article 225 
Resolution for a complete reversal of the 
burden of proof. If it considers that this option 
is not realistic or feasible, it should 
demonstrate this clearly in the discarded 
options section. In addition, the report should 
be more specific on the exact content of some 
of the measures, such as the ‘targeted 
alleviation of the burden of proof’ or the 
‘harmonised strict liability regime’. It should 
consider whether there are possibly alternative 
solutions and should analyse these as sub-
options if policy choices need to be made. 
Again, if some of them are not feasible or 
realistic, the report should discuss this in the 
discarded options section. 

(4) The structure of the policy options should 
be presented in a coherent manner. The report 
should present genuine and credible 
alternatives that can tackle the identified 
problems. The report should bring out much 
more clearly the differences between options 1 
and 3, which, in terms of substance and of 
expected impacts, appear to be identical given 
that both options can be reviewed once a more 
robust evidence base (that could justify more 
ambitious action) is in place. Given that the 

Description of how and where comments were 
addressed 
explanations on the views of business 
stakeholders as well as MS and the EP have 
been included in the revised version, 
acknowleding transparently that MS were for 
the most part not expressing firm positions on 
the AI liability initiative.   

Explanations inserted in the description of the 
baseline, to clarify that the preventive effect of 
the AI Act and other relevant safety legislation 
was taken into account, including by the 
supporting studies. It is however acknowledged 
that this effect could be expressed in 
qualtitative terms only, as no quantitative 
estimates for AI-enabled products and services 
are available.  

Further detailed explanations were inserted in 
the description of the policy options to address 
this comment. In particular, the possible 
alternative approaches for alleviating the 
burden of proof (in particular the reversal of the 
burden of proof suggested by the EP) are 
described more concretely. Moreover, the 
reasons for retaining (only) the tool of a 
rebuttable presumption for detailed assessment 
are further developed, taking into account 
stakeholder feedback as well as considerations 
of proportionality and coherence with national 
legal systems.  
Similarly, with respect to policy option 2, the 
design and content of the strict liability regime 
is presented in more detail, mentioning the 
content of a harmonised strict liability rule and 
the approach to determine the scope of this rule 
with a list of AI use cases in a technical Annex, 
which could be updated by way of Commission 
delegated acts. 
The description of the policy options was 
revised to clarify the differences between 
options 1 and 3. In particular, it has been 
further explained how the ‘targeted review’ 
under option 3 differs from a standard 
evaluation as required by general better 
regulation rules. It is now mentioned that this 
mechanism also fulfills the function to integrate 
the approach suggested by the European 
Parliament – a combination of measures to ease 
the burden of proof for fault-based claims with 

66 

 
 
Comments by the RSB 

two options, on substance, seem identical, the 
report should consider the continued practical 
relevance of option 3, and, if retained, it should 
be adjusted to make it substantively different 
from option 1 both in terms of measures 
included and expected impacts. Such 
differences would need to be substantiated by 
credible and robust evidence. 
(5) At the minimum, the report should ensure 
that options 1 and 3 score equally in terms of 
effectiveness, efficiency and coherence since 
any other scoring lacks credibility given their 
inherent similarity. The assessment of impacts 
for option 2 needs to be revisited and clarified, 
in a manner that justifies why it is not the 
preferred option, considering the objectives to 
be reached, assuming it is maintained as a 
realistic and feasible option at this point in 
time. The sub-option on insurance should be 
explicitly analysed.   

(6) The report should be more transparent 
about the credibility and relevance of the 
quantitative impact estimates. As the economic 
support study did not model the impacts of the 
options as described in the report, it should 
clearly explain the limitations of its results. It 
should better justify the conclusion that the 
(non-quantified) benefits for businesses 
resulting from increased legal certainty and 
reduced legal fragmentation outweigh their 
(quantified) costs, not least given this appears 
to contradict businesses’ views. 
(7) The report should add a separate subsection 
on the application of the ‘one in, one out’ 

67 

Description of how and where comments were 
addressed 
a harmonised strict liability regime for certain 
specific AI applications – into the AI liability 
instrument, while allowing the Commission to 
take the necessary policy decision on strict 
liability at a point in time when all the 
necessary information and evidence will be 
available.  

The scoring was adapted in line with RSB 
comments. Options 1 and 3 now receive equal 
scores and additional clarifications are 
provided justifying why option 2 is not the 
preferred option. Option 3 remains the 
preferred option because it strikes the best 
overall balance It namely takes into account all 
elements suggested by the European 
Parliament’s legislative own-initiative 
resolution, including a harmonised strict 
liability regime for certain AI-technologies, as 
well as the broad support from non-business 
stakeholder for such a measure. At the same 
time, it is strictly aligned with the principle of 
proportionality and the constraints imposed by 
political feasibility, given that the AI use cases 
with a potential strict liability profile are 
presently not on the market yet, and business 
stakeholders were strongly opposed to 
harmonising strict liability for AI at the present 
time. The revised version of the impact 
assessment elaborates on these considerations 
to substantiate the choice of option 3 as the 
preferred option. 
In addition, the revised version contains 
additional explanations pertaining specifically 
to the sub-option of combining strict liability 
with a mandatory insurance regime. 
The uncertainty linked to the fact that the 
policy options were adjusted compared to the 
economic study is explicitly acknowledged in 
the revised impact assessment. The reasons for 
nevertheless considering the findings of that 
study relevant for the assessment of the policy 
options are explained. These explanations are 
namely provided in the context of PO1, 
because the economic study did not explicitly 
assess alleviations of the burden of proof taken 
in isolation, as per PO1.  

A subsection on the application of the OIOO 
approach has been inserted in section 8 

 
 
Description of how and where comments were 
addressed 
(‘Preferred option’), explaining that the 
initiative will not entail administrative costs, 
but only small incremental adjustment costs for 
liable parties. 

The revised version of the impact assessment 
elaborates on the reasons for varying 
stakeholder positions, with a particular focus 
on the views submitted by business 
stakeholders. 

In the revised version, it is also acknowledged 
and explained that MS have not yet expressed 
any clear views on the AI liability intiative, 
reserving their positions for the negotiations on 
the legislative proposal. 

Comments by the RSB 

approach. It should explain why it has been 
concluded that the preferred option will not 
entail significant administrative costs. As 
indirect administrative costs are in scope of the 
‘one in, one out’ approach, they should also be 
discussed.   
(8) The report should explain the reasons 
behind divergent stakeholder views on the 
policy options and, if possible, differentiate the 
views of various businesses segments (e.g. 
producers, service providers, distributors 
versus users etc). It should explore and discuss 
the reasons cited by stakeholders opposed to 
EU-level action. The report needs to explain 
particularly why business stakeholders are less 
positive about the initiative than other 
stakeholders are. It should also explain whether 
and how such less positive views have been 
taken into consideration in the impact analysis 
and the comparison of the options. The report 
should be upfront about the absence of 
Member States views and the reasons for their 
decision not to engage in the tailored 
consultations.   

68 

 
 
 
 
5.  EVIDENCE, SOURCES AND QUALITY 

The discussion on liability for AI has started with  the work undertaken by  the Expert Group on 
Liability  and  New  Technologies216,  which  had  a  series  of  meetings  in  2018  and  2019  and  was 
organised  in  two  formations:  Product  Liability  and  New  Technologies.  The  New  Technologies 
formation  of  the  Expert  Group  adopted  a  Report  in  2019  on  the  topic  of  liability  for  artificial 
intelligence  and  other  emerging  digital  technologies.217  This  report  informed  the  Commission 
services in the preparation of the Commission Report on AI Liability218 accompanying the White 
Paper on AI. 

In  view  of  preparing  this  Impact  Assessment,  the  Commission  also  contracted  legal  experts  for 
further  comparative  legal  analysis219,  an  additional  study  to  provide  economic  analysis220  and  a 
third study focusing on behavioural analysis.221 

The specific details of all these studies, their scope and methodology are described in Annex 4. 

In addition,  the Commissions services also  considered  the ‘Comparative  study on national  rules 
concerning  non-contractual  liability,  including  with  regard  to  AI’  which  accompanied  the 
European  Added  Value  Assessment  on  a  Civil  liability  regime  for  AI222  commissioned  by  the 
European Parliament. 

The  Impact  Assessment  was  further  based  on  the  results  of  the  public  consultation,  the  input 
provided by stakeholders on the roadmap, online webinars, survey with citizens (over 8,000) done 
in  the  context  of  the  behavioural  economics  study  and  numerous  bilateral  contacts  with 
stakeholders  and  meetings  with  public  authorities.  Annex  2  provides  more  details  about  these 
sources. 

for  artificial 

technologies,  November  2019, 

216 Register of Commission expert groups and other similar entities (europa.eu)  
217  Liability 
intelligence  and  other  emerging  digital 
https://op.europa.eu/en/publication-detail/-/publication/1c5e30be-1197-11ea-8c1f-01aa75ed71a1/language-en  
218 Report from the Commission to the European Parliament, the Council and the Economic and Social Committee on 
the  safety  and  liability  implications  of  Artificial  Intelligence,  the  Internet  of  Things  and  robotics,  19.2.2020, 
COM(2020) 64 final. 
219  Karner/Koch/Geistfeld,  Comparative  Law  Study  on  Civil  Liability  for  Artificial  Intelligence,  2021, 
https://op.europa.eu/en/publication-detail/-/publication/8a32ccc3-0f83-11ec-9151-01aa75ed71a1/language-en 
220  Deloitte,  Study  to  Support  the  Commission’s  Impact  Assessment  on  Liability  for  Artificial  Intelligence,  2021 
(‘Economic Study’). 
221 Behavioural Economics Study, op.cit. 
222  EPRS,  Civil 
https://www.europarl.europa.eu/RegData/etudes/STUD/2020/654178/EPRS_STU(2020)654178_EN.pdf  

intelligence,  September  2020,  Author:  Tatjana  Evas, 

for  artificial 

liability 

regime 

69 

 
 
 
 
 
                                                           
Annex 2: Stakeholder consultation  

A. Outline of the consultation strategy/process  

In line with the Commission’s Better Regulation requirements, an extensive consultation strategy 
has been implemented to ensure a wide participation throughout the policy cycle of this initiative. 
The consultation strategy was based on both public and targeted consultations. The Commission 
has  sought  a  wide  and  balanced  range  of  views  on  this  issue  by  giving  the  opportunity  to  all 
relevant  stakeholders  to  express  their  views.  In  particular,  the  stakeholders  addressed  by  the 
strategy were:  

-  EU and national consumer associations and civil society organisations active in the justice 

field, victims associations and individual victims of damage caused by products  

- 

Industry associations at EU, national or sectoral level   

-  Economic  operators,  including  SMEs  (e.g.  manufacturers  of  products  and  components, 
software  developers,  AI  programmers,  medical  devices  manufacturers,  manufacturers  of 
pharmaceutical  products,  retailers  (offline  and  online),  online  marketplaces,  repair  and 
refurbishment providers, providers of services, operators and users of various products)  

- 

Insurance associations and individual insurers 

-  Legal firms and lawyers 

-  Academic experts and research bodies 

-  Citizens  

-  National authorities. 

The  extensive  consultation  process  included  various  activities,  for  which  the  content  and  results 
are described in  greater  detail  in  the subsequent sections of this Annex, along with  explanations 
about  how  the  received  feedback  was  taken  into  account  for  the  development  of  policy  options 
covered by this IA: 

-  public  consultation  on  the  White  Paper  on  AI  and  the  Commission  report  on  safety  and 

liability; 

-  online  interactive  webinars  with  a  broad  range  of  stakeholders,  focused  on  specific 

questions regarding AI liability; 

-  gathering of feedback on the published inception impact assessment; 

- 

- 

- 

surveys  and  targeted  interviews  carried  out  in  the  framework  of  two  supporting  studies 
(general economic and behavioural analysis); 

a dedicated 12-weeks public consultation on Adapting Civil Liability Rules to the Digital 
Age and AI, which covered both the general review of the Product Liability Directive and 
the AI-specific issues addressed by this IA; 

a  workshop  with  Member  State  representatives  organised  to  gather  feedback  on  the  AI 
liability initiative and the review of the PLD.  

The  results  of  the  dedicated  public  consultation  are  summarised  first  (Section  B),  and  other 
consultation activities subsequently (Section C). 

70 

 
 
B.  

Public consultation on Adapting Civil Liability Rules to the Digital Age and AI 

1. 

Introduction 

The public consultation on Adapting Civil Liability Rules to the Digital Age and AI covered both 
the general review of the Product Liability Directive and the AI-specific issues addressed in this 
IA. Section I of the public consultation aimed to gather stakeholders’ views on how to improve the 
Product  Liability  Directive  (PLD);  this  section  received  a  total  of  291  responses.  Section  II 
concerned  problems  linked  to  certain  types  of  AI  that  make  it  possibly  difficult  to  identify  the 
potentially liable person, to prove that person’s fault and/or the defect of a product and the causal 
link  with  the  damage.  There  was  a  filter  question  asking  respondents  to  choose  if  they  want  to 
continue with Section II or finish the survey after Section I, so only 233 responses were provided 
in  Section  II.    A  large  number  of  business  associations  (63),  representing  a  wide  variety  of 
interests  and  company  sizes,  contributed  as  well  as  individual  companies  (29),  including  SMEs 
(9). Associations representing consumers also contributed (7), including BEUC. The consultation 
includes also input from individual citizens (95) and NGOs (10), as well as research institutions 
(14) and national ministries (5)223. Some questions received less answers, since not all participants 
answered all the questions. The following summary focuses on the results relevant concerning AI-
related questions in particular (section II of the public consultation survey). 

Respondents by stakeholder category

National Ministries, 5 = 2% Trade union, 1 = 0%

Consumer organisation, 7 = 3%

Non-EU citizen, 1 = 0%

Other, 8 = 4%

Non-governmental organisation 
(NGO), 10 = 4%

Research institutions, 
15 = 7%

Company/business 
organisation, 29 = 13%

Business association, 
63 = 27%

EU citizen, 94 = 40%

In terms of geographical representation, the consultation includes contributions from 21 Member 
States,  as  well  as  from  third  countries.  The  geographical  coverage,  however,  is  broader  because 
some associations, which indicated Belgium as their country of origin, also represent stakeholders 
from Member States not directly mentioned in the responses. The majority of contributions come 
from Germany (91), followed by Belgium (39) and France (20). No campaigns were identified. 

223 The Czech Ministry of trade and industry, the Austrian Ministry for Social Affairs, the Bulgarian Ministry for the 
Economy and the Ministries of Justice of Estonia and Finland participated in the Survey. 

71 

 
 
 
 
 
                                                           
There were also 70 position papers submitted separately in the public consultation.2. 
summary of key results 

Short 

2.1. 

Problems and problem drivers 

(a) The specific characteristics of AI make it difficult to meet the burden of proof  

The results of the public consultation confirm the existence of this problem driver:  

Amongst  responding  consumer  organisations,  NGOs,  national  ministries,  academic/research 
associations and EU citizens, an overwhelming majority agreed that:  

(i)  it  could  be  difficult  to  link  damage  caused  by  highly  autonomous  AI  to  the  actions  and  or 
omissions  of  a  human  actor  (91%,  or  85  out  of  93,  agreement  amongst  responding  citizens; 
unanimous  agreement  amongst  responding  consumer  organisations,  NGOs,  national  ministries 
and academic/research associations),  

(ii)  in  the  case  of  opaque  and  complex  AI,  it  could  be  difficult  for  victims  to  prove  that  the 
conditions  of  liability  are  fulfilled  (84%,  or  78  out  of  93,  agreement  amongst  responding 
citizens; unanimous agreement amongst responding consumer organisations, national ministries, 
NGOs and research institutions), and  

(iii) because  of  AI’s  specific  characteristics,  victims  may  in  certain  cases  be  less  protected  that 
victims of damage that did not involve AI (79%, or 74 out of 94, agreement amongst responding 
EU  citizens;  84%  agreement  amongst  responding  consumer  organisations,  national  ministries, 
NGOs and research institutions, with only one disagreeing).  

Responding  business  stakeholders  (business  associations  and  companies/business  organisations) 
also agreed broadly with this problem driver, but were less unequivocal:  

-  An absolute majority agreed that because of AI’s specific characteristics, victims may in certain 
cases be less protected that victims of damage that did not involve AI (54 %, or 45 out of 85, v. 
26 %, or 22 out of 85, who disagreed).  

-  A relative majority agreed that in the case of opaque and complex AI, it could be difficult for 
victims to prove that the conditions of liability are fulfilled (41 or 35 out of 85 % v. 28 % or 24 
out of 85 who disagreed). 

-  Only  with  respect  to  the  statement  that  it  could  be  difficult  to  link  damage  caused  by  highly 
autonomous AI to the actions and or omissions of a human actor , more business stakeholders 
disagreed (45% or 38 out of 85) than agreed (36% or 30 out of 85). 

(b) Lack of compensation leads to a lack of trust in AI 

A  clear  majority  of  overall  respondents  confirmed  that  the  lack  of  adaptation  of  existing  liability 
rules to AI may negatively affect trust in AI (60 %, or 135 out of 227, agreement v. 25%, or 58 out 
of 227, who disagreed) and the uptake of AI-enabled products and services (56%, or 126 out of 227, 
agreement  v.  27%,  or  60  out  of  227,  disagreement).  In  particular,  responding  NGOs,  consumer 
organisations, academic/research institutions and EU citizens overwhelmingly confirmed these 
problems (92%). By contrast, close to 60 % (or 49 out of 85) of responding business stakeholders 
(business  associations  and  companies/business  organisations)  did  not  confirm  them,  while  around 
27 % (or 23 out of 85) did.  

(c) Legal uncertainty 

The public consultation confirmed legal uncertainty leading to internal market obstacles: Amongst 
responding consumer organisations, NGOs, academic/research associations and EU citizens, an 
overwhelming majority agreed that:  

72 

 
 
(i) 

(ii) 

it is uncertain whether and how liability rules under national law apply to damage caused by 
AI  (84%,  or  27  out  of  32,  agreement  amongst  consumer  organisations,  NGOs  and 
academic/research associations; 85%, or 80 out of 94, amongst EU citizens),  
it  is  uncertain  how  national  courts  will  address  possible  difficulties  of  proof  and  liability 
gaps  in  relation  to  AI  (81%,  or  25  out  of  31,  agreement  amongst  consumer  organisations, 
NGOs and academic/research associations; 91%, or 84 out of 92, amongst EU citizens).  

Responding business stakeholders were more divided: A bigger share disagreed with statement (i) 
(52%, or 44 out of 85), while still a sizeable share of 32 % (or 27 out of 85) confirmed uncertainty 
as to how national liability rules will apply. More business respondents agreed with the uncertainty 
as  to  how  national  courts  will  react  to  the  challenges  of  AI:  34%  (29  out  of  85)  confirmed 
uncertainty, while 43% (36 out of 85) did not. 

Substantial shares of businesses stakeholders (although not the overall majority) think that the lack 
of  adaptation  of  the  current  liability  rules  to  AI  will  entail  a  number  of  negative  economic 
consequences.  They  expect  for  instance  that  this  lack  of  adaptation  of  liability  rules  will  entail 
additional costs (e.g. legal information costs, insurance costs) for companies (27% or 23 out of 85), 
and that companies may defer or abandon certain investments in AI technologies if the current 
liability framework is not adapted (20% or 17 out of 85).  

A slight majority of business associations representing primarily SMEs think that, if the liability 
framework is not adapted: 

- 

- 

- 

companies will face additional costs (e.g. legal information costs, increased insurance 
costs) (4 agree while 3 disagree),  
insurers  will  increase  risk-premiums  due  to  a  lack  of  predictability  of  liability 
exposures (3 agree and 2 disagree) and  
there  will  be  a  negative  impact  on  the  roll-out  of  AI  technologies  in  the  internal 
market (3 agree and 2 disagree). 

(d) Legal fragmentation 

A majority of overall respondents expect that legal fragmentation with respect to civil liability for 
AI and/or the lack of adaptation of the existing liability framework will, for instance, entail at least 
to some extent additional costs for companies (e.g. legal information costs, insurance costs) (75 % 
or 153 out of 205) and the need to adapt AI technologies, distribution models and cost-management 
models (70 % or 146 out of 205). They will also cause at least some companies to limit their cross-
border  activities  related  to  the  production,  distribution  or  use  of  AI-enabled  products  or  services 
(67 % or 135 out of 203). 

Regarding  business  stakeholders  in  particular,  more  than  half  of  these  respondents  expect  that,  if 
Member  States  adapt  their  liability  rules  to  AI  in  a  divergent  way,  this  will  entail  at  least  some 
additional  costs  for  companies  (54%,  or  46  out  of  85  agree),  may  reduce  cross-border  activities 
involving AI (36% (31 out of 85) ) and have a negative impact on the roll-out of AI technologies 
(39% or 33 out of 85) while 29% (or 25 out of 85) do not expect such an impact. ). 

2.2. Policy options 

(a) Policy Option 1 

EU  citizens,  consumer  organisations,  academic/research  institutions  and  NGOs  overwhelmingly 
supported the measures envisaged under Option 1. 

73 

 
 
 
 
Replies  from  business  stakeholders  (business  associations  and  companies/business  organisations) 
were  more  nuanced:  Their  agreement  and  disagreement  is  roughly  evenly  distributed  as  regards 
rules on the disclosure of information  (33%, or 28 out  of 86,  agreement  v. 31%, or 27 out  of 86, 
disagreement).  A  majority  of  business  stakeholders  is  against  inferring  facts  from  the  refusal  to 
disclose information (63% or 54 out of 86, disagreement), shifting the burden of proof (63% or 54 
out  of  86,  disagreement),  or  presuming  causality  if  the  provider  of  an  AI  system  did  not  comply 
with their obligations under the AI Act (41%, or 36 out of 86, disagreement v. 30%, or 26 out of 86, 
agreement).  By  contrast,  business  stakeholders’  views  were  roughly  evenly  split  about  applying 
such a presumption vis-à-vis  the user of the AI-system:  while ca. 35 % (or 30 out  of 86) of them 
disagreed with that approach, close to 30 % (or 25 out of 86) supported it. 

SME stakeholders were more supportive of measures to ease the burden of proof: The responding 
individual  SMEs  approved  of  those  measures.  The  views  of  business  associations  representing 
(primarily) SMEs were in most cases roughly evenly split. 

(b) Policy Option 2 

The  results  of  the  open  public  consultation  showed  support  amongst  EU  citizens,  NGOs, 
academic/research 
institutions  and  consumer  organisations  for  either  full  or  minimum 
harmonisation of strict liability for the operation of certain AI-enabled products and the provision of 
certain AI-enabled services. In particular, EU citizens favoured a ‘full harmonisation’ approach to 
strict  liability  (77 %,  or  68  out  of  88,  v.  54 %,  or  47  out  of  88,  supporting  minimum 
harmonisation224).  By  contrast,  more  responding  consumer  organisations  supported  a  minimum 
harmonisation approach, allowing MS to maintain broader and/or more far-reaching national strict 
liability schemes (4 out of 7 agreed with a minimum harmonisation of strict liability). None of the 
responding consumer organisations supported full harmonisation of strict liability. 

Business stakeholders tend to oppose the harmonisation of strict liability. Opposition was stronger 
regarding  a  minimum  harmonisation  approach  (70 %,  or  59  out  of  84,  v.  14%,  or  12  out  of  84, 
support) than a full harmonisation of strict liability (42 %, or 35 out of 84, disagreement v. 30 %, or 
28  out  of  84,  agreement).  Interestingly,  SME  stakeholders  were  distinctly  more  supportive  of  a 
harmonised strict liability regime: Almost all of responding individual SMEs (9) approved of this 
policy option, and business associations representing (primarily) SMEs were evenly split regarding 
both  the  minimum  and  full  harmonisation  of  strict  liability.  Business  stakeholders  consistently 
argued against the harmonisation of strict liability in their position papers.  

(c) Policy Option 3 

The  ‘staged  approach’  (=  preferred  option)  was  not  presented  as  a  separate  policy  option  in  the 
public consultation, because it is a combination of elements included under the other policy options. 
Therefore, there are no results linked specifically to  this PO. The staged approach was developed 
and  refined  in  light  of  feedback  received  from  stakeholders  throughout  the  impact  assessment 
process.  It  strikes  a  balance  between  the  needs  expressed  and  concerns  raised  by  all  relevant 
stakeholder groups. 

3. Detailed summary of results 

224  The  answer  options  were  multiple  choice,  respondents  could  express  support  for  both  full  and  minimum 
harmonisation. 

74 

 
 
 
                                                           
The  following  summary  explains  for  each  of  the  topics  covered  by  the  public  consultation  the 
respective  results  as  regards  the  problem  analysis  as  well  as  the  development  and  assessment  of 
policy options and how they were taken into account in the IA.  

3.1. 

Problems  to  be  addressed:  Lack  of  compensation  and  consumer  trust,  legal 
uncertainty and fragmentation 

(a) 

Problem for victims of damage caused by AI: Lack of compensation 

The  problem  of  lack  of  compensation  due  to  the  specific  characteristics  of  AI  was  largely 
confirmed. 

-  74% (or 167 out of 227) of overall respondents (56%, or 127, strongly) agreed that in 
the  case  of  AI  that  lacks  transparency  and  explainability,  it  could  be  difficult  for 
injured  parties  to  prove  that  the  conditions  of  liability  (fault  or  causation)  are 
fulfilled. Only 12 % (or 26 out of 227) of respondents disagreed.  

Difficulties in proving the conditions of liability

6,3%

2,7%

8,9%

8,5%

17,4%

56,3%

Strongly agree

Agree

Neutral

Disagree

Strongly disagree

No opinion

Looking  at  specific  stakeholder  groups,  all  responding  consumer  organisations  and 
national  ministries  as  wells  as  research  institutions  confirmed  this  problem.  An 
overwhelming majority of EU citizens (91% or 85 out of 93) also agreed. Also a relative 
majority of businesses, including business associations and individual companies, agreed 
with this statement (41% or 35 out of 85), while 28% (or 24 out of 85) disagreed.  
- 
the  question  about  whether  it  could  be  difficult  to  link  damage  caused  by  AIto  the 
actions  or  omissions  of  a  human  actor  when  AI  operates  with  a  high  degree  of 
autonomy,  approximately  67%  of  respondents  agreed  (153  out  of  227,  of  which  105 
strongly,  i.e.  ca.  46%)  that  it  could  be  difficult  to  link  damage  caused  by  highly 
autonomous AI to the actions or omissions of a human actor. 20% (or 47 out of 227) 
disagreed. In this respect, high agreement was reached amongst responding citizens (84% 
or 78 out  of 93) and almost  unanimous  agreement  (94%, or 31 out  of 33, with  only one 
disagreeing)  amongst  responding  consumer  organisations,  national  ministries  and 
academic/research associations. The same applies for the responding  national ministries 
and research institutions. Businesses were split on this question: while 36% (or 30 out of 
85) agreed with this question, 45% (or 38 out of 85) disagreed. 

On 

75 

 
 
 
 
 
- 

61 % (or 139 out of 232) of respondents also agreed (42%, or 95 out of 232,  strongly) that 
because of specific AI characteristics, victims of damage caused by AI may in certain 
cases be less protected than victims of damage that did not involve AI;  only 23% (or 
52  out  of  232)  disagreed.  While  a  majority  of  businesses  disagreed  with  such  statement 
(54% or 45 out of 85), 26% (or 22 out of 85) did think that victims will be less protected 
due  to  the  specific  characteristics  of  AI.  Respondent  consumer  organisations,  national 
ministries and EU citizens overwhelmingly confirmed this problem. 

The  positions  submitted in  writing  point  to  a  similar  trend:  Representatives  of  academia,  NGOs 
and consumer associations agree that AI characteristics make it more difficult for victims to claim 
compensation and some agree with the legal uncertainty of applying existing rules. Many business 
associations  and  companies  cast  doubt  on  the  specific  challenges  linked  to  AI,  although  some 
admit that victims might have difficulties claiming compensation.  

  These results of the public consultation were analysed and taken into consideration to fine-
tune  and  confirm  the  problem  definition.  In  particular,  it  was  further  clarified  in  the  IA 
that  the  AI-specific  difficulties  in  claiming  compensation  do  not  affect  only  private 
individuals  harmed  by  AI,  but  companies  –  in  particular  SMEs  –  may  face  similar 
problems.  The  link  between  AI-specific  challenges  to  current  liability  rules  and  the 
identified internal market barriers was also further clarified.       

(b) 

Problem of lack of trust in AI – reduced uptake 

For a majority of respondents the lack of adaptation of the current liability framework to AI may 
negatively affect trust in AI (60%, or 135 out of 227, agreed; 25%, or 58 out of 227, disagreed) 
and the uptake of AI-enabled products and services (56%, or 126 out of 227, agreement v. 27%, or 
60 out of 227, disagreement).  

Lack of adaptiation and trust in AI

3,6%

7,6%

36,3%

17,5%

11,2%

Strongly agree

Agree

Neutral

Disagree

Strongly disagree

No opinion

23,8%

NGOs  and consumer associations overwhelmingly confirm  these problems  (89%, or 16 out  of 
18, expect a lack of trust and 78%, or 14 out of 18, reduced uptake). Similarly, a large majority of 
EU citizens consider that the lack of adaptation of the current liability framework may negatively 
affect trust in AI (81% or 77 out of 95) and the uptake of AI-enabled products and services (79% 
or 74 out of 94). Likewise, the vast majority of research institutions share this opinion. Among 
responding national ministries 1 confirmed the problem and 1 denied it. While the majority of 
business stakeholders (business associations and individual companies) (58% or 49 out of 85), did 
not  confirm  these  particular  problems,  significant  shares  of  these  stakeholders  did  consider  that 
current liability rules will lead to lower consumer trust (27% or 23 out of 85) and uptake (25 % or 

76 

 
 
 
 
 
21 out of 85), if not adapted. Moreover, amongst those business stakeholders who considered that 
AI can make it difficult to claim compensation: 

-  64%  agreed  that  the  lack  of  adaptation  of  the  current  liability  framework  to  AI  may 

negatively affect trust in AI, and  

-  59%  considered  that  the  lack  of  adaptation  of  the  current  liability  framework  to  AI  may 

negatively affect the uptake of AI-enabled products and services. 

  These  results  were  taken  into  account  in  particular  to  complement  the  findings  of  the 
behavioural  economics  study  regarding  the  link  between  the  perception  of  liability  rules 
and consumer trust  in  AI-enabled products  and  services  (see Annex 4 for  details  on that 
study). 
Legal uncertainty  

(c) 

Almost 63% (or 144 out of 227) of respondents agreed (36%, or 81 out of 227, strongly) that there 
is uncertainty as to whether and how liability rules under national law apply to damage caused by 
AI; only 22% (or 50 out of 227) disagreed (only 5 (11) strongly).  

Uncertainty under national law

6,7%

4,9%

36,2%

17,4%

7,1%

27,7%

Strongly agree

Agree

Neutral

Disagree

Strongly disagree

No opinion

Consumer  associations  unanimously  confirmed  their  perception  of  legal  uncertainty,  and 
responding  EU  citizens  NGOs  and  research  institutions  agreed  with  a  very  large  majority.  In 
particular,  85%  of  EU  citizens  (or  80  out  of  94,  with  only  one  citizen  disagreeing)  and  84%  of 
responding  NGOs,  consumer  organisations  and  academic/research  institutions  (or  27  out  of  32, 
with  none  disagreeing)  agreed  that  it  is  uncertain  whether  and  how  liability  rules  under 
national law apply to damage caused by AI. Responding national ministries were split. While 
about  half  of  businesses  stakeholders  (52%  or  44  out  of  85)  do  not  perceive  legal  uncertainty 
specifically  with  respect  to  liability  for  AI,  about  one  third  (32%  or  27  out  of  85)  of  these 
respondents did confirm that problem as well.   

This  picture is  mirrored by the responses to  the question  whether it is  uncertain how national 
courts  will  address  possible  difficulties  of  proof  and  liability  gaps  in  relation  to  AI.  An 
overwhelming majority  of responding  NGOs  +  consumer organisations (77% or 13 out  of 17) 
agreement; 0% disagreement) and EU citizens (91% or 84 out of 92, agreement) agreed that the 
approach of national judges is uncertain (while some of these respondents did not have an opinion 
on this, none disagreed). 2 out of 5 responding national ministries also confirmed the existence of 
such uncertainty, while one disagreed. Respondents from the business side were again evenly split 
on this: 34% (or 29 out of 85) confirmed the existence of such uncertainty while 43% (36 out of 
85) did not.  

77 

 
 
 
 
Businesses  stakeholders  in  particular  were  asked  to  provide  feedback  on  the  expected 
consequences of a lack of adaptation of the existing liability rules to AI. In this respect:  

-  Substantial  shares  of  businesses  stakeholders  (although  not  the  overall  majority)  think 
that  the  lack  of  adaptation  of  the  current  liability  rules  to  AI  will  entail  a  number  of 
negative economic consequences. They expect for instance: 

- 

o 

o 

that this lack of adaptation of liability rules will entail additional costs (e.g. legal 
information costs, insurance costs) for companies (27% or 23 out of 85),  
that companies may defer or abandon certain investments in AI technologies if 
the  current  liability  framework  is  not  adapted  (20%  or  17  out  of  85)  and  refrain 
from using AI when automating certain processes (21% or 18 out of 85). 
Interestingly, when looking specifically at business associations representing primarily 
SMEs,  opinions  are  even  more  balanced.  In  particular,  a  slight  majority  of  these 
respondents slightly agree with the  statements that companies will face additional costs 
(e.g. legal information costs, increased insurance costs) (4 agree while 3 disagree), that 
insurers  will  increase  risk-premiums  due  to  a  lack  of  predictability  of  liability 
exposures (3 agree and 2 disagree) and that if the liability framework is not adapted there 
will be a negative impact on the roll-out of AI technologies in the internal market (3 
agree and 2 disagree). They  slightly disagree with the statement that if the framework is 
not adapted companies may refrain from using AI when automating certain processes 
(4 against 3) and that there will be higher prices of AI-enabled products and services (4 
against 3).  

-  Most of the few responding individual SMEs expect additional costs, reduced investments 
and  cross-border  business  activities,  higher  prices  and  insurance  premiums  as  well  as  a 
negative impact on the roll-out of AI as a result of the lack of adaptation of current liability 
rules. 

-  When  focusing  specifically  on  those  business  stakeholders  who  agreed  that  there  is 
legal uncertainty regarding the application of national liability rules to damage caused by 
AI, strong majorities expect that, if the current liability framework is not adapted:  
o  companies will, at least to a moderate extent, face additional costs  (82%); 
o  companies  may  refrain,  to  a  moderate,  large  or  very  large  extent,  from  using  AI 

when automating certain processes (66 %); 

o  companies  may  limit  their  cross-border  activities  related  to  the  production, 

o 

distribution or use of AI-enabled products or services (62%); 
insurers will increase risk premiums, at least to a moderate extent, due to a lack 
of predictability of liability exposures (67%). 

  The results regarding legal uncertainty and its impacts were incorporated into the problem 
analysis  in  this  IA.  In  particular,  significant  shares  (although  relative  minorities)  of 
business  stakeholders  confirmed  the  perception  of  legal  uncertainty,  and  amongst  those 
who did, strong majorities linked the lack of adaptation of existing liability rules to AI to 
negative consequences such as additional costs and reduced cross-border activities in AI. 
The  fact  that  a  clear  overall  majority  of  respondents  confirmed  the  existence  of  legal 
uncertainty  regarding  the  application  of  national  liability  rules  to  AI  also  fed  into  the 
problem definition. 

(b) 

Legal fragmentation 

If  Member  States  adapt  liability  rules  for  AI  in  a  divergent  way,  or  national  courts  follow 
diverging interpretations of existing liability rules in  the case of damage caused by AI, 75% (or 
153  out  of  205)  of  respondents  expect  that  this  will  entail  at  least  some  additional  costs  for 
companies active in AI (e.g. legal information costs, increased insurance costs). 70% (or 146 out 

78 

 
 
 
 
of 205) also expect legal fragmentation with respect to liability for AI to entail a need to adapt AI 
technologies, distribution models and cost management models at least to some extent. 

Businesses stakeholders in particular were asked to provide feedback on the expected impacts of 
AI-specific legal fragmentation. Amongst these respondents: 

-  A  majority  (54%  or  46  out  of  85)  expects  that  legal  fragmentation  with  respect  to  civil 
liability  for  AI  will  entail  at  least  some  additional  costs  for  companies  (e.g.  legal 
information  costs,  increased  insurance  costs).  Only  15%  (or  13  out  of  85)  of  businesses 
would not expect such costs at all.  

Businesses: legal fragmentation and additional cost

17,6%

12,9%

15,3%

22,4%

12,9%

18,8%

To a very large extent

To a large extent

To a moderate extent

To a small extent

Not at all

Don't know/no answer

-  A relative majority (46% or 39 out of 85) also expects that legal fragmentation with respect 
to liability for AI entails a need to adapt AI technologies, distribution models and cost 
management models, while only 18% (or 15 out of 85) disagree. 59% (or 49 out of 83) of 
responding  businesses  stakeholders  anticipate  that  fragmented  AI-specific  liability  rules 
would  entail  at  least  some  (including  small)  need  for  technological  adaptations  when 
providing  AI-based  cross-border  services  (v.  27%  or  22  out  of  83  who  do  not  anticipate 
such a need for adaptation at all).  

-  50%  (or  42  out  of  84)  expect  that  insurers  will  increase  premiums  due  to  more 

divergent liability exposures (whereas 14% or 12 out of 84 do not). 

-  36%  (or  31  out  of  85)  expect  that  companies  may  limit  their  cross-border  activities 
related  to  the  production,  distribution  or  use  of  AI-enabled  products  or  services, 
while only 28% (or 24 out of 85) would not.  

-  Similarly, 38% (or 32 out  of 85) think that legal fragmentation regarding liability for AI 
will lead to higher prices for AI-enabled products and services (while only 24%, or 21 
out of 85, disagree). 

-  39% (or 33 out of 85) expect that increased legal fragmentation regarding liability for AI 
would have a negative impact on the roll-out of AI technologies, whereas only 29% (or 
25 out of 85) dispute this impact. 

Responding  business  associations  representing  in  particular  SMEs  tend  to  agree  even  more 
strongly  that  legal  fragmentation  would  lead  to  additional  costs,  reduced  investments  and  cross-
border activities, higher prices and insurance premiums, as well as a negative impact on the roll-
out  of  AI.  For  instance,  7  of  these  respondents  agreed  that  legal  fragmentation  can  entail 
additional costs for companies (e.g. legal information costs, increased insurance costs) when 
producing,  distributing  or  using  AI-enabled  products  or  services,  while  only  1  of  them 
disputed this. The same trend was confirmed by the responding individual SMEs. 

79 

 
 
 
 
  
  The results regarding legal fragmentation and its impacts confirmed the economic analysis 
commissioned  for  this  impact  assessment  (see  Annex  4  for  details),  and  were  taken  into 
account in the problem definition in this IA. 

3.2.  Need for EU action 
The public consultation results showed clear overall support for EU action on AI liability. When 
asked to rank policy options by order of preference from 1 (most preferred) to 8 (least preferred), 
respondents gave by far the lowest average score to the option ‘No EU action’ (baseline).225 There 
was particularly strong support for EU action amongst consumer organisations and NGOs, almost 
all  of  which  chose  ‘No  EU  action’  as  their  least  preferred  option.  Likewise,  EU  citizens  clearly 
support EU action.226 

The following chart shows that: 

-  only 14% of respondents supported No EU Action; 
-  only  11%  expressed  a  preference  for  addressing  AI-specific  issues  exclusively  by  easing 

the burden of proof under the PLD; 

-  75% of respondents preferred one of the policy options involving harmonised measures to 

adapt also national liability rules to AI: 

Need for EU action- all stakeholders

13,6%

11,2%

Support for EU measures on AI
liability beyond PLD review
(Options 2-7)

Support for addressing AI liability
issues only through PLD review
(Option 1)

Preference for No EU action
(Option 8)

75,3%

While support for EU action on AI liability was consistently strong across responding EU citizens, 
consumer  organisations,  NGO  and  research  institutes,  it  was  a  bit  less  pronounced  amongst 
business  stakeholders  in  particular.  Nevertheless,  as  shown  by  the  following  graph,  while  a 
preference  for  ‘No  EU  Action’  was  considerably  more  widespread  amongst  these  respondents 
(32 %),  the  largest  share  nevertheless  expressed  a  preference  for  one  of  the  policy  options 
involving harmonised measures to adapt also national liability rules to AI: 

225 Average score of 5,87 on a scale of 1 (most preferred) to 8 (least preferred), with the second least popular option 
having an average score of 4,44, and the best average being 4,03. 
226 Only 2 responding citizens chose ‘No EU action’ as their preferred option, and this option scored an average 7,56 
on the scale from 1 (most preferred) to 8 (least preferred). 

80 

 
 
 
 
 
 
 
 
 
                                                           
Need for EU action - businesses' views

32%

17%

51%

Support for EU measures on AI
liability beyond PLD review
(Options 2-7)

Support for addressing AI liability
issues only through PLD review
(Option 1)

Preference for No EU action
(Option 8)

  The  results  of  the  dedicated  public  consultation  are  aligned  with  the  trend  indicated 
already by the public consultation on the AI White Paper. This confirmation was taken into 
account in particular in the context of the proportionality and subsidiarity assessment in 
this IA. 

3.3. Policy options 

(a) AI-specific measures to ease the burden of proof under national civil liability rules 

The  public  consultation  showed  overall  support  for  targeted  EU  measures  to  ease  the  victim’s 
burden of proof under existing national liability rules: 

- 

-  More than two third of respondents (67%) agreed that the defendant (e.g. producer, user, 
service provider, operator) should be obliged to disclose necessary technical information 
(e.g. log data) to the injured party to enable the latter to prove the conditions of the claim 
(v.  only  14%  who  disagreed  with  this  policy  option).  Consumer  associations  and 
research  institutions  unanimously  supported  this  option  and  EU  citizens  were  also 
strongly in favour of it. Amongst business stakeholders, a slightly bigger share (33%, or 
28 out of 86) agreed with this policy approach than disagreed (31% or 27 out of 86). 
In connection with the previous question, 57% of respondents agreed that if the defendant 
refuses  to  disclose  relevant  information,  courts  should  infer  that  the  conditions  to  be 
proven by that information are fulfilled (v. 33% who disagreed with this). This option 
received strong support from responding NGOs + consumer organisations (61% support v. 
17% disagreement) and EU citizens (84%, or 77 out of 92) support v. 13%, or 12 out of 92, 
disagreement). By contrast, business stakeholders were more sceptical in this regard: close 
to two thirds (63% or 54 out of 86) of these respondents were opposed to such a rule. 
-  51% (or 114 out of 225) of respondents consider that if the provider of an AI system 
failed  to  comply  with  their  safety  or  other  legal  obligations  to  prevent  harm  (e.g. 
those proposed under the proposed AI Act), courts should infer that the damage was 
caused due to that person’s fault. While all the other stakeholder categories support this 
approach, with consumer associations supporting it unanimously, businesses were more 
split: 30%, or 26 out of 86, agreed with his option and 41%, or 36 out of 86, disagreed. 
-  The  option  to  apply  a  similar  mechanism  (presuming  causality  in  the  case  of  non-
compliance with AI Act) to users of AI systems, received more support from businesses 

81 

 
 
 
 
 
(35%, or 30 out of 86, agreed, while 29%, or 25 out of 86, disagreed) and was supported 
also by a clear majority of the other stakeholder categories. 

-  63  %  (or  142  out  of  226)  of  overall  respondents  consider  that  if,  in  a  given  case,  it  is 
necessary  to  establish  how  a  complex  and/or  opaque  AI  system  (i.e.  an  AI  system  with 
limited  transparency  and  explainability)  operates  in  order  to  substantiate  a  claim,  the 
burden of proof should be shifted from the victim to the defendant in that respect (v. 
27% (or 61 out of 226) who disagreed with this option). While about a quarter of business 
stakeholders also supported this approach, a clear majority (63% or 54 out of 86) disagreed 
with it. 

The  position  papers  submitted  in  the  consultation  revealed  similar  trends:  representatives  of 
consumer organisations, citizens and NGOs agree that victims should not bear the burden of proof 
and  there  is  a  need  for  an  alleviation  while  opinions  vary  about  how  this  could  be  done  (for 
example some consider that access to information is not helpful as the victim would not be able to 
understand and use it). Business organisations and companies usually do not favour a shift of the 
burden  of  proof  or  state  that  sufficient  alleviations  can  be  granted  by  courts  under  the  various 
national  laws.  In  addition,  some  of  these  stakeholders  caution  against  an  obligation  to  make 
technical  information  available  to  the  victim,  because  of  intellectual  property  considerations. 
Academia recognises the difficulties in finding the right balance and is suggesting, for example, to 
focus  on  further  defining  duties  of  care  or  introducing  rebuttable  presumptions  for  breaches  of 
such duties of care. 

  The overall support for  measures to  ease the burden of  proof showed that the envisaged 
options  go  into  the  right  direction.  In  particular,  responses  from  business  stakeholders 
reinforced  the  Commission’s  approach  to  focus  not  only  on  producers  but  also  other 
responsible actors in the AI value chain (in particular users of AI systems). The feedback 
received during the public consultation also allowed for a fine-tuning of the policy options 
covered  by  this  IA.  For  instance,  the  intellectual  property  concerns  raised  by  businesses 
stakeholders will be taken into consideration when conceiving provisions on the disclosure 
of  information  to  be  recorded/logged  pursuant  to  the  AI  Act.  In  addition,  the  results 
the 
showed 
perspective  of  a  potentially  liable  party,  which  is  in  line  with  the  PLD’s  approach  to 
exclude property damage suffered by businesses or economic loss from its scope. However, 
the  AI-specific  issues  covered  by  this  IA  can  affect  also  businesses  suffering  AI-induced 
harm,  in  particular  SMEs.  The  explanations  in  the  problem  definition  and  the  policy 
options have been strengthened to clarify this angle for business stakeholders.  

liability-related  questions  primarily 

that  businesses  considered 

from 

(b) Strict liability and mandatory insurance 

EU citizens, NGOs and research institutions were strongly in favour of harmonising strict liability 
for operating AI-enabled products and providing AI-enabled services, limited to cases where these 
activities pose serious injury risks to the public: 

-  EU  citizens:  54%  (or  47  out  of  88)  agreement  with  minimum  harmonisation  of  strict 

liability; 77% (or 68 out of 88) agreement with full harmonisation; 

-  NGOs + research institutions: 73% (19 out of 26) agreement with minimum harmonisation 

of strict liability; 69% (18 out of 26) agreement with full harmonisation. 

They also tended to agree with laying down a harmonised insurance obligation, where it does not 
exist yet, covering such a strict liability regime.  

82 

 
 
 
 
 
 
 
 
Consumer  organisations  supported  strict  liability  only  in  the  form  of  minimum  harmonisation, 
allowing  Member  States  to  maintain  broader  and/or  more  far-reaching  national  strict  liability 
schemes  applicable  to  other  AI-enabled  products  and  services  (4  agreed  with  minimum 
harmonisation  of strict liability v. 3 that disagreed;  6 disagreed with  full  harmonisation  whereas 
none agreed with this approach).  

Written  contributions  submitted  by  citizens,  consumer  organisations,  NGOs  and  research 
institutions  were  not  very  clear  on  the  strict  liability  option;  the  ones  that  supported  it  did  not 
provide much input or referred to the need to exempt consumers from strict liability, to take risks 
and circumstances of use into account or to allow national law to have in place more favourable 
regimes. 

The survey results showed that business stakeholders tend to disagree with the harmonisation of 
strict liability and mandatory insurance, but their disagreement was stronger as regards a minimum 
harmonisation approach to strict liability (70% opposition (59 out of 84) v. 14% support (12 out of 
84) than a full harmonisation approach (42% disagreement (35 out of 84) v. 30% agreement (28 
out  of  84)).  In  the  position  papers,  business  associations  and  companies  almost  universally 
opposed  a  harmonisation  of  strict  liability,  for  various  reasons:  some  consider  that  AI  does  not 
warrant  strict  liability  in  general  as  not  being  more  dangerous  than  other  technologies,  others 
consider  that  it  is  too  soon,  as  uses  of  AI  that  would  pose  serious  risks  are  not  yet  here  and 
harmonising  it  would  deter  innovation,  while  others  believe  that  existing  national  strict  liability 
regimes are sufficient. The few representatives of this category that supported strict liability had 
done so to the extent it would be clearly limited to a few high-risk cases, if the heads of claims 
would be restricted or if clear exceptions would also be harmonised. 

  Stakeholders’  diverging  views  on  the  harmonisation  of  strict  liability  and  mandatory 
insurance for AI fed into the design and assessment of the policy options covered by this 
IA. The results  namely confirmed the importance of  achieving  a balanced distribution  of 
risks  by  following  a  targeted  and  strictly  proportionate  approach,  focused  only  on  AI-
systems with the most critical risk profile. In addition, business stakeholders’ reservations 
regarding the need for harmonised strict liability rules and the technological scope of such 
a regime were aligned with the policy option involving a ‘staged approach’, whereby an 
EU  initiative  would  be  initially  limited  to  targeted  measures  easing  the  burden  of  proof 
under  fault-based  liability  rules,  and  the  need  for  a  harmonised  strict  liability  regime 
would be re-assessed at a later stage, when more evidence on possible liability gaps and 
the relevant AI-enabled products and services is available. 

(c) Types of compensable harm 

Stakeholders were asked whether an EU instrument on AI liability should harmonise the types of 
harm that give rise to a compensation claim when caused by AI. Here again, views varied widely 
between different stakeholder categories.  

Amongst  EU  citizens,  consumer  associations,  NGOs  and  research  institutions  there  was  clear 
support  for  EU  rules  prescribing  the  compensability  of  pure  economic  loss  (e.g.  loss  of  profit), 
loss  of  or  damage  to  data  (not  covered  by  the  GDPR)  and  immaterial  harm  (like  pain  and 
suffering, reputational damage or psychological harm). 

By  contrast,  business  stakeholders  were  mainly  opposed  to  such  rules.  Their  opposition  was 
strongest regarding the compensability of immaterial harm (63% (or 52 out of 83) disagreement v. 
23% (or 19 out of 83) support) and of data loss/damage not resulting in a verifiable economic loss 
(75% (or 63 out of 84) opposition v. 12% (or 10 out of 84) support). This picture is confirmed also 

83 

 
 
 
 
 
 
by  written  contributions  submitted  by  business  stakeholders.  They  namely  emphasised  the  very 
diverse approaches under existing national rules and the concern that harmonisation for AI-related 
damage would create compares to cases not involving AI. 

  Stakeholders’ opinions contributed to shaping the preferred policy option as presented in 
this  IA.  The  widespread  calls  for  ensuring  the  compensation  of  various  types  of  harm 
support the Commission’s holistic approach to look not only at the existing EU instrument 
(the PLD) – which covers only damage caused by physical harm or damage to individual 
private  property.  Under  existing  national  civil  liability  rules,  victims  can  claim 
compensation  also  for  other  types  of  harm  (including  e.g.  immaterial  harm,  damage  to 
business property, etc.). This is why it is crucial to ensure that victims have the same level 
of protection also under those national rules. At the same time, the Commission took into 
consideration  the  concerns  raised  by  (primarily  business)  stakeholders,  who  pointed  out 
that a harmonisation of the types of compensable harm only with respect to cases where AI 
causes damage could lead to an internal fragmentation within national legal systems, and 
inconsistent outcomes.  

  In  light  of  these  results,  it  was  determined  that  the  most  coherent  and  proportionate 
approach is to ensure that victims are not less protected under existing national liability 
rules  when  AI  causes  damage,  but  abstain  from  harmonising  the  types  of  compensable 
harm in an AI-specific instrument 

C. 

Other consultation activities 

1.  Public  consultation  launched  by  the  White  Paper  on  AI  and  the  Commission  report  on 
safety and liability 

An  open  16  weeks  web-based  public  consultation  ran  from  February  19  to  June  14,  2020.  The 
objective  of  the  consultation  was  to  collect  views/opinions  on  the  White  Paper  on  Artificial 
Intelligence  -  A  European  Approach  to  excellence  and  trust,  COM(2020)  65  and  on  the 
Commission Report on the safety and liability implications of Artificial Intelligence, the Internet 
of  Things  and  robotics,  COM  (2020)  64.  The  public  consultation  resulted  in  more  than  1200 
responses from all categories of stakeholders from across the EU. 

63 % of respondents to the open public consultation on the AI White Paper in 2020 were in favour 
of  adapting  national  liability  rules  for  all  or  for  specific  AI  applications  to  better  ensure  proper 
compensation and a fair allocation of liability. In that context, citizens were particularly supportive 
with  72%  of  respondents  calling  for  some  adaptations.  While  the  overall  support  among 
companies was 45%, it was higher among SMEs with 60% of respondents. 

  These results confirmed the need to examine specifically the need for EU policy measures 
on  AI  liability,  which  was  taken  up  in  the  further  evidence  gathering  and  impact 
assessment activities. 

2. Online interactive webinars 

Twelve  webinars  were  organised  from  8  to  12  June  2020  for  a  total  of  18  hours  of  interactive 
discussions with stakeholders. The webinars had the objective to inform the Commission as part of 
the consultation launched by the AI White Paper and to encourage stakeholders to participate in 
the public consultation.  

211 stakeholders registered and 170 participated. Many stakeholders’ categories were represented: 
the  majority  were  from  the  tech  industry,  including  medical  tech  companies,  producers  and 

84 

 
 
 
 
software developers. Organisations representing consumers were also well represented as well as 
academia  and  legal  professions.  Interested  citizens  and  non-European  stakeholders  also 
participated. A successful effort in the communication strategy was put in place to make sure that 
the most important organisations representing SMEs and start-ups participated in the discussions.  

The  webinars  covered  three  main  topics  related  to  liability:  how  to  define  high-risk  AI 
applications, who should be held  strictly liable and possible measures to  help  victims in  case of 
fault-based rules.  

2.1. Webinar 1: Risk-based approach: How should high-risk and non-high-risk AI applications be 
determined? 

Stakeholders provided feedback on the risk based-approach presented in the AI White Paper and 
on  the  possible  criteria  to  identify  the  AI  applications  with  a  specific  risk  profile.  Several 
stakeholders asked for consistency on what is high risk in the different dimensions presented in the 
WP (fundamental rights, safety and liability). However, a slight differentiation could be justified 
with respect to strict liability, which should have a narrower scope.  

  These insights were taken into account when developing the policy options. In particular, 
the need for consistency with the other strands of the Commission’s AI policy is reflected 
by  the  fact  that  the  policy  options  on  easing  victims’  burden  of  proof  are  closely  linked 
with the AI Act. Moreover, the call for a narrow scope of a possible strict liability regime 
was taken into account for designed the strict liability element of Policy Option 2.  
A European organisation representing consumers stressed that consumers should be entitled to an 
effective redress mechanism and access to justice irrespective of the risk level. Another consumer 
organisation  stated  its  preference  for  a  precautionary  approach  taking  into  account  how  new  an 
application is and how little it is known about the risks it can raise. Interests of minorities should 
also be considered.  

  This  feedback  was  consistent  with  the  Commission’s  objective  to  ensure  that,  whichever 
route to compensation victims of harm caused by AI choose (fault-based, strict or PLD), 
they need to enjoy the same level of protection as persons having suffered harm caused by 
other technologies. 

A number of participants emphasises that a possible liability instrument would need to fit into the 
current framework. The need for a mandatory insurance should also be carefully considered. An 
organisation  representing  start-ups  added  that  strict  liability  for  high-risk  AI  applications  could 
stifle innovation. The same representative also stated that any legislation put forward needs to be 
easy for  companies to  understand. Compliance is difficult  for many start-ups. Other participants 
emphasised that any policy measures should be based on empirically established issues to address 
real  gaps.  Some  stakeholders  representing  different  interests  argued  that  the  level  of  autonomy 
should  be  considered  for  the  criteria  to  define  AI  applications  with  a  particular  risk  profile.  A 
greater degree of autonomy leads to a higher risk. Another element to consider is whether the AI 
system merely supports the human decision or replaces it. While participants largely agree that the 
AI  currently  on  the  market  is  merely  assisting  human  decisions,  it  was  highlighted  that  some 
challenges might arise in those cases where AI is autonomous and continues to develop once it is 
put  on  the  market.  The  AI  could  for  example  learn  to  act  outside  previously  awarded  safety 
certificates. 

  This  input  was  taken  into  account  in  developing  the  policy  options.  In  particular,  the 
measures  were  designed  in  a  flexible  manner  to  fit  seamlessly  in  MS’  existing  liability 
frameworks.  Moreover,  the  staged  approach  was  developed  to  support  an  empirical  and 
evidence-based  approach  to  AI  liability,  in  particular  when  it  comes  to  the  more  far-
reaching measures such as strict liability and mandatory insurance. 

85 

 
 
2.2. Webinar 2: Strict liability of the operator of high-risk AI applications: Who is the operator in 
that case? 

The majority of the speakers agreed in principle that the liable person could be defined based on 
the  criterion  of  control.  Some  stakeholders  representing  tech  companies  mentioned  that  the 
criterion  of  “economic  benefit”  should  also  be  considered,  particularly  in  the  B2B  context.  In 
addition, it was stated that the person who is closest to the operations would probably be the best 
addressee for liability. Some stakeholders were also in favour of joint and several responsibility, 
with the possibility to take redress along the supply chain. A number of stakeholders representing 
different  interests  were  of  the  opinion  that  in  the  case  of  strict  liability  there  is  a  need  for 
mandatory  insurance  and  caps  are  important,  otherwise  it  would  be  difficult  for  the  insurers  to 
calculate  the  coverage.  Medtech  representatives  supported  caps  in  the  medical  industry. 
Mandatory insurance should be without prejudice to developers’ claim against the players in the 
upstream  value  chain.  Stakeholders  representing  the  automobile  industry  voiced  scepticism 
regarding the idea of regulating liability caps at EU level because Member States do not calculate 
damage the same way. A lawyers’ association brought the counter-argument that different caps in 
different MS could result in forum shopping. Representatives from industry and legal professions 
took the view, with some variations, that exceptions/defences are important, namely: 

-  Force  majeure  -  Several  speakers  were  of  the  opinion  that  an  exception  from  liability  in 

unforeseen circumstances should be applicable  

-  Contributory damage – most speakers were also in favour of contributory damage (e.g. in 

cases where the victim doesn’t carry out a necessary update); 

-  Cybersecurity attacks/3rd party interference  – several speakers were in favour of such an 
exception. However, some industry  representatives argued against an  absolute exception, 
but  in  favour  of  a  defence  allowing  the  liable  person  to  demonstrate  that  he  or  she  has 
complied with cyber standards; 

-  Open source AI – Some representatives from  academia suggested that the liability of the 

developer of open source AI should be limited; 

-  A few stakeholders mentioned the misuse of AI by a user. 

By  contrast,  representatives  of  consumer  interests  spoke  against  any  kind  of  defence,  caps, 
threshold  or  other  exceptions,  because  digital  goods  expose  consumers  to  particular  difficulties 
when it comes to claiming compensation. 

  All of this feedback was taken into consideration when developing and assessing the policy 
options.  In  particular,  participants’  input  regarding  the  detailed  elements  of  a  possible 
strict  liability  and  mandatory  insurance  regime  fed  into  the  design  of  policy  option  2. 
Moreover,  the  importance  of  taking  into  account  redress  possibilities  for  liable  persons 
was reflected in the IA. 

2.3. Webinar 3: Burden of proof for AI applications other than those with a specific risk profile: 
How  can  it  be  made  easier  for  victims  of  damage  caused  by  such  applications  to  prove  their 
claim? 

Representatives of  consumer interests emphasised the  crucial importance of the burden of proof 
for all AI products, stressing that it is it is almost impossible for the consumer to prove fault and 
even harder to prove causality. Consumers should have to prove only the existence of a damage, 
because  the  prevailing  information  asymmetry  between  consumers  and  professionals  justifies  a 
reversal of the burden of proof with respect to all other liability conditions. These arguments were 
seconded by some representatives from academia, who stated that the difficulty of proving fault is 

86 

 
 
 
particularly relevant in discrimination cases. An organisation representing businesses, in particular 
SMEs, added that a reversal of the burden of proof was necessary not only for consumers but also 
for companies that suffer harm.  

By contrast, several representatives of the tech and software industry argued against a reversal of 
the burden of proof for AI, questioning the need to  deviate from the general rules. One of these 
discussants  stressed  that  providers  of  AI-services  or  products  should  not  be  required  to  prove  a 
negative fact (i.e. that they did not commit a faulty act). Large software companies argued that a 
reversal of the burden of proof was a very sharp tool and represented a significant burden for the 
defendant.  Therefore,  such  a  tool  should  only  be  considered  on  a  case-by-case  basis  if  it  is 
prohibitively  difficult  for  the  victim  to  prove  a  claim.  Representatives  of  tech  companies 
submitted that the claimant should in any case be required to provide at least prima facie evidence 
that the defendant did something wrong. Representatives  of manufacturing companies stated that 
a  reversal  of  the  burden  of  proof  should  apply  only  under  the  condition  that  it  is  impossible  to 
ascertain  the  facts  of  the  case,  for  instance  in  the  case  of  traffic  accidents.  Legislative 
modifications of the burden of proof should be limited in scope based on the risk and the need for 
protection. 

  These diverging views were taken into consideration to develop targeted measures to ease 
the burden of proof (only) to the extent necessary to ensure that victims of harm caused by 
AI  enjoy  the  same  level  of  protection  as  persons  having  suffered  harm  caused  by  other 
technologies,  in  strict  alignment  with  the  principle  of  proportionality.  For  instance,  the 
policy  options  do  not  envisage  a  general  reversal  of  the  burden  of  proof,  which  was 
criticised as unjustified by a number of stakeholders, but a targeted alleviation only with 
respect to the question how or why an AI system arrived at a certain output.  

3. Feedback on inception impact assessment 

Thirty-four stakeholders submitted feedback throughout the feedback period (30th June 2021 – 28th 
July  2021).  Many  categories  were  represented.  The  majority  of  the  stakeholders  came  from  the 
tech  industry,  including  medical  tech  companies  and  software  developers.  Several  NGOs 
submitted  feedback,  as  well  as  some  organisations  representing  consumers.  EU  citizens,  public 
authorities and academic institutions also participated. 

  Stakeholders  from  different  sectors  drew  attention  to  the  need  for  consistency  with  the 
current  and  up-coming  legislation  (e.g.  PLD  and  its  possible  revisions,  AI  Act).  This 
feedback was taken into account, firstly, by aligning the timing of the planned review of the 
PLD with the proposal on AI Liability to ensure consistency and complementarity. While 
the PLD review aims at ensuring the effectiveness of its horizontal and technology neutral 
rules  for  claims  against  producers  based  on  defects,  it  has  to  be  combined  with  the 
measures  assessed  in  this  IA  to  ensure  that,  whichever  route  to  compensation  victims  of 
harm  caused  by  AI  choose  (fault-based,  strict  or  PLD),  they  enjoy  the  same  level  of 
protection  as  persons  having  suffered  harm  caused  by  other  technologies.  Secondly,  the 
AI-specific measures to ease the burden of proof are closely linked to the AI Act, and the 
definitions of the AI Act are taken over to ensure consistency. 

Some  stakeholders  support  a  minimum  harmonisation  or  an  approach  based  on  non-binding 
measures. 

  These  points  were  reflected  in  the  further  policy  development,  namely  by  assessing 
possible  non-binding  measures  (e.g.  a  recommendation)  and  by  designing  the  envisaged 
measures as a minimum harmonisation instrument. 

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A number of stakeholders urged for a risk-based approach, stressing that AI applications ought to 
be treated according to the specific risk each of them poses.  

  This  feedback  was  aligned  with  the  Commission’s  risk-based  approach,  which  the  AI 
liability initiative notably implements by referring to the risk-based measures of the AI Act. 
In order to provide legal certainty and avoid fragmentation among MS, several stakeholders from 
different  sectors  favour  a  broader  harmonisation  of  rules  concerning  extra-contractual  liability. 
Some industry stakeholders argued that the producer should not  be burdened with  strict liability 
for damage caused by AI, as it would be impossible for him/her to anticipate every conceivable 
use  of  the  product  or  service.  This  is  because  AI  applications  can  be  employed  in  unlimited 
scenarios  and  can  even  be  misused  or  used  in  ways  not  specified  by  or  foreseeable  for  the 
producer.  

  These  views  confirmed  the  Commission’s  holistic  approach  looking  at  all  routes  to 
compensation currently available to ensure that, whichever route victims of harm caused 
by  AI  choose  (fault-based,  strict  or  PLD),  they  enjoy  the  same  level  of  protection  as 
persons having suffered harm caused by other technologies. The call to focus not only on 
the  producer  supported  in  particular  the  Commission’s  approach  to  look  also  at  the 
liability of AI users. 

Many stakeholders from NGOs and consumers’ organisations expressed support for an alleviation 
of the burden of proof, in order to overcome challenges typical of AI which make it difficult for 
the  injured  parties  to  get  compensation  (such  as  autonomous  behaviour,  continuous  adaptation, 
limited  predictability  and  opacity).  Another  aspect  that  makes  the  burden  of  proof  particularly 
heavy for the injured parties is their lack of technical information. Within the tech industry, while 
some stakeholders are generally in favour of a promotion of the exchange of technical information 
throughout the value chain to ensure that AI systems remain predictable, others cautioned that no 
obligation  to  disclose  technical  information  should  be  imposed,  if  it  infringes  the  intellectual 
property or legitimate confidentiality interests of  the producer. An EU  citizen expressed support 
for  the  introduction  of  an  obligation  to  disclose  technical  information,  whereas  an  NGO  argued 
that such an obligation, although generally desirable, would not be sufficient to educate consumers 
about whether or not the AI system is defective in their case. 

Several  stakeholders support a reversal  of the burden of proof, whereas the majority of industry 
representatives  opposed  it,  arguing  that  it  would  deter  innovation.  However,  an  NGO  counter-
argued  that  such  a  reversal  would  incentivise  producers  to  invest  more  in  preventing  defects, 
which would in turn increase the customers’ willingness to pay higher prices for products that they 
reasonably  trust  to  be  safe.  Finally,  some  companies  caution  against  a  blanket  reversal  of  the 
burden of proof, since such a tool should only be applied to very specific cases as a measure of 
last resort. 

  These diverging views were taken into consideration to develop targeted measures to ease 
the burden of proof (only) to the extent necessary to ensure that victims of harm caused by 
AI  enjoy  the  same  level  of  protection  as  persons  having  suffered  harm  caused  by  other 
technologies,  in  strict  alignment  with  the  principle  of  proportionality.  For  instance,  the 
policy  options  do  not  envisage  a  general  reversal  of  the  burden  of  proof,  which  was 
criticised as unjustified by a number of stakeholders, but a targeted alleviation only with 
respect to the question how or why an AI system arrived at a certain output.  

Finally, medtech companies maintained that, whenever AI is employed in the healthcare system, it 
is  only  used  as  a  supporting  tool,  while  the  final  decision  rests  with  the  healthcare  professional 
who should then ultimately be responsible for his/her choices. 

88 

 
 
  This  feedback  confirmed  the  Commission’s  analysis  according  to  which  the  specific 
challenges  of  AI  are  relevant  (only)  in  situations  where  autonomous  AI  systems  are 
interposed  in  the  causal  chain  between  a  human  action/omission  and  the  damage.  The 
envisaged  policy  options  are  therefore  designed  to  help  victims  in  such  situations  rather 
than in cases (like med tech) where AI systems only a advise a human decision maker. 

4. Surveys and targeted interviews carried out in the framework of two supporting studies 
(general economic and behavioural analysis) 

Two  external  studies  commissioned  to  inform  this  IA  contributed  to  the  consultation  of 
stakeholders:  

- 

- 

an economic analysis of AI-specific problems affecting current civil liability rules227; 

a  behavioural  economics  study  investigating  the  link  between  liability-related  problems 
and  policy  measures  and  societal  trust  in  /  consumer  uptake  of  AI-enabled  products  and 
services228. 

The  methodology  implemented  by  these  studies  is  described  in  more  detail  in  Annex  4.  The 
following summary is limited to the tasks relevant as part of the consultation activities for this IA. 

4.1. Economic study 

-  Consultation  activities  to  collect  data  on  the  costs  and  time  required  to  claim 
compensation  under  current  liability  rules,  as  a  means  of  approximating  victims’ 
difficulties  in  claiming  compensation  due  to  the  specific  characteristics  of  AI:  The 
study team  consulted legal  experts in  13 Member States and the  UK,  with  long-standing 
experience in civil  liability cases  related to  information  and communication technologies 
and novel technologies. The legal experts estimated the costs and time required by lawyers 
and  technical  experts  in  “traditional”  cases  of  damage,  as  well  as  in  cases  involving  AI 
applications.  The  data  collection  was  complemented  by  desk  research.  The  results 
confirmed  that  AI  can  make  it  substantially  more  difficult  for  victims  to  claim 
compensation (see Annex 4 for more detailed explanations). 

-  Consultation  activities  to  collect  data  on  the  impact  of  liability-related  legal 
uncertainty and fragmentation on the  roll-out of AI:  The study team  principally  used 
two  methods  to  collect  data  for  this  task:  (i)  A  survey  targeted  to  European  trade 
associations  and  companies  with  activities  linked  to  AI,  and  in  particular  the  operation, 
production,  development  of  AI  systems  in  relation  to  the  six  selected  use  cases;  (ii) 
Interviews with trade associations, companies and experts. The interviews were conducted 
in  a  semi-structured  manner,  with  an  overall  list  of  questions  shared  in  advance  and  the 
possibility  to  address  also  different  topics  during  the  interviews.  The  findings  supported 
the consultant’s overall conclusion that legal uncertainty and fragmentation regarding civil 
liability for AI entail significant internal market barriers 

-  Consultation activities to assess the impact of preliminary policy options: Here as well, 
the study team used a combination of survey and interviews. In order to receive targeted 

227  Deloitte,  Study  to  Support  the  Commission’s  Impact  Assessment  on  Liability  for  Artificial  Intelligence,  2021 
(‘Economic Study’). 
228  Kantar/Behavia/CEPS,  Behavioural  study  on  the  link  between  challenges  of  Artificial  Intelligence  for  Member 
States’  civil  liability  rules  and  consumer  attitudes  towards  AI-enabled  products  and  services,  December  2021 
(‘Behavioural Economics Study’). 

89 

 
 
 
 
                                                           
data  across  different  relevant  stakeholder  groups,  the  survey  was  designed  in  three 
different  versions:  (i)  for  operators  of  AI-enabled  products  and  providers  of  AI-enabled 
services  (including  companies  that  are  at  the  same  time  manufacturers/developers  of  AI-
systems);  (ii)  for  the  legal  experts  consulted  also  on  the  cost  of  claiming  compensation 
under the current liability rules, to  ascertain whether the preliminary policy options were 
suitable to improve victims’ situation; (iii) for insurance companies. The results supported 
the  consultant’s  conclusion  that  policy  measures  to  ease  the  burden  of  proof,  in 
combination  with  a  limited  harmonised  strict  liability  regime  for  AI,  would  significantly 
increase legal certainty, prevent fragmentation, and thus have a significant positive effect 
on the functioning of the internal market in AI-enabled products and services. 

-  The  study  team  organised  and  hosted  a  webinar  during  the  month  of  October  2020.  The 
webinar enabled over 40 participants from  various industrial  sectors to  provide views on 
the preliminary findings of the study and to debate the major research questions (impact of 
legal uncertainty, future legal fragmentation, and preliminary policy options). The webinar 
allowed  the  study  team  to  confirm  and  adjust  the  findings  and  provided  useful  input  to 
orientate the subsequent data collection activities. 

4.2. Behavioural economics study 

In the framework of the behavioural economics study, the consultant sought stakeholder feedback 
was through the following activities: 

- 

In-depth  interviews  with  consumers,  which  helped  to  deepen  the  understanding  of  the 
behavioural  dynamics  that  link,  on  the  one  hand,  societal  acceptance  of  AI  applications, 
consumers’ trust  and their willingness  to  take up AI applications  and, on the other hand, 
civil liability rules. The insights derived allowed the consultant to fine-tune the design of 
the  survey  and  of  the  survey-based  experiment,  while  also  partly  complementing  their 
results.  The  interviews  were  carried  out  individually  with  18  consumers  from  three 
countries. 

-  An online survey carried out on representative samples of the adult population from eight 
countries (Denmark, Netherlands, Ireland, France, Germany, Italy, Poland, and Romania), 
with a sample size of at least 1,000 respondents per country, amounting to a total of 8,079 
respondents  across  the  eight  countries.  The  sample  for  each  country  was  a  quota-based 
one,  with  additional  demographic  weights  being  computed  and  appended  to  the  data  in 
order to correct any potential distortions of the characteristics of the respondents within the 
sample from the characteristics of the population from which the sample was extracted. 

The results of the behavioural survey and experiments showed that the perception of liability rules 
is  one  of  the  factors  shaping  societal  trust  and  consumers’  willingness  to  take  up  AI-enabled 
products and services (see Annex 4 for a more detailed summary of findings). 

5. Workshop with Member States 

A workshop with representatives of MS’ governments was organised on 1 February 2022, with a 
view  to  gathering  MS’  views  and  input  on  the  problem  definition  and  policy  options  before 
finalising this  IA. Most MS did not express a clear position yet, but some conveyed preliminary 
observations. 

-  Need  for  EU  action:  Some  MS    formulated  reservations  regarding  the  need  for 
harmonisation  of liability  for  AI, considering notably that existing national  liability rules 
are sufficiently flexible to deal with the specific challenges of AI. 

90 

 
 
 
 
 
  This  feedback  was  taken  into  account  by  re-assessing  and  strengthening  the  problem 
analysis  in  this  IA.  Moreover,  the  policy  options  were  designed  specifically  to  strike  a 
delicate balance between, on the one hand, addressing the legal uncertainty and internal 
market  obstacles  linked  precisely  to  the  far-reaching  flexibility  in  applying  existing 
national  liability  rules,  and  on  the  other  hand,  MS’  legitimate  interest  in  preserving  the 
consistency  of  their  national  private  law  systems.  The  Commission’s  targeted  minimum 
harmonisation  approach  strikes  this  balance,  and  the  staged  approach  was  designed  to 
ensure  that  comprehensive  empirical  evidence  is  available  before  deciding  on  the  more 
far-reaching measures (strict liability and mandatory insurance). 

-  Types of AI to be covered: Several MS favoured a narrow definition of AI, e.g. focused 

on highly autonomous and/or opaque AI.  

  These views are aligned with the Commission approach of addressing only the particular 
challenges linked to the specific characteristics of certain types of AI systems (e.g. highly 
autonomous functioning and opacity) through targeted measures. 

Fault-based liability:  A few MS  were sceptical  about  harmonised measures to  ease the 
burden of proof under fault-based liability rules, or at least encourage a cautious approach. 
However, other MS were open to the adaptation of rules on access to information to help 
victims make a successful claim as well as the targeted adaptation of the burden of proof.  

  MS cautious stance vis-à-vis measures adapting national rules on the burden of proof was 

taken into consideration when fine-tuning the design of the envisaged measures.  

-  Some MS expressed the view that the types of damages compensable for harm caused by 
AI  ought  to  be  determined  at  national  level  or  that  the  EU  proposal  should  only  cover 
economic  loss,  whereas  the  rest  should  be  left  to  national  law.  Some  MS  oppose  the 
harmonisation of rules regarding contractual limitations or exclusions of liability while one 
MS would welcome such intervention at EU level. 

  These opinions correspond to the Commission’s assessment that the types of compensable 
harm and the admissibility of contractual limitations/exclusions of liability, not being AI-
specific questions, do not need to be harmonised in an instrument targeting specifically the 
challenges linked to the peculiar characteristics of AI. 

-  Strict liability: One MS deems it advisable to reassess the need for strict liability after the 
roll-out of AI-enabled products and services. According to another MS, if strict liability is 
considered, then a risk-based approach should be followed, although the definition of high 
risk  is  yet  to  be  determined.  Two  MS  opposed  a  harmonisation  of  strict  liability  in  their 
responses  to  the  public  consultation.  Some  MS  stated  at  the  workshop  that  the  initiative 
should not lower the level of protection provided by national law and that B2B, C2C and 
B2C relationships ought to be treated equally. 

  Member  States  cautious  stance  vis-à-vis  the  harmonisation  of  strict  liability  for  damage 
caused by AI was taken into consideration when designing the policy options covered by 
this  IA.  In  particular,  the  staged  approach  is  aligned  with  MS’  concern that  there  might 
currently  not  yet  be  a  sufficiently  robust  evidence  base  for  introducing  far  reaching 
measures such as strict liability coupled with mandatory insurance. 

91 

 
 
 
 
 
 
 
 
 
 
 
- 

Insurance:  MS  that  provided  input  are  of  the  opinion  that,  should  strict  liability  be 
introduced,  mandatory  insurance  should  also  be  considered.  However,  it  should  first  be 
evaluated  whether  the  insurance  sector  offers  such  coverage  on  the  market.  A  few 
additional  points  were  raised:  such  obligation  ought  to  be  enshrined  within  the  relevant 
national  legal  acts;  mandatory  insurance  might  cause  an  increase  of  premiums  and  it 
should be technology-neutral to ensure proportionality. 

 These points raised by Member States were taken into account in developing and assessing 
the  policy  options.  In  particular,  the  staged  approach  is  designed  specifically  to  ensure 
that the concrete market conditions (including available insurance coverage) defining the 
products  and  services  potentially  subject  to  strict  liability  are  empirically  established 
before deciding on the harmonisation of strict liability and mandatory insurance. 

92 

 
 
 
 
 
Annex 3: Who is affected and how? 

1.  PRACTICAL IMPLICATIONS OF THE INITIATIVE 

1.1. 

Implications for the most relevant stakeholder groups 

The preferred option has two elements: targeted alleviations of the burden of proof and a targeted 
review mechanism to re-assess the need for more far-reaching measures, including strict liability 
and possibly a mandatory insurance for the use of certain AI-technologies. Given that the review 
mechanism  would  not  have  practical  implications  for  stakeholders,  as  it  might  lead  only  in  the 
future  to  changes  of  the  proposed  Directive,  this  analysis  will  focus  on  the  implications  of  the 
targeted alleviations of the burden of proof. 

(a) Impacts on businesses (in particular as potentially liable parties): 

The implications for businesses would mainly concern an increased legal certainty and a reduced 
fragmentation.  It  will  be  easier  for  companies  to  estimate  liability  risks  and  the  related  costs. 
While under the baseline scenario courts might apply on an ad-hoc basis alleviations of the burden 
of proof to remedy what they consider an unequitable result, clear and harmonised alleviations of 
the  burden  of  proof  will  help  companies  know  what  to  expect  in  case  AI  is  involved  both 
domestically and cross-border. Although for what concerns cross-border relations Member States 
can  impose  more  stringent  measures  (for  instance  strict  liability),  companies  would  still  benefit 
from  reduced compliance costs compared to  the  very fragmented baseline scenario. Such clarity 
might  also  help  companies  get  more  appropriately  priced  liability  insurance  coverage.  The 
combined economic gains linked to these impacts have been approximated in terms of increases in 
the AI market size. For further explanations on this quantification of indirect economic benefits, 
see heading 1.2.(a) below and the cost-benefits table at the end of this Annex. 

These  benefits  outweigh  the  adjustments  costs  as  the  business-as-usual  costs  under  the  baseline 
scenario related to the uncertainty to assess what liability rule would apply to AI and what burden-
of-proof rule a court would apply in a concrete case are higher. These benefits also outweigh the 
redistribution effects linked to the alleviations of the burden of proof: an improved liability regime 
would allow shifting the costs of the damage born by the victim to the wrongdoer. However, this 
shift cannot be considered as an undesirable impact or undue burden. Firstly, it is in line with one 
of the fundamental justice-related purposes of liability law, i.e. to ensure that a person who harms 
another person in an illegal way will compensate the harm caused to the victim. Secondly, it is in 
line  with  the  Commission  policy  objective  to  ensure  that  victims  of  damage  caused  with  the 
involvement of AI systems have the same level of protection as victims of damage caused by other 
technologies.  Finally,  it  also  achieves  a  more  efficient  cost-allocation,  i.e.  to  the  person  having 
caused the damage/best  placed to prevent damage from occurring, instead of being borne by the 
victim.  

(b) Impacts on victims of damage caused by AI (natural persons and businesses)  

The targeted alleviations of the burden of proof would allow victims of damage caused by AI to 
have the same level of protection as victims of damage caused by other technologies. This means 
that it would be prevented that victims of AI induced damage would be left without compensation. 
Instead, the economic cost for compensating the harm occurred would be allocated to the person 

93 

 
 
who is indeed responsible for causing the damage. In addition, victims’ costs linked to the burden 
of proof would be reduced, including the costs of expert analysis, by ensuring access to relevant 
information and alleviating the victim’s burden of establishing how or why an AI-system arrived 
at a certain output.  

(c) Consumers 

The practical benefits for businesses would also have impacts on consumers because of the faster 
rollout of AI-technologies. In particular, consumers will benefit from faster and more personalised 
services,  innovative  and  performant  products  as  well  as  advances  in  the  fields  of  health,  safety, 
security, mobility, sustainability, circular economy, media, etc.  

(d) Insurance companies 

Targeted  harmonisation  and  adaptation  of  the  rules  on  the  burden  of  proof  may  lead  to  a  slight 
increase in the take-up of insurance policies by liable parties due to increased legal certainty and 
reduced  fragmentation.  These  aspects  increase  awareness  of  liability  risks  and  create  more 
favourable conditions to offer insurance policies.     

1.2. Broader economic and societal implications 

(a) Implications for the European economy 

The targeted and harmonised alleviations of the burden of proof would contribute to an efficient 
cost  allocation.  By  increasing  legal  certainly  and  reducing  fragmentation,  the  harmonised  rules 
would incentivise the rollout of AI and consumer trust. Its combined impacts are expected to have 
a positive effect on cross-border trade in AI-enabled products and services and the development of 
the  European  AI-sector  as  a  whole.229  The  economic  study  commissioned  for  this  impact 
assessment  estimated  that  a  combination  of  alleviations  of  the  burden  of  proof  and  measures  to 
harmonise  strict  liability  for  certain  AI-enabled  products  and  services  would  increase  the  cross-
border trade in the AI-enabled goods and services falling under the six use-cases analysed in depth 
for  that  study  by  about  5 %.  While  the  preferred  option  does  not  include  all  of  the  assumptions 
made  for  that  estimation  (the  need  for  strict  liability  would  be  assessed  at  a  later  stage),  it  is 
nevertheless relevant because the decisive drivers of the expected economic benefits  – increased 
legal certainty, reduced fragmentation and increased consumer trust – are likely to materialise also 
under targeted and harmonised alleviations of the burden of proof. Under the same assumptions, 
the expected increase in cross-border trade could translate to an increase by EUR 150 million of 
the  production  value  of  cross-border  trade  in  those  products  and  services  alone.230  While  these 
figures  give  some  conservative  indication  of  the  direction  of  economic  impacts,  they  do  not 
provide an estimate of their overall magnitude. As they do not take into account trade in products 
and  services  within  individual  MS  and  only  refer  to  the  six  specific  use-cases,  the  overall 
economic  impact  is  likely  to  be  significantly  larger,  because  the  overall  affected  market  size  is 

229 Economic Study, p. 196 and 197. 
230 Ibid., pp. 196-198. 

94 

 
 
                                                           
much  larger:  the  preferred  policy  option  is  estimated  to  generate  an  overall  increase  in  the  AI 
market size by ca. EUR 500 mln (low estimated value) to EUR 1.1 bln (high estimated value)231.    

(b) Environmental impacts  

On a general level, it is expected that AI-solutions can generate efficiencies and contribute to the 
innovation  of  environmentally  friendly  technologies.  By  contributing  to  an  increased  roll-out  of 
AI-enabled  products  and  services,  the  preferred  policy  option  might  hence  yield  certain  indirect 
environmental benefits. However, it is acknowledged that there is no sufficient basis for assessing 
such  effects  even  approximatively,  because  they  are  too  far  removed  from  the  envisaged 
adaptations of liability rules. 

(c) Impacts relating to the UN sustainable development goals 

By  incentivising  the  development  and  rollout  of  AI,  the  preferred  option  would  support  several 
targets232 across all the Sustainable Development Goals (SDGs). The Covid pandemic has already 
showed  the  potential  of  AI  to  contribute  to  Goal  3  on  good  health  and  well-being,  as  it  can 
contribute to  finding effective vaccines, detecting diseases via pattern  recognition  using medical 
imagery,  calculating  probabilities  of  infection,  or  providing  emergency  response  with  robots 
replacing humans for high-exposure tasks in hospitals233. AI can also be an enabler by supporting 
the provision of food, health, water and energy services to the population. It can also support low-
carbon  system  by  supporting  the  creation  of  circular  economies  and  smart  cities  that  efficiently 
use their resources234. Looking at SDGs related to the environment, especially Goal 13 on climate 
action,  there  is  evidence  that  AI  can  support  the  understanding  of  climate  change  and  the 
modelling of its possible impacts. For instance, AI techniques can help to identify desertification 
trends over large areas, information that is relevant for environmental planning, decision-making 
and management to  avoid  further desertification, or help  reverse trends by identifying the major 
drivers235.  

Since  the  present  initiative  aims  specifically  to  minimise  the  risks  related  with  the  use  of  AI 
systems as regards access to justice, it is expected to contribute to Goal 16, target 16.3, that seeks to 
promote the role of law at national and international levels and, especially, to ‘ensure equal access 
to justice for all’. In particular, this initiative addresses the potential risk of non-effective remedies 
for victims of harm caused by AI-enabled products and services by helping them meet their burden 
of  proof  on  the  difficult-to-prove  requirements  where  AI  is  concerned,  namely  fault,  defect  and 
causality.  The  preferred  policy  option  is  expected  to  contribute  to  the  rollout  of  AI  and  thus  to 
achieving the related SDGs and targets. It would also impact positively on them by contributing to 
the  enforcement  of  the  AI  Act,  because  effective  legislation  on  transparency,  accountability  and 
fundamental rights is essential to direct AI’s potential towards the highest benefit for individuals, 
society  and  the  environment,  as  well  as  towards  achieving  the  SDGs.  The  policy  option  also 

231 See the overview of benefits below, p. 32. 
232  Vinuesa,  R.  et  al.,  ‘The  role  of  artificial  intelligence  in  achieving  the  Sustainable  Development  Goals’,  Nature 
communications 11(1), 2020, pp. 1-10. 
233 OECD, Using artificial intelligence to help combat COVID-19, 2020. 
234 Ibid., p.2. 
235 Ibid., p. 4. 

95 

 
 
                                                           
contributes to raising awareness of the risks and SDGs trade-offs associated with possible failures 
of AI systems.  

As regards indirect environmental impacts, all policy options are expected to contribute, albeit to a 
non-quantifiable extent, to  the uptake of AI applications  that are beneficial  for the environment. 
For  instance,  AI  systems  used  in  process  optimisation  make  processes  less  wasteful  (e.g.  by 
reducing  the  amount  of  fertilizers  and  pesticides  needed,  decreasing  the  water  consumption  at 
equal  output, etc.).  AI systems supporting improved vehicle automation  and traffic management 
contribute to the shift towards cooperative, connected and automated mobility, which in turn can 
support  more  efficient  and  multi-modal  transport,  lowering  energy  use  and  related  emissions. 
However, it is probable that unintended effects may occur. For instance, an increase in traffic may 
be anticipated that could partly offset the lower energy use and emissions achieved through more 
efficient and multi-modal transport. 

2.  SUMMARY OF COSTS AND BENEFITS 

The costs and benefits of the preferred policy option are summarised in the following tables. 

I. Overview of Benefits (total for all provisions) – Preferred Option 

Description 

Amount 

Comments 

Reduced AI induced 
compensation gap 

Reduced costs  

Direct benefits 
No quantified estimates available.236 The 
targeted alleviations of the burden of proof are 
expected to effectively ensure that victims of 
damage caused with the involvement of AI 
enjoy the same level of protection as persons 
having suffered harm caused by other 
technologies. 

For citizens and businesses as potential victims, 
the alleviations of the burden of proof are 
expected to reduce litigation and enforcement 
costs linked to meeting the burden of proof 
under current liability rules by ca. EUR  2 000 
per case in which those alleviations apply.237 
This estimate should not be misconstrued as a 
quantification of the AI-specific difficulty of 
meeting the burden of proof, because it does not 
take into account the cases in which liability 
claims would not pursued in the first place based 
on current liability rules, because the victim 
either cannot identify the liable party or 

Citizens and businesses as potential victims 

Citizens and businesses as potential victims 

236  Due  to  the  future-oriented  nature  of  this  initiative,  aimed  at  creating  the  right  conditions  for  the  rollout  of  AI-
enabled products and services, the technologies to which this initiative would apply are in most cases not yet on the 
market.  There  is  hence  no  statistical  data  available  on  damage  caused  by  such  products  and  services,  nor  on  the 
success  rate  of  liability  claims  brought  on  the  basis  of  current  liability  rules.  The  qualitative  assessment  of  the 
expected compensation gaps (under the current liability rules = baseline scenario) and the extent to which the policy 
options would address those gaps are based on expert analysis, stakeholder feedback and desk research on the tools 
used in national and EU law to overcome information asymmetries and difficulties of proof. 
237  This  quantification  is  based  on  estimated  costs  of  technical  expertise  to  be  advanced  by  victims  to  claim 
compensation under current liability rules. In the framework of the supporting economic study (Deloitte), these costs 
were estimated, on the one hand, for cases where AI systems are involved in causing damage, and on the other hand, 
for cases not involving AI. The difference between these estimates was used to approximate the cost of meeting the 
burden  of  proof  due  to  the  specific  characteristics  of  certain  AI  systems.  On  that  basis,  assumptions  were  made 
regarding  the  effect  each  policy  options  would  have  on  this  cost  factor.  For  detailed  explanations  regarding  the 
methodology and assumptions made, see Annex 10, A.2.1.3.(d) and B.1.1.(b).  

96 

 
 
                                                           
considers the prospect of a successful claim 
insufficient to justify legal action. The preferred 
policy option will help victims also in the latter 
cases, by overcoming the compensation gaps 
induced by the specific characteristics of AI. 
This benefit is reflected in the previous row 
(‘reduced AI induced compensation gaps’). 

The burden of proof will be distributed more 
efficiently overall, as potentially liable parties 
must by definition be capable of influencing, to 
some extent, the operation of AI-systems. They 
are therefore typically in a position to discharge 
more easily the burden of proof, with respect to 
how or why such systems arrived at a certain 
harmful output. This has a cost-cutting effect on 
overall litigation costs. 

Indirect benefits 

From ca. EUR 500 mln (low estimated value) to 
ca. EUR 1.1 bln (high estimated value)238 

Businesses active in AI 

Citizens and businesses as potential victims 

Increased AI market value 
in the EU, due to reduced 
costs and increased 
revenues achieved through 
increased legal certainty, 
reduced legal fragmentation 
and increased consumer 
uptake 

Safer AI systems  

(direct/indirect) 

n/a 

Administrative cost savings related to the ‘one in, one out’ approach* 

II. Overview of costs – Preferred option 

Citizens/Consumers 

Businesses 

Administrations 

One-off 

Recurrent 

One-off 

Recurrent 

One-off  Recur

EUR 5.35mln (based 

rent 

Targeted  Direct 

238 These values are obtained by multiplying the estimated shares of the AI market affected by legal uncertainty and 
fragmentation  regarding  civil  liability  in  2025  under  the  baseline  scenario  (low  and  high  scenarios  assumed  by  the 
economic  study  supporting  this  IA)  with  the  estimated  impact  of  the  preferred  option  (+5%).  This  percentage  was 
determined conservatively, taking into account the estimated impact generated by a combination of measures to ease 
the  burden  of  proof  with  a  harmonisation  of  strict  liability  limited  to  certain  AI  applications  (cf.  Economic  Study, 
pp. 195  et  seq.).  In  the  supporting  study,  policy  options  including  these  elements  were  estimated  to  increase  the 
production value of the affected cross-border trade by 5-7 %, for the six use-cases analysed specifically by that study 
(AI-enabled autonomous vehicles, autonomous drones/delivery robots, AI-enabled road traffic management systems, 
AI-enabled  warehouse  robot,  AI-enabled  medical-diagnosis  services,  AI-enabled  automated  lawnmowers/vacuum 
cleaners). In order to quantify the overall economic benefits generated by the preferred option (not limited to the six 
use-cases),  a  conservative  extrapolation  of  this  estimate  was  applied  to  the  relevant  market  shares  of  all  sectors 
affected by legal uncertainty and fragmentation, taking into account that the preferred PO does not include the strict 
liability element assumed for the supporting study with respect to a small number of specific AI application.  
The  Joint Research Centre (JRC) has  provided complementary  micro-economic quantification of the impacts of the 
preferred  policy  option,  based  on  the  use-case  example  of  robotic  vacuum  cleaners.  This  analysis  reaches  the 
conclusion that the envisaged measures to ease the victim’s burden of proof would generate an increase in consumer 
welfare of EUR 11.5-19.12mln and in total welfare of EUR 30.11-53.74mln for this product category in the EU-27. 
See Annex 11 for the JRC report with detailed explanations and results. 

97 

 
 
 
 
 
 
 
 
 
 
 
 
                                                           
adjustme
nt costs 

and 
harmonis
ed 
alleviatio
n of the 
burden of 
proof   

on the lower estimate 
of the AI market 
size) to 
EUR 16.1mln (based 
on the higher 
estimate of the AI 
market size)239 This 
estimate represents 
the possible increase, 
due to the preferred 
policy option, of the 
overall amount of 
general liability 
insurance premiums 
paid annually in the 
EU. This cost factor 
is likely to be mostly 
relevant for 
businesses as 
potentially liable 
parties rather than for 
natural persons. This 
is because the AI-
specific liability gaps 
addressed by the 
preferred policy 
option are more 
likely to affect the 
liability exposure of 
actors with an active 
influence on the 
functioning of the 
relevant AI systems. 

Direct 
administr
ative 
costs 

Direct 
enforcem
ent costs 

The preferred policy option does not involve administrative obligations that would entail direct 
administrative costs. 

n/a  
In particular, the 
preferred policy 
option is not expected 
to entail additional 
litigation costs for 
private persons (as 
potentially liable 
parties).240 These 
stakeholders are 
likely to defend 

Between ca. 
EUR 200 and ca. 
EUR 1600 to be 
advanced by 
businesses as 
potentially liable 
party, per case in 
which the measures 
to alleviate the 
burden of proof 
apply.241  

239 See footnotes 25 and 26. 
240 As explained in the main part of the IA, only the targeted alleviation of the burden of proof regarding the ‘inner 
workings’ of an AI system could apply vis-à-vis citizens as potentially liable parties. The other measures forming part 
of the preferred policy option (presumption of causality in the case of non-compliance with relevant requirements of 
the AI Act / harmonised rules on the disclosure of information on AI systems to be documented/logged pursuant to the 
AI Act) are designed to apply only to addressees of obligations under the AI Act, that is to say businesses. 
241 This quantified estimate is based on reasoned assumptions regarding the extent to which the liable parties might 
have to advance the costs of technical expertise that would otherwise be borne by victims under the baseline scenario. 
This extent would vary widely in practice, as it depends on the liable party’s knowledge and information on the AI 
system. Moreover, it is important to underline that this cost increase would apply only in cases  where national courts 

98 

 
 
 
 
 
 
                                                           
 
themselves against 
liability claims using 
the same type of 
arguments and 
evidence as under the 
existing burden of 
proof rules. For 
example, they might 
seek to avoid liability 
by demonstrating that 
they acted diligently 
and in accordance 
with the instructions 
of use accompanying 
an AI-enabled 
product. Contrary to 
potentially liable 
businesses, which 
may have special 
knowledge and be 
subject to certain 
requirements 
regarding the 
functioning and 
‘inner workings’ of 
an AI system (in 
particular under the 
AI Act), private 
persons would not 
have to base their 
defence on an 
analysis of the 
functioning of such a 
system. The 
envisaged alleviation 
of victims’ burden of 
proof regarding the 
‘inner workings’ of 
AI systems is 
therefore not 
expected to prompt 
potentially liable 
private persons to 
commission technical 
expertise. 

Indirect 
costs 

The preferred policy option would not entail costs incurred in related markets or experienced by 
stakeholders that are not directly targeted by the initiative. In particular, as the initiative is expected to 
generate net cost savings for businesses active in AI (see benefits), it is not expected to lead to increased 
consumer prices. 

Costs related to the ‘one in, one out’ approach 

consider it necessary to establish how or why an AI system arrived at a certain output. As it is not possible to estimate 
in how many instances this might be the case, the costs are estimated only per individual case in which the targeted 
alleviation of the burden of proof would apply. The estimate also takes into account that for businesses falling under 
the  AI  Act,  the  preferred  PO  can  trigger,  aside  from  the  targeted  alleviation  of  the  burden  of  proof,  the  disclosure 
(subject to appropriate confidentiality safeguards) of information on the relevant AI system as well as a presumption 
of causality in the case of non-compliance with the AI Act. For details regarding the methodology and assumptions 
underpinning these estimates, see Annex 10, A.2.1.3.(d) and B.2.1.(g). 

99 

 
 
 
 
 
 
                                                                                                                                                                                     
Total   

n/a 

Direct 
adjustment 
costs  

n/a 

Indirect 
adjustment 
costs 

n/a 

Administr
ative 
costs (for 
offsetting
) 

n/a 

n/a 

n/a 

n/a 

n/a 

n/a 

EUR 5.35mln to 
EUR 16.1mln per 
year 

n/a 

n/a 

3.  RELEVANT SUSTAINABLE DEVELOPMENT GOALS 

III. Overview of relevant Sustainable Development Goals – Preferred Option(s) 
Relevant SDG 

Expected progress towards the Goal 

Comments 

SDG no. 3 – healthy lives and 
well being 

Reduce mortality rate caused by existing or 
future diseases by supporting doctors in early 
detection of symptoms (including through 
telemedicine), finding appropriate cures 
(including vaccines) and supporting hospitals in 
case of tasks dangerous for humans or shortage 
of staff.     

Citizens as potential victims 

SDG no. 9 – foster innovation  Promote the development and rollout of 

Citizens and businesses active in AI 

innovative AI enabled products and services, in 
particular safe AI solutions. 

SDG no. 13 – climate action  Help strengthen resilience and adaptive capacity to 
climate-related hazards and natural disasters in all 
countries, since AI systems can be used to  
improve environmental prediction, as well as  
optimising operations. 

Citizens and businesses as potential victims 

SDG no. 16 – access to justice 
for all 

Ensure effective access to the evidence necessary 
to build a case in court for victims of harm 
caused by AI-enabled products and services, in 
order for them to enjoy the same chances of a 
successful claim as victims of harm caused by 
other technologies. 

Citizens and businesses as potential victims 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 4 

Analytical methods and key findings of supporting studies / 
Explanations on how the European Parliament’s legislative own-
initiative resolution on a civil liability regime for AI was taken into 
account 

This Annex presents the analytical techniques applied in the framework of the studies supporting 
this IA and explains how the European Parliament’s legislative own-initiative resolution on a civil 
liability  regime  for  AI  (2020/2014(INL)  was  taken  into  account.  Detailed  explanations  on  the 
methodology  and  criteria  used  for  the  comparison  of  policy  options  (multi-criteria  analysis),  as 
well as the results of that comparison, are presented separately in Annex 10. That Annex contains 
also detailed explanations, in the context of the efficiency assessment, on the available quantified 
estimates,  and  the  assumptions  and  methodology  underlying  the  quantification  approaches.  In 
order  to  avoid  duplication,  reference  is  made  to  the  parallel  IA  on  the  PLD  review  for  a 
presentation of the analytical methods used by the study supporting that IA. 

1. Legal comparative analysis 

Building  on  the  report  on  from  the  Expert  Group  on  Liability  and  New  Technologies  (New 
Technologies Formation)242, the challenges posed by the specific characteristics of AI with regard 
to  various  national  legal  systems  was  further  investigated  by  legal  experts  using  the  method  of 
comparative legal analysis.243  

1.1. European tort laws 

Based on input provided by experts covering representative legal systems, the comparative legal 
analysis of European tort laws focused on key AI-related issues, such as: 

- 
- 
- 

- 

the standard of proof; 
the burden of proving fault and causality, respectively; 
causal uncertainty and procedural alleviations of the burden of proof available in national 
legal systems; 
the varieties of strict liability in Europe. 

The  comparative  problem  analysis  was  illustrated  based  on  relevant  use-cases,  namely 
autonomous vehicles, autonomous lawnmowers and combine harvesters, and autonomous drones. 

Regarding the application of European tort laws to damage caused by AI, the study came to the 
following key conclusions244: 

for  artificial 

intelligence  and  other  emerging  digital 

242  Liability 
https://op.europa.eu/en/publication-detail/-/publication/1c5e30be-1197-11ea-8c1f-01aa75ed71a1/language-en  
243  Karner/Koch/Geistfeld,  Comparative  Law  Study  on  Civil  Liability  for  Artificial  Intelligence,  2021, 
https://op.europa.eu/en/publication-detail/-/publication/8a32ccc3-0f83-11ec-9151-01aa75ed71a1/language-en  
244 Cf. Comparative Law Study, Executive Summary. 

technologies,  November  2019, 

101 

 
 
                                                           
- 

- 

It is to be feared that at least some victims will not be indemnified at all or at least remain 
undercompensated  if  harmed  by  the  operation  of  AI  technology.  The  outcome  of  such 
cases  in  the  Member  States  will  often  not  be  the  same  due  to  peculiar  features  of  these 
legal systems that may play a decisive role especially in cases involving AI. 
Identifying  the  cause  of  damage  and  convincing  the  court  of  its  impact  on  the  turn  of 
events is challenging if it is an AI system that is suspected to have at least contributed to 
causing harm to the victim. This is due to the very nature of AI systems and their peculiar 
features  such  as  complexity,  opacity,  limited  predictability,  and  openness.  Identifying 
harmful conduct will be the more difficult the more independent the behaviour of the AI 
system is designed, or – figuratively speaking – the more black the box is. After all, while 
human conduct is literally visible and can be witnessed, identifying the processes within an 
AI  system  and  persuading  the  court  thereof  seems  much  more  challenging,  even  if  the 
defendant’s  conduct  relating  to  the  AI  system  can  be  traced  back  with  convincing 
evidence. 

-  Those charged  with  the  burden of proof  can sometimes benefit  from certain  alleviations, 
for  example  if  the  courts  are  satisfied  with  prima  facie  evidence.  It  is  hard  to  foresee 
whether  and  to  what  extent  this  will  also  be  available  in  cases  of  harm  caused  by  AI 
systems. At least initially, it would be difficult to apply, considering that this requires as a 
starting point a firmly established body of experience about a typical sequence of events, 
which at first will be missing for novel technologies. 

1.2. US law 

Having regard to the pre-eminent position of the US in the field of AI technologies, information 
on the legal regimes in the US on AI was added, focusing on federal law and the laws of relevant 
states. The analysis yielded the following key insights, showing that problems similar to the ones 
addressed  by  the  AI  liability  proposal  arise  under  US  tort  law,  and  measures  to  address  those 
problems are being contemplated also by US regulators: 

- 

- 

- 

In the US, a patchwork of federal and state laws expressly address various issues involving 
artificial intelligence (AI). Federal legislators and regulators have paid the most attention 
to  autonomous  vehicles.  In  Congress,  there  has  been  considerable  bipartisan  support  for 
federal  legislation  that  would  create  a  comprehensive  framework  for  regulating 
autonomous vehicles. This legislation, however, has stalled because of the pandemic and 
the 2020 presidential election. 
In  addition  to  bodily  injury  or  property  damage,  AI  can  cause  other  types  of  harms 
governed by existing US laws. In some cases, for example, plaintiffs have alleged that the 
defendant trained the AI with datasets that violate federal or state privacy laws. Other cases 
involve  algorithms  that  allegedly  violate  anti-discrimination  laws  or  those  governing  fair 
trade practices.  
In the absence of federal law that resolves an issue of liability or insurance, state law will 
govern  these  issues.  As  of  June  2020,  seven  states  have  adopted  statutes  that  expressly 
regulate  the  use  of  AI.  For  example,  two  states  have  adopted  statutes  permitting 
optometrists  to  use  AI  in  eye  assessments.  They  must  maintain  liability  insurance  in  an 
amount adequate to cover claims made by individuals examined, diagnosed, or treated with 
the AI. 

102 

 
 
-  The state governments’ primary emphasis with respect to the safety performance of AI has 
involved  autonomous  vehicles.  As  of  June  1,  2020,  thirty-five  states  and  the  District  of 
Columbia  have  enacted  statutes  expressly  regulating  autonomous  vehicles.  Three  states 
have adopted statutes that directly address liability in the event of a crash, and one of them 
(Tennessee) has comprehensively addressed the liability and insurance questions.  
In the absence of  either  federal  or  state statutes that  dictate otherwise, state tort law will 
govern  cases  in  which  AI  technologies  cause  injury.  In  general,  someone  who  has  been 
physically harmed by AI will have four potential tort claims: 

- 

o  If the operator negligently deployed the AI, then  the victim can recover  based on 

proof of fault, causation, and damages. 

o  If the AI determines the physical performance of a product, the victim can re-cover 
from  the  manufacturer  under  strict  products  liability  by  proving  that  the  product 
contained  a  defect  that  caused  the  injury.  Alternatively,  if  the  AI  only  provides  a 
service  that  was  otherwise  reasonably  used  by  the  operator,  then  the  victim  must 
prove  that  the  AI  performed  in  an  unreasonably  dangerous  manner  because  of 
negligence by the manufacturer or other suppliers of the AI. 

o  The owner can be subject to negligence liability for negligent entrustment of the AI 
to the party who negligently caused the victim’s injury, or under limited conditions, 
the owner can be vicariously liable for the user’s negligence. 

o  Finally, the victim can recover from the operator and potentially the owner under 
the rule of strict liability that would apply only if the AI technology is abnormally 
dangerous despite the exercise of reasonable care. 

-  Plaintiffs in all states bear the burden of proving negligence, which will be hard to do in 
many  cases  of  AI-caused  injury.  There  might  be  uncertainty  about  how  AI  should  be 
reasonably used before customary practices have been established. 

1.3. Methodological constraints 

Given  budgetary  and  time  constraints,  the  comparative  law  study  could  not  provide  a 
comprehensive overview of all existing tort law regimes with regard to AI, but focused on selected 
problem  constellations  that  serve  to  highlight  key  aspects  of  civil  liability.  The  report  was 
therefore limited to a core outline of the key features of applicable liability rules that distinguish 
the tort laws of Europe. The findings are based on illustrative examples from selected European 
legal systems representing different legal families, with a starting point in Germanic jurisdictions 
due to the authors’ background.  

Furthermore, the comparative analysis was of a descriptive nature, explaining the diversity of tort 
laws in the EU as evidenced in particular by cases involving AI systems, and neither attempted to 
demonstrate potential solutions to overcome such differences nor made policy recommendations. 

In order to complement the comparative law study, and take into account as complete information 
as  possible  on  the  existing  legal  landscape,  other  sources  were  used  to  inform  this  IA.  For 
example, the ‘Comparative study on national rules concerning non-contractual liability, including 

103 

 
 
with regard to AI’ which accompanied the European Added Value Assessment on a Civil liability 
regime for AI245 was taken into consideration. 

2. Behavioural analysis 

2.1. Scope and objectives 

The link between the identified legal challenges of claiming compensation under existing national 
liability rules and societal/consumer trust in AI-enabled products and services was researched by 
an external consultant, based on the methods of behavioural analysis.246 The study examined the 
following two dimensions: 

-  As regards the societal acceptance of AI-enabled products and services, the study assessed 
the  current  level  of  acceptance  of  AI  applications,  the  factors  shaping  it,  as  well  as  the 
awareness of potential challenges in obtaining compensation for damage caused by these 
applications and its effect on societal acceptance. 

-  With  respect  to  consumers’  trust  and  willingness  to  take  up  AI-enabled  products  and 
services, the study generated insights on the potential impact of targeted adaptations of the 
liability regime on consumers’ trust and consumers’ willingness to take up such products 
and services, and on the causal mechanisms underlying this impact. 

2.2. Methodology 

The study followed a mixed-method approach, consisting of the following components: 

- 

-  Desk research and review of literature, through which the consultant took stock of the most 
up-to-date  policy  studies  and  empirical  research  on  the  topics  of  liability,  consumer 
behaviour, and attitudes towards technology and AI. 
In-depth  interviews  with  consumers,  which  helped  to  deepen  the  understanding  of  the 
behavioural  dynamics  that  link,  on  the  one  hand,  societal  acceptance  of  AI  applications, 
consumers’ trust  and their willingness  to  take up AI applications  and, on the other hand, 
civil liability rules. The insights derived allowed the consultant to fine-tune the design of 
the  survey  and  of  the  survey-based  experiment,  while  also  partly  complementing  their 
results.  The  interviews  were  carried  out  individually  with  18  consumers  from  three 
countries. 

-  An online survey carried out on representative samples of the adult population from eight 
countries (Denmark, Netherlands, Ireland, France, Germany, Italy, Poland, and Romania), 
with a sample size of at least 1,000 respondents per country, amounting to a total of 8,079 
respondents  across  the  eight  countries.  The  sample  for  each  country  was  a  quota-based 
one,  with  additional  demographic  weights  being  computed  and  appended  to  the  data  in 
order to correct any potential distortions of the characteristics of the respondents within the 
sample from the characteristics of the population from which the sample was extracted. 

245  EPRS,  Civil 
https://www.europarl.europa.eu/RegData/etudes/STUD/2020/654178/EPRS_STU(2020)654178_EN.pdf  
246 Behavioural Economics Study, op. cit. 

intelligence,  September  2020,  Author:  Tatjana  Evas, 

for  artificial 

liability 

regime 

104 

 
 
 
                                                           
-  A survey-based online experiment, through which, following a between and within subject 
design,  the  consultant  tested  the  effect  of  alternative  liability  regimes  on  consumer 
behaviour  towards  AI  applications.  The  experiment  design  was  built  around  three  AI 
applications  (autonomous  lawnmower,  grocery-carrying  robot  and  a  smart  irrigation 
system).  These  AI  applications  were  selected  based  on  their  relevance  in  terms  of 
consumer  use  and  acceptance.  According  to  dedicated  market  research,  these  types  of 
applications  have  already  been  rolled  out  or  are  likely  to  be  on  the  market  in  five  years 
from now.  
In  order  for  the  behavioural  experiment  to  cover  the  full  range  of  situations  in  which 
liability can play a role for consumers, separate scenarios were envisaged covering liability 
for  ‘damage  suffered  by  the  owner  of  ab  AI  application’  or  ‘damage  suffered  by  a  third 
party’.  Within  each  of  these  scenarios,  three  alternative  liability  regimes  were  posited, 
including  one  (fault-based  liability  with  the  burden  of  proof  on  the  injured  party)  that 
generally corresponds to most Member States’ existing fault-based liability rules.247 

- 

-  For  the  online  experiment,  the  participants  comprised  in  the  sample  were  randomly 
allocated, within the sample for each country, to one of the three groups defined by the AI 
applications  of  reference.  Within  each  of  the  three  groups,  they  were  further  randomly 
allocated  to  one  of  two  groups,  as  defined  by  the  two  scenarios  encompassing  either 
damage to the owner of the AI application or to a third party.  

-  The  participants  were  shown  a  visual  image  of  the  AI  application  of  reference  and  were 
asked  to  respond  to  a  series  of  question  within  that  context.  After  having  answered  the 
questions, they were asked to read a mock-up article presenting an accident caused by the 
AI  application  of  reference.  After  having  read  the  article,  the  treatments  comprising  the 
alternative liability regimes consisted of a series of mock-up newspaper interviews with a 
legal  expert  providing  explanations  on  the  assumed  liability  regime  and  likelihood  of 
compensation.  Each  participant  was  required  to  read  three  such  interviews  in  a  random 
order  and  was  asked  after  each  one  to  answer  a  series  of  questions  through  which  we 
measured the effect of the exposure to the information contained in the interview. 

2.3. Key findings248 

The behavioural analysis has yielded the following key insights: 

-  Liability  regimes  that  place  the  burden  of  proof  on  the  injured  party  contribute  to  lower 
levels of societal acceptance, consumer trust and willingness to take up A-enabled products 
and  services.  Regulatory  alternatives  addressing  the  expected  lack  of  effectiveness  of 
national  liability rules are likely to  have a positive impact  on these indicators. The study 
has  also  shown  that  other  factors  such  as  the  perceived  level  of  safety/risk  of  accidents 
associated  with  AI-enabled  products  and  services  also  play  a  significant  role  in  shaping 
consumer trust. 

-  The  expected  likelihood  of  receiving  compensation  if  AI-enabled  products  or  services 
cause damage is one of the factors shaping the level of consumer trust in such products and 

247 The study focused on national liability rules and regulatory alternatives for adapting these rules. Accordingly, none 
of the posited liability regimes corresponded to the PLD. In particular, strict liability as posited for the purposes of this 
study does not require a defect and was not linked specifically to the producer. 
248 Cf. Behavioural Economics Study, op. cit, Executive Summary. 

105 

 
 
                                                           
services:  if  consumers  expect  that  they  are  less  likely  to  get  compensation  for  damage 
compared to traditional devices, levels of trust are lower. For most consumers, a low level 
of trust is linked, amongst others, to the same AI characteristics that are also expected to 
make it more difficult to claim compensation for damage, namely  

o 
o 

the ability to function without human intervention or supervision and  
the opacity of certain types of AI applications.  

-  When  asked  to  specify  the  reasons  for  their  lack  of  trust  in  AI  applications,  consumers 

identified the following as the strongest reasons: 

o 

o 
o 

the difficulty to determine who is responsible in the case of damage caused by AI 
applications,  
the perceived low likelihood of compensation in case of such damage, and  
the perception of AI as ‘black boxes’. 

-  The  behavioural  experiment  also  showed  that,  overall,  liability  regimes  that  increase  the 
likelihood of compensation and shift  the liability to  a party other than the consumer  (for 
instance  a  provider),  lead  to  more  positive  attitudes  towards  AI-enabled  products  and 
services.  Willingness  to  buy  is  lower  under  the  assumption  that  the  consumer  claiming 
compensation  for  own-damage  has  the  difficult  burden  of  proving  fault,  as  it  is  the  case 
under most Member States’ tort laws. Willingness to buy increases if damage suffered by 
consumers caused by their own AI application is covered by the strict liability of another 
party  (which  could  be  the  party  that  produced,  designed,  monitored  or  updated  the  AI 
application remotely) or if the burden of proving the other party’s fault is  adapted in  the 
consumers’ favour.  

-  When provided with information about the likelihood that a third party harmed by an AI 
application  will  get  compensation,  consumers’  willingness  to  buy  is  higher  if  their  own 
liability exposure is low because it is either difficult for the injured third party to meet the 
burden of proof or that  party  has easy  access  to compensation  from  another liable party. 
Consumers’ willingness to buy is higher under the latter scenario, which indicates that they 
also take into account the interests of the injured third party. Consumers’ willingness to use 
an AI application – for instance when it is made available as a service or for rent – follows 
the same pattern as in the case of their willingness to buy. 

2.4. Methodological constraints 

Due  to  the  general  methodological  limitations  relating  to  the  design  of  consumer  surveys  and 
behavioural  experiments  (in  particular  the  need  to  avoid  over-complexity),  the  specificities  of 
certain  sectors (such as  AI in  education or healthcare), could  not  be reflected in  the behavioural 
analysis.  Some  sectors  may  be  partly  characterised  by  different  consumer  concerns  than  the 
sectors represented by the AI applications used as examples. 

Moreover, the behavioural analysis did not investigate specifically the effect of safety legislation 
on  consumer  trust,  in  line  with  this  IA’s  focus  on  issues  related  to  liability  rules.  However,  the 
study  did  analyse  the  perceived  risk  of  accidents  associated  with  AI  applications  as  a  possible 
factor shaping consumer trust, and acknowledged the complementary nature of safety and liability 
rules, namely in light of the recent Commission initiatives designed to ensure the effectiveness of 
safety rules in the face of AI-specific challenges (AI Act, Machinery Products Regulation). 

106 

 
 
 
3. Economic analysis 

3.1. Objectives 

An additional study was commissioned to provide economic analysis of the following aspects249: 

- 
- 

- 

the market for AI applications capable of causing damage; 
an assessment of whether and to what extent, under the current liability framework, victims 
of damage caused by AI applications would be in a comparatively more difficult situation 
than victims of damage caused by non-AI devices when trying to obtain compensation for 
their loss; 
an  assessment  of  whether  the  challenges  raised  by  AI  for  the  national  civil  liability 
frameworks  are  likely  to  slow  down  the  uptake  of  AI  in  the  EU.  In  particular,  the  study 
investigates the impact of: 

o 

legal  uncertainty:  whether  and  to  what  extent  businesses  are  uncertain  about  the 
application  of  current  liability  rules  to  their  operations  with  AI,  and  whether  the 
impact of legal uncertainty can hamper investments in AI; 

o  future  legal  fragmentation:  if  Member  States  individually  adjust  their  –  already 
fragmented  –  liability  laws  to  the  challenges  posed  by  AI,  whether  this  would 
reduce the effectiveness of the internal market for AI applications and services. 

- 

an assessment of whether and to what extent harmonising certain aspects of national civil 
liability via EU legislation would reduce these problems and facilitate the overall uptake of 
AI technology by EU companies. 

3.2. Analytical methodology 

(a) Risk-based approach and use cases 

Similar to the approach set out in the White Paper on AI and the accompanying Report on Safety 
and Liability Implications, the economic study addressed the research questions with a risk-based 
approach  to  AI  applications.  This  means  that,  in  order  to  identify  relevant  industry  stakeholders 
and subsequently assess the impact of policy options, the study team selected a number of relevant 
AI-enabled products  and services and categorised them as follows based on their respective risk 
profile: 

Risk profile 
(specific/ 
other) 

Type of use case 
(traditional / AI-based / 
IoT-based) 

Product/service 

Type of damage 

specific 

AI-based 

AI-enabled autonomous vehicles 

Motor vehicles  

Injury or death; damage to property (e.g. 
other vehicles, pedestrians, static objects  
such as infrastructure) 

specific 

AI-based 

Autonomous drones/delivery robots 

Injury (in rarer cases death); damage to  

Drones/autonomous delivery robots 

249  Deloitte,  Study  to  Support  the  Commission’s  Impact  Assessment  on  Liability  for  Artificial  Intelligence,  2021 
(‘economic study’). 

107 

 
 
 
                                                           
Road traffic management systems 

property (e.g. objects on the ground) 

specific 

AI-based 

AI- and IoT-enabled road traffic  
management system 

Injury or death; damage to property (e.g. road  
vehicles involved in accidents) 

AI-enabled manufacturing appliances 

other 

AI-based 

AI-enabled warehouse robots 

Injury or death; damage to property (e.g. goods 
being processed in a facility) 

other 

AI-based 

AI-enabled Medical diagnosis services 

Injury, illness, or death 

Medical devices 

other 

AI-based 

Automated lawnmowers/vacuum 
cleaners 

Minor or serious injury; damage to property 
(e.g. property standing in gardens) 

Lawnmowers 

The  selected  use  cases  represent  examples  intended  to  make  the  analysis  more  concrete  and 
facilitate  stakeholder  input.  They  were  the  main  reference  for  the  data  collection  and  analysis. 
However,  the  identified  AI-specific  problems  concerning  liability  rules,  as  well  as  the 
Commission’s policy options on AI liability, are not limited to these use-cases. Therefore, both the 
impacts of legal uncertainty and fragmentation under the baseline scenario and the impacts of the 
preliminary policy options were also analysed on a broader basis, taking into account all relevant 
sectors  of  the  economy.  The  use  case  based  approach  and  the  broader  economic  analysis 
complement one another to inform the overall findings.  

(b) Data collection and analysis 

- Market data collection: The study team reviewed market research reports and analyses on the 
current  and  expected  economic  significance  of  AI  applications  in  the  EU  space.  This  data 
collection allowed the study team to identify market trends and to estimate the development of the 
market until 2030. The study team  ensured the use of comparable sources that applied the same 
methodology to calculate market value for the different use cases. For the analysis of the structure 
of  the  EU  market  for  AI,  the  study  team  relied  on  recognised  databases  consolidating  data  on 
companies manufacturing and/or operating AI applications in the EU. 

-  Collection  of  data  on  the  costs  and  time  required  to  claim  compensation  under  current 
liability  rules,  as  a  means  of  approximating  victims’  difficulties  in  claiming  compensation 
due to the specific characteristics of AI: The study team consulted legal experts in 13 Member 
States and the UK with long-standing experience in civil liability cases related to information and 
communication  technologies  and  novel  technologies.  The  legal  experts  estimated  the  costs  and 
time required by lawyers and technical experts in “traditional” cases of damage, as well as in cases 
involving AI applications. The data collection was complemented by desk research. 

- Data collection on the impact of liability-related legal uncertainty and fragmentation on the 
roll-out  of  AI:  The  study  team  principally  used  two  methods  to  collect  data  for  this  task:  (i)  A 
survey targeted to European trade associations and companies with activities linked to AI, and in 
particular the operation, production, development of AI systems in relation to the six selected use 
cases;  (ii)  Interviews  with  trade  associations,  companies  and  experts.  The  interviews  were 
conducted in a semi-structured manner, with an overall list of questions shared in advance and the 
possibility to address also different topics during the interviews. 

108 

 
 
 
- Data collection on the impact of preliminary policy options: Here as well, the study team used 
a combination of survey and interviews. In order to receive targeted data across different relevant 
stakeholder  groups,  the  survey  was  designed  in  three  different  versions:  (i)  for  operators  of  AI-
enabled products and providers of AI-enabled services (including companies that are at the same 
time manufacturers/developers of AI-systems); (ii) for the legal experts consulted also on the cost 
of  claiming  compensation  under  the  current  liability  rules,  to  ascertain  whether  the  preliminary 
policy options were suitable to improve victims’ situation; (iii) for insurance companies.  

The analysis of the data collected relied on traditional triangulation techniques in all tasks of the 
study.  For  the  market  analysis,  data  collected  from  databases  and  market  research  reports  was 
aggregated to the extent necessary to provide a sound representation of market trends until 2030. 
The study team organised and hosted a webinar during the month of October 2020. The webinar 
enabled over 40 participants  from  various industrial  sectors to  provide views on the preliminary 
findings  of  the  study  and  to  debate  the  major  research  questions  (impact  of  legal  uncertainty, 
future legal fragmentation, and preliminary policy options). The webinar allowed the study team 
to  confirm  and  adjust  the  findings  and  provided  useful  input  to  orientate  the  subsequent  data 
collection activities. 

(c) Methodology to assess the challenges of AI for victims of damage, based on the costs of 
claiming compensation 

As  a  basis  for  gathering  cost  estimates  as  well  as  input  on  specific  challenges  associated  with 
claiming compensation for damage caused by AI, the study team presented the respondents with 
hypothetical liability scenarios. Those scenarios involve, in a first step, the causation of damage by 
‘traditional’ technologies (that is to say, technologies without autonomous AI-enabled functions) 
for  which  the  AI-enabled  use-cases  presented  under  Sections  3.1  above  are,  or  are  expected  to 
become, available as functionally equivalent alternatives. Respondents were asked for data on the 
average/range  of  fees  required  by  them  and  by  technical  experts  to  work  on  traditional  liability 
cases, the average/range of hours needed to deal with this type of cases, and the likelihood that the 
victim obtains a favourable judgment (i.e. obtains a fair and reasonable amount of compensation).  

In a second step, the responses gathered on those ‘traditional’ liability cases were juxtaposed with 
respondents’ estimates and input regarding the same liability scenarios, but this time involving the 
AI-enabled use-cases. This approach enables a targeted comparison with a view to pinpointing the 
effects of AI-specific challenges under existing liability rules. 

(d) Methodology to assess the impact of liability-related legal uncertainty and fragmentation 
on the roll-out of AI 

- Economic impact analysis based on six use cases: The methodology was designed to estimate 
the size of the economy in the six use cases (measured in terms of production value and/or gross 
value  added)  concerned  or  affected  by  legal  fragmentation  and/or  uncertainty  regarding  liability 
for AI. It is limited to the cross-border trade in this regard, assuming that these economic activities 
will be lowered in a worst case, if transaction costs  due to uncertainty and fragmentation appear 
too  high.  The  methodology  is  based  on  a  economic  analytical  approach  involving  the  following 
steps (source: Deloitte): 

109 

 
 
The market values within the EU27 for each of the six use cases were taken as a starting point for 
this  evaluation.  In  a  second  step,  extra  EU-27  exports  are  added  and  extra  EU27-imports  are 
subtracted  in  order  to  obtain  the  production  values  of  the  six  use  cases.  As  a  next  step,  these 
production values were multiplied by two shares to determine, firstly, the production value which 
crosses  borders  within  the  EU27,  and  secondly,  the  proportion  for  which  uncertainty  and 
fragmentation  regarding  Member  States’  existing  liability  rules  might  be  relevant.  The  latter 
proportion was approximated by applying the share of the companies that see liability rules as a 
challenge  to  their  businesses  (based  on  the  IPSOS  European  enterprise  survey  on  the  Use  of 
Technologies based on Artificial Intelligence” of 2020). The study team thus obtained the EU27 
intra-trade production (for the six use-cases) potentially affected by liability issues regarding AI. 

It  should  be  noted,  that  even  though  cross-border  business  might  be  more  affected  by  legal 
uncertainty  –  e.g.  for  companies  entering  regional  markets  outside  their  home  country  –  legal 
uncertainty  regarding  the  existing  national  liability  rules  can  also  be  relevant  within  individual 
Member  States.  However,  to  estimate  the  share  of  the  economy  affected  by  liability-related 
impacts  –  being  either  legal  uncertainty  or  fragmentation  –  in  a  consolidated  approach,  this 
analysis  was  limited  to  cross-border  related  production  in  the  internal  market.  Therefore,  the 
approach likely leads to a certain under-estimation of the size of the economy affected (measured 
in terms of the production value).  

liability-related 

implications  of 

legal  uncertainty  and 
-  Economic  analysis  of  the 
fragmentation on the roll-out of AI across all relevant market sectors: As the identified AI-
specific problems  concerning liability  rules  are relevant  beyond the six use cases across various 
sectors of the EU economy, the above methodology was complemented by a broader analysis of 
the economic impacts of legal uncertainty and fragmentation with respect to liability for AI. For 
this  purpose,  firstly,  all  sectors  which  include  relevant  AI-enabled  applications  were  identified 
based on  existing research and other relevant  analytical  findings. The  Ipsos  European enterprise 
survey of 2020 was again taken into account in this framework: 

In  a  second  step,  the  percentage  of  AI-enabled  applications  in  each  sector  was  determined  as  a 
share  of  the  total  AI  market  size  in  the  EU.250  As  a  third  step,  the  share  of  these  AI-enabled 
applications that is affected by liability-related issues (legal uncertainty and legal fragmentation) 

250 The share was determined at an overall EU-level and did not differentiate within sectors across EU Member States. 
This  is  due  to  a  lack  of  sufficient  data  at  the  Member  State  level  and  because  the  study  focused  on  the  impact  of 
uncertainty  and  fragmentation  at  the  EU  level.  Existing  country-level  differences  in  AI  adoption  were,  however, 
mentioned and analysed where necessary. 

110 

 
 
 
                                                           
was determined on a sectoral level. For this purpose, the ability of causing physical harm, property 
damage or other types of damage that may be compensable under Member States’ liability rules 
(such  as  immaterial  harm  and  pure  economic  loss)  were  taken  into  account  based  on  existing 
research: 

Figure Share of AI-enabled applications potentially affected by liability-related issues per sector (Source: Deloitte estimation based Ipsos 
(2020) and analysis of existing research.) 

Having performed these three steps, general conclusions regarding the impact of liability-related 
issues on the wider roll-out of AI per sector were drawn, and tendencies for future development 
outlined. 

The  implications  for  SMEs  were  analysed  specifically,  taken  into  account  the  share  of  start-ups 
and other SMEs within the various sectors. 

(e) Methodology to assess the impact of policy measures 

-  For  the  purposes  of  the  economic  analysis,  preliminary  policy  options  were  determined  in 
coordination with the Commission’s steering committee: 

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These  preliminary  policy  options  comprised  the  same  tools  and  elements  as  the  policy  options 
assessed in this IA, but the structure and logic underpinning the policy options evolved in certain 
respects  following  the  conclusion  of  the  study.  In  particular,  the  consultant  did  not  examine 
specifically  the  ‘staged  approach’,  involving  a  harmonisation  of  proof-related  aspects  in  a  first 
stage  and  a  targeted  review  of  the  need  to  harmonise  strict  liability  for  AI  in  a  second  stage. 
Moreover,  measures  to  ease  the  burden  of  proof  were  analysed  in  a  combination  with  strict 
liability and mandatory insurance as part of one policy option, whereas these elements were also 
considered as separate approaches for the purposes of this IA. The results of the economic analysis 
of  policy  options  are  nevertheless  useful  to  inform  this  IA,  as  they  cover  the  same  tools  and 
approaches, albeit in a partly different composition. 

- The costs and benefits of the preliminary policy options were analysed against the baseline (no 
EU action) for four separate stakeholder groups: (i) victims of damage caused by AI applications; 
(ii) potentially liable parties, in particular operators or potential operators of AI applications; (iii) 
insurance companies; and (iv) wider society and economy. 

3.3. Key findings251 

(a) Challenges faced by victims of damage caused by AI-based products or services 

Under  Member  States’  current  non-harmonised  civil  liability  rules,  because  of  the  higher 
autonomy,  opacity  and  complexity  of  AI,  victims  of  damage  caused  by  AI  are  likely  to  be  in  a 
comparatively more difficult situation than victims of damage caused by non-AI applications. The 
involvement  of  AI  will  substantially  increase  the  burden  on  victims,  leading  even  to  a 
multiplication of costs in some Member States (in particular due to drastically increased costs for 
expert  analyses).  The  AI-related  burden  is  expected  to  increase  significantly  more  when  fault-
based liability applies, especially if the burden of proof lies with the claimant. This is mainly due 

251 Economic Study, Chapter 8 Conclusions. 

112 

 
 
 
 
                                                           
to  the  increased  difficulty  in  identifying  the  liable  party,  proving  fault  and  establishing  the 
required  causal  link  between  a  faulty  behavior  and  the  damage.  The  increasing  degree  of  AI 
autonomy presents a significant challenge because it affects fundamentally the viability of today’s 
civil liability regimes, which normally require the victim to prove that the damage was caused by 
human  fault.  Furthermore,  with  respect  to  AI’s  characteristic  of  opacity,  the  consulted  experts 
submitted  that  the  need  to  have  a  thorough  understanding  of  the  algorithms  and  the  data  flows 
behind  the  functioning  of  AI  in  order  to  correctly  assess  liability  claims  calls  for  specialised 
expertise.  The  study  team’s  analysis  has  confirmed  that  the  estimated  cost  and  time  required  to 
make  a  liability  claim  are  relevant  proxies  that  can  be  used  to  measure  the  challenges  faced  by 
victims in AI-related cases. 

(b)  The  effect  of  present  and  probable  future  challenges  of  AI  regarding  national  civil 
liability rules on the functioning of the internal market in AI-based products and services 

Significant  growth  is  expected  for  AI  in  all  sectors  over  the  next  10  years.  Almost  all  AI 
technologies can be considered as cross-sector technologies, the adoption being especially high in 
equipment optimisation, anomaly detection and process automation technologies. Despite this, the 
roll-out  of  AI-technologies  in  the  internal  market  could  be  impacted  by  a  number  of  challenges 
that businesses will face in the coming years. Aside from the obstacles directly linked with legal 
uncertainty and fragmentation under existing liability rules, one challenge relates to data-sharing 
rules, as a significant amount of data is necessary for software developers to train the technology. 
The roll-out of AI-based applications could also be hampered by a lack of trust from consumers. 
For the cross-border trade of AI, there is hence significant unused internal market potential, with 
no region emerging as a leader in terms of cross-border trade. This finding applies to a particular 
degree with respect to micro-enterprises (start-ups) and other SMEs, which have a strong presence 
in the AI market (around 90-95% of all companies) but are also more affected by barriers to cross-
border trade. 

Legal certainty is key for businesses to invest and market their products, because it enables them 
to  understand  their  rights  and  obligations,  to  seek  redress  for  violations  of  such  rights,  and  to 
foresee the risk of their economic activity. Legal certainty is especially relevant for the roll-out of 
emerging technologies, such as AI. The existing research confirms  that liability law plays a key 
role as an instrument to control the risk associated with the development and use of AI. However, 
based  on  existing  research  findings  and  consultation  of  stakeholders,  there  is  widespread 
uncertainty  within  the  business  world  as  to  the  application  of  national  civil  liability  rules  to 
damage caused by AI. Business stakeholders consulted for this study overall indicated uncertainty 
about  the  application  of  liability  rules  to  the  operation  of  AI  applications  and  the  differences  in 
their interpretation in the Member States, making it difficult to understand their liability risk. They 
submitted  that  this  would  impact  their  operations  and  possibly  their  uptake  of  AI-products  and 
services. These views are not representative as they come from  a limited set of respondents, but 
they confirm the assessment, based on available research, of how the peculiar features of AI may 
challenge the application of traditional liability rules.  In principle, businesses tend to  favor legal 
harmonisation over fragmentation, because the more fragmented and diverse a legal framework is, 
the  more  costly  compliance  and  the  more  complex  risk  assessments  become.  Accordingly, 
amongst  the limited sample of business  stakeholder  contributing  to  this  study,  a majority  feared 
that  national  liability  rules,  once  adjusted  to  the  AI  context,  may  be  (more)  fragmented  in  the 

113 

 
 
coming  years,  and  that,  if  this  scenario  materialised,  it  would  constitute  a  challenge  for  their 
operations. 

More particularly, both stakeholder input and research findings suggest that a significant share of 
businesses active in the AI field does or will incur various costs to cope with legal uncertainty and 
fragmentation of national liability rules, namely information and compliance costs, investment and 
economic transactions costs, insurance costs and opportunity costs in the form of forgone revenue 
due to hesitation in exploring new AI markets. A key finding of this study is that such challenges 
are perceived more strongly by SMEs compared  to  large  corporations: this is  due to  the limited 
resources  available  to  the  former  to  cope  with  greatly  diverging  legal  frameworks  within  the 
internal market (both in terms of compliance costs, and costs of compensation in case of damage). 

While the impact of legal uncertainty and fragmentation is not yet quantifiable, it is likely that the 
related costs for businesses would amount to significant obstacles to the functioning of the internal 
market,  affecting  start-ups  and  other  SMEs  disproportionately.  Legal  uncertainty  and 
fragmentation of liability rules may hinder the roll-out of AI applications in the EU market. If no 
action is taken at EU level to mitigate legal uncertainty and fragmentation regarding liability for 
damage  caused  by  AI,  those  additional  costs  would  persist,  posing  barriers  to  the  entry  of  new 
firms in the internal market and to the expansion of activities across borders. For micro and small 
enterprises,  the  additional  cost  incurred  due  to  legal  uncertainty  and  fragmentation  may  be 
prohibitive or at least reduce their competitiveness in relation to larger companies. 

(c) Impact of possible regulatory solutions at EU level 

The study team concluded that policy options 2 and 3 (see overview above) are the most suitable 
to improve the current situation for all stakeholder groups, as well as to facilitate the realisation of 
the societal benefits of AI. While there is at the present stage still a lack of robust evidence and 
data  allowing  for  precise  estimations  of  impacts,  these  conclusions  rely  on  a  triangulation  of 
stakeholder input, existing research and economic theory. 

Regarding potentially liable parties, in particular operators of AI-applications, Options 2 and 
3  are  expected  to  be  the  most  beneficial.  Despite  higher  early  compliance  costs  for  the  limited 
number of stakeholders falling under the envisaged insurance obligation who would not anyway 
have  taken  out  insurance  voluntarily,  these  options  are  expected  to  greatly  reduce  legal 
uncertainty,  prevent  fragmentation,  and  facilitate  consumer  acceptance  and  uptake  of  AI.  It  is 
hence likely that the internal market obstacles resulting from legal uncertainty and fragmentation 
with  respect  to  liability  for  damage  caused  by  AI  would  be  effectively  addressed  through  those 
options,  leading  to  a  reduction  of  legal  information  and  compliance,  transaction,  internal  risk 
management  and  opportunity  costs.  Specifically,  associations  representing  SMEs  indicated  that 
the  increased  certainty  enabled  by  Option  2  would  allow  their  members  to  save  substantial 
resources currently devoted to compliance and legal representation. The higher risk of exposure to 
liability under a strict liability regime would be offset by higher legal certainty and lower costs, 
and also by the prospect of higher revenue stemming from a higher acceptance of AI-systems by 
reason of a more reliable compensatory regime. 

Insurance solutions would allow limiting liable parties’ financial burden to the annual premium, 
thus making liability costs predictable and manageable. This would be particularly beneficial for 

114 

 
 
SMEs, including micro-enterprises, which are disproportionately affected by legal uncertainty and 
fragmentation,  and  for  which  a  liability  event  is  likely  to  be  more  disruptive  than  for  large 
companies. These effects  of insurance would be direct  impacts  of Options 2 and 3 to  the extent 
that  those  options  envisage  a  mandatory  insurance  regime  for  the  operation  of  AI-applications 
having a specific risk profile. Such a regime is, however, expected to impose an initial burden on 
those  operators  who  would  not  have  taken  out  insurance  voluntarily.  Mainly  due  to  a  lack  of 
historic  data  regarding  the  liability  risk  of  AI,  but  also  due  to  other  costs  to  be  recouped  by 
insurers, insurance premiums are likely to be higher than warranted by the actual liability exposure 
during an initial period. Yet, that initial burden is likely to be mitigated by the safety benefits of 
AI-systems, which are expected to outweigh premium-driving factors: thanks to higher safety of 
AI-systems, operators’ insurers are likely to be less exposed to costs of compensation than in the 
status quo, which would have in turn a positive impact on the level of premiums. Moreover, the 
proposed Artificial Intelligence Act is expected (notably through documentation requirements for 
the development  and use of AI-systems) to enable a more informed risk-assessment by insurers, 
who are also likely to manage the initially lower predictability of AI-related risks by relying on re-
insurance  and  tools  such  as  real-life  testing  of  AI-systems,  advanced  analytics,  and  catastrophe 
models adapted to the AI context. Although it is too early to predict the level of competition in the 
insurance market for AI, as actuarial data becomes available in the medium term and is analysed 
with the help of advanced analytics, the insurance and re-insurance market for AI-specific liability 
risks  could  also  become  ever  more  competitive,  which  would  lead  to  lower  premiums.  Liability 
insurance  is  expected  to  play  an  important  role  also  in  attenuating  possible  shifts  of  liability-
related costs connected to the envisaged adaptations of the burden of proof: As potentially liable 
entities are in many cases likely to be covered by voluntary insurance, their exposure would again 
be limited to the premiums. 

Options 2 and 3 would also improve significantly the situation of victims of damage caused by 
AI;  these  options  would  address  the  challenges  highlighted  with  regard  to  the  current  legal 
framework through the harmonisation of strict liability for the operation of AI-applications with a 
specific  risk-profile,  and  targeted  adaptations  to  the  burden  of  proof  (irrespective  of  the  risk-
profile) for other AI-applications, whose opacity and high degree of autonomy challenge liability 
rules and may hinder the path to compensation for victims. 

3.4. Methodological constraints and measures taken to remedy them 

(a) Scope 

The study focused on national rules on non-contractual liability other than direct transpositions of 
the PLD. This  approach  was chosen with  a view to  complementing the information  gathered by 
the evaluation of the PLD252 and the separate impact assessment study on the review of the PLD, 
to  enable  a  holistic  assessment  of  the  need  for  policy  measures  regarding  liability  for  AI.  The 
study  report  underlined  that  any  EU-level  regulatory  solution  needs  to  rely  on  a  coordinated 
assessment  of  the  potential  gaps  of  the  PLD  and  national  liability  rules.  Such  a  coordinated 
assessment  is  delivered  by  this  IA,  which  explains  how  the  AI-specific  problems  and  policy 

252 SWD(2018) 157 final, 7.5.2018. 

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measures  are  coherent  and  complementary  with  the  technology-neutral  measures  regarding  the 
PLD. 

(b) Low response rate to data gathering surveys and mitigating actions taken 

Despite the efforts by the study team, only a small percentage of the stakeholders contacted – both 
stakeholders across the various use cases, and insurance companies –provided specific input: 

-  Some of the stakeholders indicated that it is too early for them to provide opinions on the 
research questions of the study. This  is  because,  despite planning to  use  AI in  the future 
(or, in the case of insurance companies, planning to insure AI activities), such stakeholders 
have not yet had the chance to define concrete business plans and reflect thoroughly on the 
question of liability.  

-  Several  trade  associations  indicated  that  only  a  few  of  their  member  companies  had  an 
informed  opinion  on  this  topic,  and  since  the  majority  of  them  was  not  ready  yet,  they 
preferred not to participate.  

-  Other  stakeholders  said  that  given  the  absence  of  case  law  or  practical  examples  of 
problems arising out of damage caused by AI, it is difficult to make reliable estimates on 
the  impact  of  uncertainty  and  fragmentation  at  the  present  time.  This  feedback  points  in 
general  to some stakeholders’ perception that, since the market  for AI is  still in  its early 
stages and they have not yet experienced liability-related issues, they are not in a position 
to assess the legal framework. In turn, they are also not in a position to assess the policy 
options. 

-  Several  stakeholders,  especially  SMEs  and  associations,  although  interested  in  the  topic, 
were forced to decline participation due their resources being allocated to dealing with the 
COVID-19 crisis. 

Mitigating measures  taken:  The surveys  were  kept  longer than initially planned to  allow more 
stakeholders to show their interest and prepare their replies. Contacts of backup associations and 
companies  have  also  been  mobilised  to  make  up  for  the  lack  of  replies.  The  study  team  also 
proposed  alternative  semi-structured  interviews,  based  on  the  same  questions  included  in  the 
survey.  This  has  allowed  the  study  team  to  increase  the  number  of  respondents  considerably. 
Given  the  nevertheless  low  overall  response  rate  to  the  surveys  and  other  consultation  activities 
conducted  for this study, the conclusions  drawn from those surveys and  activities should not  be 
considered  as  statistically  representative.  However,  additional  desk  research  (on  other  studies, 
literature,  and  economic  theory)  has  been  performed  by  the  study  team  in  order  to  complement 
stakeholder input. On this basis, relevant overall conclusions could be drawn despite the limited 
amount of feedback received. 

(c) Quantification challenges and mitigating actions taken253 

The  study  team  had  difficulties  collecting  quantitative  data  –  even  in  the  form  of  order  of 
magnitudes or ranges – from respondents to the surveys and from interviewees. Only a minority of 
respondents were able to identify the cost categories affected by legal uncertainty and future legal 

253  For  further  explanations  regarding  the  scarcity  of  quantified  data  and  measures  implemented  to  overcome  this 
challenge, see Annex 10, point 1.2. 

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fragmentation, and almost no respondent was able to estimate the amount of costs and burdens due 
to these challenges. There are two major reasons for this: 

-  Some  respondents  indicated  that,  even  though  they  do  see  legal  uncertainty  and  future 
fragmentation as a challenge, the impact of these challenges on their cost structure is too 
indirect and widespread to be quantified. Respondents indicated that they do not map their 
costs to the detail needed to map the impact of uncertainty and fragmentation. 

-  Other  respondents  pointed  to  a  reason  similar  to  that  highlighted  above  on  the  low 
response rate, i.e. that uncertainty and fragmentation are likely to lead to additional costs, 
but it is too early to measure their impact. This is especially true for legal fragmentation, as 
the research question concerns potential legal developments. 

Mitigating measures taken: In order to deliver as meaningful results as possible despite the lack 
of quantified data, the study team reinforced the qualitative analysis regarding the impact of legal 
uncertainty, fragmentation, and of the policy options. For the policy options, the study team also 
had recourse to literature, market reports and case studies per use case in order to extract numbers 
to be used as a general benchmark in the cost-benefit analysis. 

4.  Explanations on how the European Parliament’s legislative own-initiative resolution on 

a civil liability regime for AI was taken into account 

4,1.  Context 

On  20  October  2020,  the  EP  adopted  a  legislative  own-initiative  resolution  requesting  the 
Commission  to  adopt  a  proposal  for  a  civil  liability  regime  for  artificial  intelligence  based  on 
Article 114 TFEU.254  The  EP  requested  the  Commission,  on  the  basis  of  Article  225  TFEU,  to 
submit  a  proposal  for  a  Regulation  on  liability  for  AI  and  provided  a  fully-fledged  text  for  the 
proposed Regulation. 

The EP’s Resolution sees no need for a complete revision of liability regimes, but considers that 
AI requires specific adjustments to liability regimes to avoid that potential victims end up without 
compensation. It regards liability for AI as one of the key aspects within the regulatory framework 
for AI.  

The Resolution focuses on claims against the operator of AI because the operator is controlling the 
risk  associated  with  the  AI-system  and  is  in  many  cases  the  first  visible  contact  point  for  a 
potential  victim.  It  uses  a  broad  definition  of  operator,  which  covers  both  the  frontend  operator 
(the person who is in control over the operation of the AI system) and the backend operator (the 
person  who  continuously  defines  the  features  of  the  AI  system  and  provides  data  and  essential 
backend  support  service).  The  proposed  Regulation  covers  damage  to  life,  health,  physical 
integrity, property and significant immaterial harm resulting in a verifiable substantial economic 
loss. In this respect, the EP requested the Commission to analyse the legal traditions in all MS and 
the existing national laws that grant compensation for immaterial harm, in order to evaluate if the 
inclusion of immaterial harm is necessary and if it contradicts the existing Union or national legal 

254 European Parliament resolution of 20 October 2020 with recommendations to the Commission on a civil liability 
regime for artificial intelligence (2020/2014(INL)). 

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framework.  It  also  requested  the  Commission  to  evaluate  the  need  for  provisions  to  prevent 
contractual non-liability clauses.255  

Following the suggestions made by  the Commission  White Paper/Report on AI  liability, the EP 
resolution uses a risk-based approach consisting of strict liability for a limited number of high risk 
AI and fault-based liability for all other AI.  

‘High-risk’  is  defined  as  a  significant  potential  to  cause  harm  or  damage  in  a  manner  that  is 
random and goes beyond what can reasonably be expected; the interplay between the severity of 
possible harm or damage, the degree of autonomy of decision-making, the likelihood that the risk 
materializes  and  the  manner  and  the  context  in  which  the  AI-system  is  being  used  is  taken  into 
account. In order to achieve legal certainty, all AI-systems that fall in the high-risk category would 
be exhaustively listed in an annex to the Regulation. This annex, i.e. the specification of what is 
high-risk AI, is left to the requested Commission proposal. The Commission would be given the 
power to amend the annex through delegated acts.  

According  to  the  EP’s  recommendations,  both  the  frontend  and  the  backend  operator  should  be 
obliged  to  take  out  insurance  coverage  for  their  harmonised  strict  liability.  To  the  extent 
compulsory  insurance  regimes  under  EU  or  national  law  already  cover  the  operation  of  the  AI-
system,  the  insurance  obligation  under  the  draft  Regulation  would  be  deemed  fulfilled.  In  the 
event that the insurer of the operator compensates the victim, any civil liability claim of the victim 
against another person for the same damage would be subrogated to the insurer.  

For  other  than  high-risk  AI-system,  the  EP’s  proposal  provides  for  fault-based  liability,  unless 
stricter national laws are in force. In this respect, it follows a minimum harmonisation approach. 
The fault-based liability is coupled with a presumption of fault of the operator, who can exculpate 
itself by proof of abiding by its duties of care. The draft Regulation defines certain circumstances 
under  which  the  operator  shall  not  be  liable  and  mentions  aspects,  which  shall  not  allow  the 
operator to escape liability. 

4.2.  How does PO1 take into account the European Parliament’s recommendations? 

PO1 takes up the EP’s recommendations to ease the burden of proof on victims under fault-based 
liability.  It  is  aligned  with  the  EP’s  conclusion  that  only  specific  harmonised  adjustments  to 
existing  liability  regimes  are  necessary  to  avoid  AI-induced  compensation  gaps.  Moreover,  like 
the EP’s proposed provisions, it is without prejudice to national liability and burden of proof rules 
that are more favourable to the victim (minimum harmonisation approach). 

However, PO1 follows a slightly more targeted approach than that proposed by the EP, in order to 
intervene only with respect to those specific issues that are linked to the particular challenges of 
AI. For instance, PO1 would not harmonise the substantive conditions of liability, e.g. the standard 
of  fault  (which  is  included  to  a  certain  extent  in  the  EP’s  resolution  in  the  form  of  defences 
available to the operator). Moreover, PO1 would not go so far as introducing a general reversal of 
the  burden  of  proof  for  the  use  of  AI-enabled  products  or  provision  of  AI-enabled  services. 
Likewise,  it  would  not  harmonise  elements  such  as  recourse  between  several  liable  parties,  the 
types  of  compensable  harm  or  the  calculation  of  damages  in  specific  cases  (such  as  death).  It 
would instead be limited to targeted alleviations of the exercise of existing national proof-related 
rules.  

In another respect, PO1 is designed in a more general way than the EP’s proposal: the envisaged 
alleviations of the burden of proof are not limited to claims against the ‘operator’, but can help the 

255 For the follow-up to both of these requests, see Annex 9 below. 

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victim to substantiate its claims also vis-à-vis any parties potentially liable for damage caused by 
AI under national fault-based liability rules.  

These  differences  are  due,  on  the  one  hand,  to  the  Commission’s  approach  of  limiting  its 
intervention to measures strictly warranted by the specific challenges posed (only) by the peculiar 
characteristics  of  certain  AI  systems.  On  the  other  hand,  they  follow  from  the  Commission’s 
objective to ensure that victims who suffer harm caused with the involvement of AI have the same 
level of protection as victims of harm caused by other technologies. This objective applies across 
all  existing  routes  of  compensation,  including  fault-based  claims  against  any  ‘wrongdoer’,  not 
only vis-à-vis the ‘operator’.  

In  addition,  PO1  (like  the  other  policy  options)  would  most  likely  be  implemented  through  a 
Directive, in order to allow Member States the necessary flexibility to insert the harmonised rules 
into their civil and procedural legal orders. Legislating by way of a directly applicable Regulation, 
as  proposed  by  the  EP,  would  achieve  more  far-reaching  harmonisation.  However,  it  would 
foreseeably be difficult to reach a political compromise on such an instrument, as any rules in the 
area  of  civil  and  procedural  law  have  to  fit  into  Member  States’  traditional  national  systems 
without  creating  frictions  or  inconsistencies.  For  reasons  of  subsidiarity  and  coherence  with  the 
existing legal systems, a Directive could be preferable. 

Finally, PO1 does not reflect the EP’s recommendation to harmonise strict liability for certain AI 
technologies  with  a  special  risk-profile.  This  element,  which  involves  a  more  far-reaching 
harmonised intervention into existing liability rules, is taken into consideration by the other policy 
options. 

4.3.  How does PO2 take into account the European Parliament’s recommendations?  

As regards the envisaged measures to ease the victim’s burden of proof under fault-based liability 
rules, the explanations regarding PO1 apply also in the context of PO2. 

PO2 would also take up the EP’s recommendations regarding the harmonisation of a strict liability 
regime  for  the  operation  of  AI  applications  with  a  special  risk  profile,  coupled  with  mandatory 
insurance.  In  order  to  ensure  legal  certainty  and  a  clear  delineation  of  the  scope  of  the  possible 
strict  liability  and  mandatory  insurance  regimes,  PO2  could  be  implemented  through  a  list  of 
specific  AI  use  cases  in  an  Annex,  as  proposed  by  the  EP.  Moreover,  the  Commission  could 
follow  the  EP’s  recommendation  to  allow  for  the  amendments  of  this  list,  by  way  of  delegated 
acts, in order to keep the legislative instrument updated in light of fast technological developments 
in the field of AI.  

By  contrast,  in  line  with  the  Commission’s  targeted  approach,  those  elements  from  the  EP’s 
resolution  that  are  not  strictly  linked  to  addressing  the  specific  (proof-related)  challenges  of  AI 
would  not  be  harmonised  under  PO2  (e.g.  statutory  limitation,  calculation  of  damages,  recourse 
between jointly liable parties). 

4.4.  How  does  PO3  (the  preferred  policy  option)  take  into  account  the  European 
Parliament’s recommendations?  

Like  PO2,  PO3  reflects  both  elements  recommended  by  the  EP,  albeit  as  regards  strict  liability 
only  in  a  staged  approach.  The  staged  approach  takes  into  account  the  EP’s  call  for  strictly 
evidence-based and proportionate, yet future-proof measures.  

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Annex 5 

Detailed explanations on how the specific characteristics of AI are 
challenging existing liability rules 

In  the  absence  of  targeted  measures  to  ease  the  burden  of  proof  or  AI-specific  strict  liability 
regimes, the specific characteristics of certain AI-systems may make it unduly difficult for victims 
to meet the burden of proof.256 This Annex explains in more detail which specific characteristics of 
AI  challenge  existing  liability  rules,  and  how.  For  illustrative  use-cases  elaborated  in  cooperation 
with AI experts from the JRC, see Annex 13. 

1. Autonomous behaviour  

AI  systems  can  increasingly  perform  tasks  with  less,  or  entirely  without,  direct  human 
intervention.257  The  level  of  autonomy  of  AI  systems  is  a  continuum,  ranging  from  fully 
supervised and controlled systems to more independent ones that combine environmental feedback 
with an analysis of their current situation and can perform tasks without direct human intervention, 
even when operating in a complex environment. While the high-level objectives of AI systems are 
always defined by humans, for a number of AI systems their outputs and the mechanisms to reach 
these objectives are not concretely specified, enabling automated decision-making, within pre-set 
boundaries, without the involvement  of a human operator. An  increasing degree of autonomy of 
the system is one of the key aspects sought by the users of certain AI systems. While it is in some 
cases technically necessary for the automation of a certain task, and commercially desirable,  this 
characteristic  makes  it  difficult  to  prove  the  link  between  a  damaging  output  of  the  AI 
system and the action or omission of a potentially liable person, as required under fault-based 
liability rules. It is very difficult to attribute directly the damaging output to an action/omission of 
a  human  liable  person  if  between  the  human  act  of  deploying  the  AI  system  and  the  damage 
caused by the AI system to a victim, the AI-system takes a highly autonomous action which is not 
pre-determined  by  a  human.  To  prove  such  a  link  is  however  a  necessary  condition  for  a 
successful liability claim. 

2. Opacity/lack of transparency and explainability 

Some AI systems lack transparency because the rules followed, which lead from input to output, are 
not  fully  pre-determined  by  a  human.  Due  to  the  opacity  of  certain  AI  systems,  it  may  not  be 
possible  to  explain  how  the  output  is  exactly  derived  from  its  input  in  a  given  context.  An  AI 
system  can  namely  be  opaque  with  respect  to  how  exactly  it  functions  as  a  whole,  how  the 
algorithm was realised in code and how the programme actually runs in a particular case, including 
the hardware and input data. As a result, some AI systems are opaque in a way (‘black box effect’) 
that other digital systems are not. Where damage happens, the opacity of an AI system can make it 
very challenging to establish which input data lead to a specific output creating a damage, and how. 
In the absence of a targeted alleviation of the burden of proof, and targeted rules on the disclosure 
of information  on AI-systems  in  the context  of civil  proceedings,  this can make it very hard or 

256 Certain AI systems may include only some of the relevant characteristics and may include mitigating mechanisms 
to  reduce  negative  effects  of  some  of  these  characteristics.  As  a  rule,  the  more  specific  characteristics  a  given  AI 
system  has,  the  higher  the  probability  that  it  becomes  a  ‘black  box’.  For  explanations  about  how  the  AI  Act  will 
impact the relevant characteristics of ‘high-risk’ AI systems, see Annex 7. 
257  Report  from  the  Expert  Group  on  Liability  and  New  Technologies  –  New  Technologies  Formation,  European 
Commission, 2019, p.33 

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even  impossible  for  victims  to  prove  the  link  between  the  harmful  AI  output  and  a  human 
action  or  omission.  These  difficulties  are  compounded  by  the  fact  that  any  human  behaviour  in 
developing or using an AI system will be accompanied and followed by built-in processes of the AI 
system  that  in  turn  depend  upon  a  variety  of  internal  and  external  factors  independent  from  such 
conduct, for instance input data collected by sensors, which makes it again more difficult to link a 
harmful output to a human action or omission.258 

3. Complexity 

Advanced  AI  models  frequently  have  more  than  a  billion  parameters  and  process  very  large 
amounts of data. In addition, AI systems are usually combined together in complex systems in real-
world  scenarios  (e.g.  in  a  robot  many  AI  systems  for  perception,  navigation,  control,  can  be 
integrated).  Due  to  this  multiplicity  of  elements  constituting  such  AI  systems,  they  may  not  be 
understandable in practice for humans. While some AI systems can be comprehensible from an ex-
post  perspective  despite  their  complexity  (e.g.  complex  rule-based  systems),  complexity  can 
contribute  to  a  lack  of  explainability  of  outputs  in  other  cases.  In  the  absence  of  a  targeted 
alleviation  of the burden of proof, e.g. in  the  form  of targeted presumptions easing the burden of 
proof,  this  can  make  it  difficult  to  establish  that  a  certain  wrongful  human  behaviour 
triggered the AI output that caused the relevant damage. 

4. Continuous adaptation and lack of predictability 

Some AI systems  can ‘learn’ while in use.  In these cases, the rules being followed by the system 
adapt  based  on  the  input  it  receives  while  in  use.  Such  continuous  adaptation  will  render  an  AI-
system more unpredictable. Machine learning based on continuous adaptation may therefore make 
it  impossible  to  prove  the  link  between  the  damaging  AI  output  and  a  specific  cause,  in 
particular  the  fault  of  a  person.  But  limited  predictability  concerns  not  only  AI  systems  that 
continuously  adapt  while  in  use.  Learning-based/data  driven  AI  systems  in  general  have  a 
probabilistic  behaviour  as  they  are  based  on  data  that  usually  does  not  represent  all  possible 
scenarios.  Outputs  of  these  systems  have  a  high  input  sensitivity  and  even  a  small  change  in  the 
inputs  can  lead  to  an  unpredictable  behaviour  of  the  system,  and  thus  a  lack  of  predictability. 
Moreover,  while  recurrent  systems  that  depend  on  the  internal  state  of  an  AI  system  are  not 
necessarily designed for continuous adaptation, they nevertheless produce different outputs for the 
same inputs at different times, even when exposed to the same input. The challenges regarding the 
attribution  of  AI  output  for  the  purposes  of  liability  claims  hence  apply  more  broadly  than  in  the 
context of continuous adaptation. 

The results of the public consultation overall confirm the assessment that the specific characteristics 
can  make  it  prohibitively  difficult  or  even  impossible  for  victims  to  obtain  compensation  under 
existing  civil  liability  rules:  Amongst  responding  consumer  organisations,  NGOs,  public 
authorities,  academic/research  associations  and  EU  citizens,  overwhelming  majorities  agreed 
that  

(i)  it  could  be  difficult  to  link  damage  caused  by  highly  autonomous  AI  to  the  actions  and  or 
omissions of a human actor,  

(ii) in the case of opaque and complex AI, it could be difficult for injured parties to prove that the 
conditions of liability are fulfilled, and  

258 Comparative Law Study, p. 48. In this respect, see also the impact assessment accompanying the proposed AI Act, 
op. cit.,  p. 28,  concluding  that  opacity  (lack  of  transparency)  and  complexity  make  it  difficult  to  prove  possible 
breaches of laws. 

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(iii)  because  of  AI’s  specific  characteristics,  victims  may  in  certain  cases  be  less  protected  that 
victims of damage that did not involve AI.  

While  responding  business  stakeholders  (business  associations  and  companies/business 
organisations)  were  more  divided  about  these  points,  a  relative  majority  nevertheless  agreed  with 
the  second  statement  (41,2 %  v.  28,3 %  who  disagreed),  and  even  an  absolute  majority  with  the 
third  (53,6 %  v.  26,2 %  who  disagreed).  Only  with  respect  to  statement  (i),  more  business 
stakeholders disagreed (44,7 %) than agreed (35,7 %). 

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Annex 6 

Detailed description of the legal context and interplay between 
existing / proposed legal rules and the AI liability initiative 

Introduction and scope of this Annex 

This  Annex  contains  information  on  the  relevant  existing  legal  instruments  of  EU  and 
international  law,  and  explains  how  the  proposal  on  AI  liability  would  complement  and  interact 
with the relevant rules.  

Only  legislation  that  is  closely  linked  with  the  presented  proposal  is  described.  While  a  broad 
range  of  EU  level  rules,  for  instance  under  sector-specific  safety  legislation,  can  be  indirectly 
relevant for establishing the duties of care for individual liability claims, not all of these rules are 
described here, as  they  do not  have an immediate impact  on the policy  objectives and measures 
covered by this IA.  

Moreover, the existing EU law instruments aimed at addressing fundamental rights risks linked to 
AI  (in  particular  discrimination  risks,  data  protection  risks,  and  risks  linked  to  the  use  of  AI  by 
large online platforms) and the interplay of the AI liability initiative with the AI Act are covered in 
detail  by  dedicated  Annexes  (Annex  7  and  8  respectively).  Therefore,  these  aspects  are  not 
repeated in this Annex. 

Finally, an overview of existing national civil liability rules, characterised by various differences 
between Member States, is provided in the main part of this IA and therefore not covered in this 
Annex.  For  details,  reference  is  made  to  the  Comparative  Law  Study  on  Civil  Liability  for  AI, 
commissioned for this IA.259 

As the transport field in particular is characterised by various relevant legal instruments at EU and 
international  level,  this  field  is  covered  in  the  dedicated  section  B.  Relevant  EU  law  rules  from 
other fields are discussed in section A. 

A. EU law context (except transport-specific instruments) 

1. Product Liability Directive 

1.1. Description 

259  Comparative 
01aa75ed71a1/language-en  

law 

study,  https://op.europa.eu/en/publication-detail/-/publication/8a32ccc3-0f83-11ec-9151-

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The  Product  Liability  Directive  (PLD)260  establishes  a  liability  regime  of  producers  when 
defective  products  cause  personal  injuries,  death  or  damage  to  property  of  natural  persons.  It  is 
without prejudice to national provisions relating to non-material damage261. 

The PLD covers all types of tangible products, ranging from raw materials to complex industrial 
products,  including  emerging  digital  technology  products.  It  applies  only  to  defective  products. 
According to Article 6  PLD,  a product  is  “defective” when it does not  provide the safety that  a 
person  is  entitled  to  expect,  taking  into  account:  (a)  its  presentation;  (b)  its  reasonable/expected 
use; and (c) the time when it was put into circulation. 

The victim does not have to prove a fault of the producer. However, the victim bears the burden of 
proof regarding the defect in the product, the actual damage and the causal link between the defect 
and the damage  

Pursuant to Article 3 PLD, “producer” means the manufacturer of a finished product, the producer 
of any raw material or the manufacturer of a component part and any person who, by putting his 
name, trademark or other distinguishing feature on the product presents himself as its producer.262  

Under certain circumstances, the producer is exempt from strict liability under the PLD. Article 7 
provides for exemptions in six cases. With respect to liability for damage caused by AI, the ‘later 
defect  defence’  and the ‘development  risk defence’ are particularly relevant.  In  accordance with 
the ‘later defect defence, the producer is not liable if he proves that it is probable that the defect 
which caused the damage did not exist at the time when the product  was put into circulation by 
him or that this defect came into being afterwards. Based on the ‘development risk defence’, the 
producer is not liable if he demonstrates that the state of scientific and technical knowledge at the 
time when he put the product into circulation was not such as to enable the existence of the defect 
to be discovered. 

1.2. PLD review and interplay with the AI liability initiative 

In the  framework of the pending PLD review assessed in  parallel to  the  AI liability initiative, a 
number of horizontal (i.e. not AI-specific) amendments of the Directive are envisaged, notably to 
modernise it and adapt it to the digital age. These measures and the interplay with the AI-specific 
measures covered by this impact assessment are explained in detail in the impact assessment itself 
and are therefore not repeated in this Annex. 

2. Rome II Regulation 

2.1. Description 

260 Council Directive 85/374/EEC of 25 July 1985 on the approximation of the laws, regulations and administrative 
provisions of the Member States concerning liability for defective products, OJ L 210 , 7.8.1985, p. 29–33. 
261 Non-material damage can be defined as “losses which do not relate to a person’s assets, wealth or income and, as 
such, cannot be quantified in an objective manner by reference to a market price or value” (CJEU C‑371/12). 
262 Pursuant to Article 5 of the Product Liability Directive, where two or more persons are liable for the same damage, 
they shall be liable jointly and severally without prejudice to the provisions of national law concerning the rights of 
contribution or recourse. 

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The Rome II Regulation263 sets out the conflict-of-law rule for non-contractual obligations. As a 
general rule, the law applicable to a non-contractual obligation arising out of a tort/delict is the law 
of the country in which the damage occurs (Article 4). It is not important whether the event which 
gave rise to the damage, or any potential indirect consequences of that event occurred in the same 
country. In situations where the liable person and the victim both have their habitual residence in 
the same country at the time when the damage occurs, the law of that country applies. Finally, in 
situations where it is clear from all the circumstances of the case that the tort/delict is manifestly 
more  closely  connected  with  a  country  other  than  that  where  the  damage  occurred  or  in  which 
both the liable person and the victim have their habitual residence, the law of that other country 
shall apply.  

For  claims  based  on  product  liability,  the  Rome  II  Regulation  determines  the  applicable  law  by 
means  of  a  cascade  system,  where  the  first  element  to  be  taken  into  account  is  the  law  of  the 
country  in  which  the  person  sustaining  the  damage  had  his  or  her  habitual  residence  when  the 
damage occurred, if the product was marketed in that country. The other elements are triggered if 
the  product  was  not  marketed  in  that  country  and,  in  essence,  the  law  applicable  to  the  claim 
depends on the place where the product was marketed (Article 5). If the liable person under the 
product liability regime and the victim both have their habitual residence in the same country at 
the  time  when  the  damage  occurs,  the  law  of  that  country  applies.  Where  there  is  a  manifestly 
closer connection to another country, the law of that country applies.  

2.2. Interplay with the AI liability proposal 

The  AI  liability  proposal  does  not  contain  provisions  determining  the  applicable  law,  while  the 
Rome II Regulation does not contain provisions on the substantive conditions of liability or proof-
related rules. The two instrument hence complement one another without overlapping. Indeed, the 
application of the Rome II Regulation leads to a situation where various different national liability 
rules  can  apply  to  the  same  type  of  AI-induced  damage.  In  such  a  situation,  the  expected 
emergence of AI-specific national liability rules would lead to legal fragmentation and obstacles to 
cross-border  trade  in  AI-enabled  products  and  services  (e.g.  due  to  the  lack  of  affordable  cross-
border  insurance  cover,  increased  legal  information,  financing  and  risk  management  costs).  The 
fact that Article 14(1) of the Rome II Regulation allows parties to agree to submit non-contractual 
obligations  to  the  law  of  their  choice  in  certain  cases  does  not  negate  these  problems.  Firstly, 
contractual choice of law clauses are permissible only in B2B, not vis-à-vis parties not pursuing a 
commercial activity. Secondly, even in B2B, such clauses are by nature only possible if parties are 
already  in  a  direct  commercial  relationship  before  the  damage  occurs;  the  legal  fragmentation 
problem can therefore not be resolved by such clauses when it comes to third party damage. 

However,  when  the  victim  is  a  third  party  (business  or  private  individual)  who  is  not  in  a 
contractual relationship with the liable party before the damage happens, the applicable law will 
almost always be determined by the general rules of the Rome II Regulation. The liable party and 
a third party victim are unlikely to agree contractually to choose a certain law after the damage has 

263 Regulation (EC) No 864/2007 of the European Parliament and of the Council of 11 July 2007 on the law applicable 
to non-contractual obligations (Rome II), OJ L 199, 31.7.2007, p. 40–49. 

125 

 
 
                                                           
 
already happened, as the law applicable by default will always be more advantageous for one or 
the other party than alternative laws. 

The  AI  liability  proposal  aims  at  preventing  the  internal  market  barriers  linked  inter  alia  to 
fragmentation between future AI-specific national liability rules. 

3. E-Commerce Directive and Digital Services Act: Liability exemptions 

3.1. Description 

The  E-Commerce-Directive264  establishes  certain  limitations  of  the  liability  of  intermediary 
service  providers.  In  particular,  “hosting”,  “caching”  and  “mere  conduit”  service  providers  are 
exempted, under certain conditions, from liability for the third-party information they transmit or 
store.  These  exemptions  cover  only  cases  where  the  activity  of  the  information  society  service 
provider  is  limited  to  the  technical  process  of  operating  and  giving  access  to  a  communication 
network  over  which  information  made  available  by  third  parties  is  transmitted  or  temporarily 
stored,  for  the  sole  purpose  of  making  the  transmission  more  efficient.  They  are  based  on  the 
consideration  that  such  activities  are  of  a  mere  technical,  automatic  and  passive  nature,  which 
implies that the information society service provider has neither knowledge of nor control over the 
information which is transmitted or stored. 

The proposed Digital Services Act265 maintains these liability exemptions and transfers them into a 
Regulation. 

3.2. Interplay with the AI Liability Proposal 

The  AI  liability  proposal  does  not  address  the  substantive  conditions  under  which  providers  of 
intermediary services are liable. It hence does not affect the existing liability exemptions.  

4. Directive 2004/35/EC on environmental liability 

4.1. Description 

The Directive on environmental  liability266 (ELD)  establishes a framework based on the polluter 
pays principle to prevent and remedy environmental damage.  

The  Directive  defines  "environmental  damage"  as  damage  to  protected  species  and  natural 
habitats, damage to water and damage to soil. Operators carrying out dangerous activities listed in 
Annex III of the Directive fall under strict liability (no need to proof fault). Operators carrying out 

264 Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects  of 
information  society  services,  in  particular  electronic  commerce,  in  the  Internal  Market  ('Directive  on  electronic 
commerce'), OJ L 178, 17.7.2000, p. 1. 
265 Proposal COM/2020/825 final for a Regulation of the European Parliament and of the Council on a Single Market 
For Digital Services (Digital Services Act) and amending Directive 2000/31/EC. 
266 Directive 2004/35/CE of the European Parliament and of the Council of 21 April 2004 on environmental liability 
with regard to the prevention and remedying of environmental damage (OJ L 143, 30.4.2004, p. 56). 

126 

 
 
 
 
                                                           
other  occupational  activities  than  those  listed  in  Annex  III  are  liable  for  fault-based  damage  to 
protected species or natural habitats. The establishment of a causal link between the activity and 
the damage is always required. Affected natural  or legal persons and environmental NGOs have 
the right to request the competent authority to decide about remedial action. 

4.2. Interplay with the AI Liability proposal 

As  the  ELD  deals  with  environmental  damage,  it  is  based  on  the  powers  and  duties  of  public 
authorities, as distinct from a civil liability system for damage to for instance property, economic 
loss  or  personal  injury.  Conversely,  the  AI  liability  proposal  deals  only  with  civil  liability.  The 
two instruments are this complementary without overlapping. 

5. Antitrust Damages Directive 2014/104/EU  

5.1. Description 

The  Antitrust  Damages  Directive267  helps  citizens  and  companies  to  claim  damages  if  they  are 
victims  of  infringements  of  EU  antitrust  rules,  such  as  cartels  or  abuses  of  dominant  market 
positions. The Directive pursues two complementary goals: First, it removes practical obstacles to 
compensation  for  all  victims  of  infringements  of  EU  antitrust  law.  For  this  purpose,  it  notably 
establishes  harmonised  rules  on  the  disclosure  of  evidence  from  the  defendant  or  a  third  party. 
Second,  the  Directive  fine-tunes  the  interplay  between  private  damages  actions  and  public 
enforcement of the EU antitrust rules by the Commission and national competition authorities. 

5.2. Interplay with the AI Liability proposal 

The  AI  Liability  Proposal  and  the  Antitrust  Damages  Directive  are  largely  covering  different 
issues.  While  the  former  is  aimed  at  addressing  the  particular  difficulties  of  proof  linked  to  the 
specific characteristics of AI, the latter facilitates (stand-alone and follow-on) actions for damages 
to ensure the effective compensation of harm caused by infringements of EU antitrust rules. 

In any event, the approaches of these two instruments are aligned. Both are meant   to ensure the 
effectiveness  of  EU  law  rules  and  existing  rights  in  civil  actions  for  damages  before  national 
courts..  While  the  AI  Liability  Proposal  is  linked  to  the  requirements  established  by  the  AI  Act 
and  designed  to  ensure  that  victims  can  claim  compensation  where  non-compliance  with  these 
requirements  causes  damage,  the  Antitrust  Damages  Directive  supports  the  application  of 
competition  rules  by  national  courts  when  ruling  on  disputes  between  (alleged  or  declared) 
competition law infringers and (alleged) injured part(ies). Both instruments recognise the crucial 
importance  of  access  to  information.  Therefore,  the  AI  Liability  proposal  harmonises  the 
conditions  for  victims’  access  to  information  about  AI-systems  documented  or  logged  in 
accordance  with  the  AI  Act,  and  the  Antitrust  Damages  Directive  contains  a  similar  provision 
(although not  linked to  AI systems  in  particular) applying in  the context of actions for damages 
based on infringements of antitrust rules. 

267  Directive  2014/104/EU  of  the  European  Parliament  and  of  the  Council  of  26  November  2014  on  certain  rules 
governing actions for damages under national law for infringements of the competition law provisions of the Member 
States and of the European Union (OJ L 349, 5.12.2014, p. 1). 

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6. Directive 2004/48/EC on the enforcement of intellectual property rights 

6.1. Description 

Directive 2004/48/EC on the enforcement of intellectual property rights268 provides for a minimum 
set  of  harmonised  measures,  procedures  and  remedies  facilitating  the  civil  enforcement  of 
intellectual  property  rights  (IPR)  across  the  internal  market.  Similarly  to  the  approach  of  the 
Antitrust  Damages  Directive,  Directive  2004/48/EC  notably  provides  for  the  possibility,  in  the 
framework  of  proceedings  concerning  an  IPR  infringement,  to  order  the  disclosure  lying  in  the 
control of the opposing party. In addition, Directive 2004/48/EC requires Member States to ensure 
that the holder of an infringed IPR can claim damages appropriate to the actual prejudice suffered 
as a result of the infringement. 

6.2. Interplay with AI Liability proposal 

Both  the  AI  Liability  Proposal  and  Directive  2004/48/EC  are  meant  to  leverage  ‘private 
enforcement’  to  ensure  the  effectiveness  EU  law  rules  and  existing  rights.  The  two  instruments 
complement one another in cases where highly autonomous, complex, opaque and unpredictable 
AI  systems  cause  IPR  infringements  in  contravention  of  EU  law.  In  such  cases,  the  targeted 
alleviation  of  the  burden  of  proof  as  regards  the  question  how  or  why  the  relevant  AI  system 
reached the harmful output will make it easier for the IPR right holder to claim compensation. The 
victim will namely be relieved from having to establish that a specific action or omission of the 
defendant was the causal trigger of the output that caused the IPR infringement, because this proof 
will  often  be  prohibitively  difficult  or  even  impossible  to  provide  under  existing  fault-based 
liability rules.  

7. Directive (EU) 2020/1828 on representative actions 

7.1. Description 

Directive (EU) 2020/1828 on representative actions for the protection of the collective interests of 
consumers269  empowers  organisations  or  public  bodies  designated  by  EU  countries  to  seek 
injunctive  or  redress  measures  on  behalf  of  groups  of  consumers  through  representative  actions 
(including cross-border representative actions). This includes seeking compensation from traders 
who  infringe  consumer  rights  in  areas  such  as  financial  services,  travel  and  tourism,  energy, 
health, telecommunications and data protection, as appropriate and available under EU or national 
law. The Directive leaves it to MS’ discretion whether the representative action can be brought in 
judicial or administrative proceedings, or both, depending on the relevant area of law or relevant 
economic sector. 

268  Directive  2004/48/EC  of  the  European  Parliament  and  of  the  Council  of  29  April  2004  on  the  enforcement  of 
intellectual property rights (OJ L 157, 30.4.2004, p. 45). 
269 Directive (EU) 2020/1828 of the European Parliament and of the Council of 25 November 2020 on representative 
actions  for  the  protection  of  the  collective  interests  of  consumers  and  repealing  Directive  2009/22/EC  (OJ  L  409, 
4.12.2020, p. 1). 

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7.2. Interplay with AI Liability proposal 

The AI Liability proposal does not address the question of representative actions (or standing in 
general). When it comes to civil proceedings, the AI Liability proposal would be synergetic with 
the Directive on representative actions.  Insofar as  damage caused by  AI  systems  is  at  stake, the 
measures  to  ease  the  burden  of  proof  would  ensure  the  effectiveness  of  existing  claims  also  in 
cases  where  victims  are  represented  by  consumer  organisations  or  bodies  in  accordance  with 
Directive (EU) 2020/1828. 

B. EU and international legal instruments from the transport field 

1. Road transport 

1.1. Motor Insurance Directive 

(a) Description 

The  Motor  Insurance  Directive  (MID)270  pursues  the  objectives  of  protecting  victims  of  motor 
vehicle  accidents  and  facilitating  the  free  movement  of  motor  vehicles  between  Member  States. 
All Member States have to ensure that civil liability for the use of vehicles is covered by insurance 
and  that  victims  of  an  accident  caused  by  a  vehicle  enjoy  a  direct  claim  against  the  insurer 
covering the person responsible against civil liability.  

Directive  (EU)  2021/2118  has  amended  certain  provisions  of  the  MID,  transposition  measures 
transposing  are to  apply from  23 December 2023. This  MID review has not  introduced  any AI-
specific  measures,  such  as  for  autonomous  vehicles.  Indeed,  the  last  evaluation  of  the  Directive 
concluded  that  no  changes  were  necessary  regarding  autonomous  vehicles.  Such  vehicles  will 
require third party liability insurance in line with the Directive.271 However, the Commission will 
submit a report, by December 2030, evaluating the application of the Directive specifically with 
regard  to  technological  developments,  in  particular  with  regard  to  autonomous  and  semi- 
autonomous vehicles. 

(b) Interplay with the AI liability initiative 

The policy measures envisaged under the AI liability initiative do not overlap with the MID. The 
MID  does  not  harmonise  issues  of  civil  liability  or  the  burden  of  proof.  Insofar  as  the  policy 
options assessed in this IA involve a mandatory insurance regime covering strict liability regime 
for  the  use  of  certain  AI-enabled  products  or  the  provision  of  certain  AI-enabled  services,  the 
scope of such an insurance obligation would specifically not include AI-enabled products, such as 
autonomous vehicles, of a category falling under the MID. 

270 Directive 2009/103/EC of the European Parliament and of the Council of 16 September 2009 relating to insurance 
against civil liability in respect of the use of motor vehicles, and the enforcement of the obligation to insure against 
such liability (OJ L 263, 7.10.2009, p. 11). 
271 
of  Directive 
on  REFIT 
consultation 
https://ec.europa.eu/info/consultations/finance-2017-motor-insurance_en. 

2009/103/EC 

on  motor 

insurance, 

review 

Public 

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1.2.  Regulation  (EU)  No  181/2011  concerning  the  rights  of  passengers  in  bus  and  coach 
transport  

(a) Description 

Pursuant to Article 7 of Regulation (EU) No 181/2011, passengers are entitled to compensation for 
death or personal injury as well as for loss of or damage to luggage due to accidents arising out of 
the use of the bus or coach in accordance with national law. The reference to national law covers 
contractual  and  extra-contractual  liability  regimes  and  does  not  specify  whether  the  national 
liability  law  should  be  fault-based  or  strict.  The  Regulation  lays  down  minimum  amounts  for 
liability  limits  set  out  in  national  law  (EUR 220 000  per  passenger  and  EUR 1 200  per  item  of 
luggage).272 

(b) Interplay with the AI Liability proposal 

The  policy  measures  envisaged  under  the  AI  liability  initiative  do  not  overlap  with  Regulation 
(EU) No 181/2011. The Regulation does not regulate the substantive liability conditions, liability 
for  third-party  damage  or  allocation  of  the  burden  of  proof.  As  far  as  liability  is  concerned,  the 
Regulation refers only to passengers’ right to compensation vis-à-vis bus or coach carriers. While 
the Commission proposal (COM/2008/817/FINAL) had contained a chapter with detailed rules on 
the liability of bus or coach undertakings with regard to passengers and their luggage, the Council 
replaced the proposed harmonised rules by a simple reference to the applicable national law. The 
effect is that the substantive conditions of liability are not regulated in existing EU law. Moreover, 
damage suffered by third parties, i.e. accident victims  other than passengers, is  not  addressed in 
Regulation (EU) No 181/2011 at all. 

1.3.  Directive  2010/40/EU  on  the  framework  for  the  deployment  of  Intelligent  Transport 
Systems in the field of road transport 

(a) Description 

Directive 2010/40/EU establishes a framework for the deployment and use of “systems in which 
information and communication technologies are applied in the field of road transport, including 
infrastructure, vehicles and users, and in traffic management and mobility management, as well as 
interfaces with other modes of transport”. Pursuant to Article 11 of that Directive, “Member States 
shall ensure that issues related to liability, concerning the deployment and use of ITS applications 
and services set out in specifications adopted [by the Commission], are addressed in accordance 

272 According to the evaluation of Regulation (EU) No 181/2011 concerning the rights of passengers in bus and coach 
transport  published  on  10  December  2021,  operators  attempt  to  limit  the  compensation  below  the  minimum 
compensation  of  EUR  1  200  per  item  of  luggage  to  which  passengers  are  entitled  under  the  Regulation  (Article 
7(2)(b)).  Serious  accidents  remain  relatively  rare  in  bus  and  coach  passenger  transport  and,  therefore,  liability  for 
injury  or  death  is  not  an  issue  thoroughly  commented  on  by  stakeholders.  However,  the  objective  of  harmonising 
these different rules in the interest of greater clarity for passengers remains relevant as passengers’ awareness of their 
rights  and  willingness  to  exercise  them  grow,  cf.  evaluation  of  Regulation  (EU)  No  181/2011  on  the  rights  of 
passengers  travelling  by  bus  and  coach,  https://transport.ec.europa.eu/transport-themes/passenger-rights/passenger-
rights-studies_en 

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with  Union  law,  including  in  particular  [the  PLD],  as  well  as  relevant  national  legislation”.  ITS 
information service providers usually include disclaimers to reduce liability. 

(b) Interplay with the AI Liability proposal 

The  policy  measures  envisaged  under  the  AI  liability  initiative  do  not  overlap  with  Directive 
2010/40/EU. The Directive does not regulate the substantive liability conditions, liability for third-
party damage or the allocation of the burden of proof. Similarly, it does not regulate the liability of 
road traffic management service providers. 

1.4.  UNECE  Convention  on  the  contract  for  the  international  carriage  of  passengers  and 
luggage by road (CVR) 

(a) Description 

The  CVR  is  the  only  international  agreement  establishing  bus  and  coach  passenger  rights.  Only 
four  Member  States  have  ratified  the  CVR:  HR,  CZ,  LV  and  SK.  It  applies  to  contracts  for  the 
cross-border  carriage  of  passengers  and  luggage  by  road.  Chapter  IV  of  the  CVR  provides  for 
strict liability of the carrier273 for passengers’ personal injuries and damage to luggage connected 
with the carriage, as well as different liability caps. Force majeure and own-fault of the passenger 
are  laid  down  as  defences.  If  a  third  party  contributed  to  the  damage,  the  carrier  is  jointly  and 
severally  liable,  without  prejudice  to  recourse  claims.  Pursuant  to  Article 18  CVR,  ‘in  all  cases 
governed  by  this  Convention,  proceedings  for  liability  on  any  grounds  whatever  may  not  be 
instituted against the carrier or against persons for whom he is responsible […] otherwise than on 
the terms and within the limits laid down in this Convention’. This includes contractual as well as 
extra-contractual liability claims. However, in the case of gross negligence or wilful misconduct, 
the carrier cannot invoke the limitations of his liability. 

(b) Interplay with the AI Liability proposal 

Damage  suffered  by  third  parties,  e.g.  physical  injury  or  property  damage  suffered  by  accident 
victims other than passengers or parties to a contract for carriage of goods, is not addressed at all 
by  the  CVR.  Neither  does  it  cover  possible  alleviations  of  the  burden  of  proof  borne  by  the 
claimant, in particular with respect to facts that may be obscured by AI systems. In addition, the 
convention does not cover the liability of road traffic management service providers. 

If  the  Commission  would  decide,  at  the  stage  of  the  targeted  review  envisaged  by  the  preferred 
policy  option  on  AI  liability,  to  adopt  harmonised  rules  on  strict  liability  of  users/operators  of 
certain  AI-enabled  technologies,  the  user’s/operator’s  strict  liability  may  apply  to  persons  and 
damages that fall also under the CVR provisions. In this respect, it could be clarified that Member 
States can continue to apply provisions which exclude, fix or limit carriers’ contractual liability in 

273 ‘Carrier’ is defined as ‘any person who in the course of trade or business, but acting other than as an operator of a 
taxi service […], undertakes […] to carry one or more persons and, where appropriate, their luggage, whether or not 
he  performs  the  carriage  himself’.  The  carrier  is  responsible  also  for  the  acts  of  any  persons  of  whose  services  she 
makes use for the performance of her obligations under the contract of carriage, as if such acts were her own. 

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accordance  with  the  CVR  also  to  extra-contractual  liability  (despite  the  fact  that  only  a  small 
number of Member States has ratified the CVR). 

1.5. UN Convention on the contract for the international carriage of goods by road (CMR) 

(a) Description 

All  EU  Member  States  have  ratified  the  CMR.  This  Convention  covers  contracts  for  the 
international carriage of goods by road in vehicles. Article 17 provides for no-fault liability of the 
carrier  for  loss  of  the  goods  and  damage  thereto,  and  lays  down  certain  grounds  of  relief  of 
liability. The carrier bears the burden of proof with respect to those grounds. In cases where loss, 
damage or delay  arising out  of carriage under the CMR gives rise to  an extra-contractual claim, 
the carrier may avail itself of the provisions of this Convention which exclude its liability or which 
fix or limit the compensation due, except in cases of wilful misconduct (Articles 28 and 29). This 
applies both to the grounds of relief listed above and to other limitations and exclusions. 

(b) Interplay with the AI Liability proposal 

Damage  suffered  by  third  parties,  i.e.  accident  victims  other  than  passengers  or  parties  to  a 
contract for carriage of goods, for instance physical injury or property damage, is not addressed at 
all  by  the  CMR.  Neither  does  it  cover  possible  alleviations  of  the  burden  of  proof  borne  by  the 
claimant, in particular with respect to facts that may be obscured by AI-systems. In addition, the 
Convention does not cover the liability of road traffic management service providers. 

If  the  Commission  would  decide,  at  the  stage  of  the  targeted  review  envisaged  by  the  preferred 
policy  option  on  AI  liability,  to  adopt  harmonised  rules  on  strict  liability  of  users/operators  of 
certain  AI-enabled  technologies,  the  user’s/operator’s  strict  liability  may  apply  to  persons  and 
damages that fall also under the CMR provisions. With respect to damage to passengers, luggage 
or  transported  goods,  the  CMR  prescribes  strict  contractual  liability  of  the  carrier  and  certain 
limits, conditions and defences that cannot be undermined through extra-contractual liability rules. 
Therefore,  a  provision  clarifying  that  Member  States  can  continue  to  apply  provisions,  which 
exclude,  fix  or  limit  carriers’  contractual  liability  in  accordance  with  the  CMR,  also  to  extra-
contractual liability could be considered if strict liability were to be harmonised at the stage of the 
targeted review. 

2. Air transport 

2.1. Regulation on insurance requirements for air carriers and aircraft operators 

(a) Description 

Regulation  (EC)  No  785/   2004  on  insurance  requirements  for  air  carriers  and  aircraft  operators274 
establishes “minimum insurance requirements for air carriers275 and aircraft operators276 in respect 

274  Regulation  (EC)  No  785/  2004  of  the  European  Parliament  and  of  the  Council  of  21  April  2004  on  insurance 
requirements for air carriers and aircraft operators, OJ L 138, 30.4.2004,1 , as last amended by Regulation (EU) 2019/  
1243 of the European Parliament and of the Council of 20 June 2019 adapting a number of legal acts providing for the 

132 

 
 
 
                                                           
 
of passengers, baggage,  cargo and third parties” (Article 1) while flying  “within, into, out  of, or 
over the territory of a Member State” (Article 2(1)). However, Article 2(2) excludes some aircraft 
from its scope, including model aircraft with a maximum take-off mass of less than 20 kg. There is 
no  definition  of  ‘model  aircraft’  and  there  is  divergence  between  the  rules  at  national  level  in 
respect  of  what  is  covered  by  this  exemption.  In  the  view  of  the  Commission  services, 
commercially  used  drones  with  a  maximum  take-off  mass  of  less  than  20kg  are  still  subject  to 
insurance  requirements  because  Article  14  of  Regulation  (EU)  2019/947  requires  an  insurance 
policy number according to national or European legislation. 

(b) Interplay with the AI liability proposal 

Similarly  to  the  interplay  with  the  Motor  Insurance  Directive,  the  policy  measures  envisaged 
under the AI liability initiative do not overlap with Regulation (EC) No 785/2004. The Regulation 
does not harmonise issues of civil liability or the burden of proof. The future proposal on liability 
for AI would include targeted alleviations of the burden of proof to ensure that victims are equally 
protected  when  AI  is  involved.  Insofar  as  the  policy  options  assessed  in  this  IA  involve  a 
mandatory  insurance  regime  covering  strict  liability  regime  for  the  use  of  certain  AI-enabled 
products or the provision of certain AI-enabled services, the scope of such an insurance obligation 
would specifically not include AI-enabled products, such as some types of autonomous drones, of 
a category falling under Regulation (EC) No 786/2004. 

2.2. Regulation (EC) No 2027/97 on air carrier liability in the event of accidents 

(a) Description 

Regulation  (EC)  No  2027/97  imposes  unlimited  liability  on  EU  air  carriers,  i.e.  licensed  air 
transport  undertakings,  in  the  event  of  death  or  injury  to  passengers.  In  accordance  with  the 
Montreal Convention, that liability is strict up to 100 000 special drawing rights.277 The air carrier 
can contest and potentially avoid liability above that amount by proving that its conduct was not 
negligent  or  otherwise  amounted  to  fault.  Different  rules  apply  with  respect  to  damage  to 
passengers’ baggage and delay, which is covered by Regulation (EC) No 2027/97 in conjunction 
with the Montreal Convention, as well as cargo and damage occasioned by delay in the carriage of 
cargo, which is covered only by the Montreal Convention. 

Regulation (EC) No 889/2002 amended Regulation (EC) No 2027/97 to implement the Montreal 
Convention,  which  had  been  ratified  by  all  Member  States  and  the  EU.278  Following  the 
amendment, Regulation (EC) No 2027/97 continues to cover liability of Community air carriers in 
respect  of passengers including passenger delay  and their baggage. The scope of the Regulation 

use of the regulatory procedure with scrutiny to Articles 290 and 291 of the Treaty on the Functioning of the European 
Union, OJ L 198, 25.7.2019, 241. 
275 “Air carrier” is defined as “an air transport undertaking with a valid operating license” (Article 3 lit. a). 
276 Unless they are “air carriers”, someone “who has continual effective disposal of the use or operation of the aircraft” 
is its “operator”, with a rebuttable presumption in favour of the person in whose name the aircraft is registered (Article 
3 lit. c). 
277 SDRs are a mix of currency values established by the International Monetary Fund (IMF). 
278 For details on the Montreal Convention, see subsequent point. 

133 

 
 
 
                                                                                                                                                                                     
was extended to  cover also  domestic flights.  The provisions of the Montreal  Convention govern 
the conditions and limits of that liability irrespective of the basis of the claims, be it a contract, tort 
or otherwise. Regulation (EC) No 2027/97 does not exclude air transport undertakings providing 
air services by means of unmanned aircraft systems (UASs) from its scope. The Regulation covers 
damage caused by passenger drones to the passengers. The Montreal Convention covers damage 
caused to cargo by delivery drones.  

(b) Interplay with the AI Liability proposal 

The  policy  measures  envisaged  under  the  AI  liability  initiative  do  not  overlap  with  Regulation 
(EC) No 2027/97. The Regulation does not address liability for damage caused by UASs to third 
parties  or  the  liability  of  parties  other  than  the  air  carrier  for  damage  caused  by  UAS,  such  as 
aircraft  operators  not  licenced  as  air  transport  undertakings  or  service  providers  of  air  traffic 
management. In addition, it does not cover alleviations of the burden of proof to the benefit of the 
claimant seeking compensation for damage caused by an UAS. 

The future proposal on liability for AI could apply to autonomous AI-enabled UASs and air traffic 
management  systems.  It  would  adapt  national  liability  rules  to  ensure  that  injured  persons, 
including  in  particular  third  parties  who  do  not  have  a  contract  of  carriage,  can  claim 
compensation  despite  the  autonomy,  opacity,  complexity  and  the  lack  of  predictability  and 
explainability characterising some of the AI-systems  used in  these technologies. With respect  to 
damage  caused  to  passengers,  their  baggage,  or  cargo,  there  would  also  be  no  risk  of  a  conflict 
between,  on  the  one  hand,  possible  AI-specific  alleviations  of  the  burden  of  proof  as  envisaged 
under the preferred policy option, and on the other hand, the existing EU and international rules in 
the field of air transport. If the Commission would decide, at the stage of the targeted review, to 
adopt harmonised rules on strict liability of users/operators of certain AI-enabled technologies, it 
could be clarified, in order to be future-proof, that this possible regime is without prejudice to the 
liability conditions and limits laid down in the Montreal Convention concerning injury or death of 
passengers or damage to  their baggage. A similar clarification could  be envisaged, at  that stage, 
with  respect  to  liability  for  damage  to  cargo,  because  the  respective  provisions  of  the  Montreal 
Convention are binding for the EU and all Member States as ratifying parties. 

2.3.  Convention  for  the  Unification  of  Certain  Rules  for  International  Carriage  by  Air 
(Montreal Convention) 

(a) Description 

All Member States and the EU are parties to the Montreal Convention. Pursuant to Article 29 of 
that  Convention,  “in  the  carriage  of  passengers,  baggage  and  cargo,  any  action  for  damages, 
however founded, whether under this Convention or in contract or in tort or otherwise, can only 
be brought subject to the conditions and such limits of liability as are set out in this Convention”. 
Drones are not excluded from the scope of the Montreal Convention, but as only damage caused in 
the international carriage of passengers, baggage and cargo is covered, only drones used for those 
professional  purposes  are  concerned.  The  Convention  provides  for  air  carriers’  liability  in  the 
event of bodily injury sustained by passengers or damage to baggage or cargo during the operation 
of the aircraft or in the course of any of the operations of embarking or disembarking. Different 

134 

 
 
 
limits are defined depending on the type of damage. With respect to bodily injury, strict liability is 
capped  at  100 000  Special  Drawing  Rights  per  passenger  whereas  the  carrier  can  avoid  liability 
exceeding that cap by proving that the damage was not due to the carrier’s fault, or was due to the 
fault of a third party.  

Liability for damage to checked baggage or cargo occurring while the damaged items were in the 
charge of the carrier does not require fault, but various limitations, exclusions or defences apply in 
that respect. The carrier is notably exempted from liability for damage to cargo if it proves that the 
damage resulted from certain facts like war, inherent defects of the cargo, etc. Regarding damage 
occasioned  by  delay,  the  carrier  is  not  liable  if  it  proves  that  it  took  all  reasonable  measures  to 
avoid  the  damage.  Fault  of  the  injured  party  is  generally  admitted  as  a  defence,  for  which  the 
defendant bears the burden of proof. The Montreal Convention also contains several provisions on 
prima  facie  evidence,  namely  regarding  the  conclusion  on  an  air  carriage  contract,  cargo,  and 
receipt of cargo in good condition. 

Parties  to  the  Montreal  Convention  are  to  require  air  carriers  to  maintain  adequate  insurance 
covering their liability under this Convention. 

(b) Interplay with the AI liability proposal 

It is doubtful whether the Montreal Convention covers the liability of UAS operators for damage 
caused  to  third  parties  on  the  ground.  In  any  event,  the  Montreal  Convention  does  not  cover 
liability towards third parties, e.g. liability vis-à-vis a passer-by who is injured by an autonomous 
delivery drone during a landing manoeuvre, due to an erroneous output of the drones’ AI-enabled 
perception  system.  Neither  does  it  cover  liability  of  other  entities  than  the  air  carrier,  such  as 
aircraft  operators  not  licenced  as  air  transport  undertakings,  or  air  traffic  management  service 
providers.  In addition,  the Convention does not  regulate alleviations  of the claimant’s burden of 
proof regarding substantive liability conditions that could be obscured by the use of AI. 

If the Commission would decide, at the stage of the targeted review, to adopt harmonised rules on 
strict liability of users/operators of certain AI-enabled technologies, it could be clarified, in order 
to  be  future-proof,  that  this  possible  regime  is  without  prejudice  to  the  liability  conditions  and 
limits laid down in the Montreal Convention concerning injury or death of passengers or damage 
to their baggage. A similar clarification could be envisaged, at that stage, with respect to liability 
for damage to cargo, because the respective provisions of the Montreal Convention are binding for 
the EU and all Member States as ratifying parties. 

2.5. Implementing Regulation (EU) 2021/664 on a regulatory framework for the U-space  

(a) Description 

The  conditions  under  which  U-space  service279  providers  are  liable  for  damage  caused  in  the 
provision  of  such  services  are  not  harmonised.  Pursuant  to  Article  15(1)(j)  of  Implementing 

279 ‘U-space airspace’ means a UAS (unmanned aircraft system) geographical zone, where UAS operations are only 
allowed  to  take  place  with  the  support  of  U-space  services;  ‘U-space  service’  means  a  service  relying  on  digital 

135 

 
 
 
                                                           
 
Regulation (EU) 2021/664, U-space service providers are required to have in place arrangements 
to  cover  liabilities  related  to  the  execution  of  their  tasks  appropriate  to  the  potential  loss  and 
damage.  

(b) Interplay with the AI Liability proposal 

The policy measures envisaged under the AI liability initiative do not overlap with Implementing 
Regulation  (EU) 2021/664.  That  Implementing  Regulation  does  not  address  liability  for damage 
caused  by  UASs  to  third  parties  or  the  liability  of  parties  other  than  the  air  carrier  for  damage 
caused  by  UAS,  such  as  aircraft  operators  not  licenced  as  air  transport  undertakings  or  service 
providers  of  air  traffic  management.  In  addition,  it  does  not  cover  alleviations  of  the  burden  of 
proof to the benefit of the claimant seeking compensation for damage caused by an UAS. 

2.6.  Implementing  Regulation  (EU)  2017/373  laying  down  common  requirements  for 
providers  of  air  traffic  management/air  navigation  services  and  other  air  traffic 
management network functions 

(a) Description 

The  conditions  under  which  providers  of  air  traffic  management  services  are  liable  for  damage 
caused in the provision of such services are not harmonised. Pursuant to Implementing Regulation 
(EU)  2017/373  (Annex  III,  ATM/ANS.OR.D.020  Liability  and  insurance  cover),  air  navigation 
services providers, air traffic flow management providers and the Network Manager are to have in 
place “arrangements to cover liabilities related to the execution of their tasks in accordance with 
the  applicable  law”.  In  addition,  “air  navigation  services  and  air  traffic  flow  management 
providers  and  the  Network  Manager,  which  avail  themselves  of  services  of  another  service 
provider shall ensure that the agreements that they conclude to that effect specify the allocation of 
liability between them”. Thereby, insurance cover is required to address liability risks sustained by 
air  navigation  service  providers.  Legislation  concerning  Air  Traffic  Management  Data  Service 
Providers (ADSPs) is still under development. 

(b) Interplay with the AI Liability proposal 

The  preferred  policy  option  envisaged  under  the  AI  liability  initiative  do  not  overlap  with 
Implementing  Regulation  (EU)  2017/373.  That  Implementing  Regulation  does  not  cover 
alleviations of the burden of proof to the benefit of the claimant seeking compensation for damage 
caused by an UAS. 

2.7.  Convention  on  Damage  Caused  by  Foreign  Aircraft  to  Third  Parties  on  the  Surface 
(1952 Rome Convention) 

(a) Description 

services and automation of functions designed to support safe, secure and efficient access to U-space airspace for a 
large number of UAS 

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BE, ES, IT and LU are parties to the 1952 Rome Convention. It covers damage to third parties on 
the earth surface caused by an aircraft in flight or by any person or thing falling therefrom. The 
“operator”  is  strictly  liable.  The  Rome  Convention  does  not  contain  any  reference  to  UASs. 
However,  some  scholars  interpret  its  regulations  as  applicable  to  all  kinds  of  aircraft  or  ‘air 
vehicles’, provided they are ‘usable for transport’.280 The operator is defined as the “person who 
was making use of the aircraft”, i.e. the person “using it personally or when his servants or agents 
are using the aircraft in the course of their employment, whether or not within the scope of their 
authority”  (Article  2(2)).  The  registered  owner  of  the  aircraft  is  presumed  to  be  the  operator, 
unless s/he proves that some other person was the operator and ensures that other person becomes 
a  party  to  the  dispute.  The  1952  Rome  Convention  contains  further  detailed  provisions  on 
collisions between aircraft, defences, joint and several liability, the extent of liability, and security 
for operators’ liability. Liability regulated in the 1952 Rome Convention is exclusive. Pursuant to 
its Article 9, “neither the operator, the owner, any person liable under Article 3 or Article 4, nor 
their respective servants or agents, shall be liable for damage on the surface caused by an aircraft 
in  flight  or  any  person  or  thing  falling  therefrom  otherwise  than  as  expressly  provided  in  this 
Convention”. 

(b) Interplay with the AI Liability proposal 

It is doubtful whether the Convention covers the liability of UAS operators for damage caused to 
third  parties  on  the  ground.  The  1952  Rome  Convention,  ratified  by  only  four  Member  States, 
addresses this kind of damage, but it is uncertain whether that Convention might be interpreted as 
covering  UASs.  Furthermore,  the  1952  Rome  Convention  does  not  cover  liability  for  collisions 
between aircraft in the air nor alleviations of the claimant’s burden of proof regarding substantive 
liability conditions  that  could  be obscured by the use of AI.  Lastly, the  Convention only  covers 
international  flights,  unless  the  signatory  state  explicitly  declares  that  it  also  covers  domestic 
flights in that state. Hardly any unmanned aircraft fly internationally today. 

If the Commission would decide, at the stage of the targeted review, to adopt harmonised rules on 
strict liability of users/operators of certain AI-enabled technologies, it could be clarified, in order 
to  be  future-proof,  that  this  possible  regime  is  without  prejudice  to  the  liability  conditions  and 
limits  laid  down  in  the  1952  Rome  Convention.  However,  it  would  then  need  to  be  ascertained 
whether that Convention is to be interpreted as covering UASs. The fact  that only four Member 
States have ratified this Convention would also need to be taken into account in this context. 

3. Transport by water 

3.1. Regulation (EU) No 392/2009 on the liability of carriers of passengers by sea in the event 
of accidents 

(a) Description 

The  liability  regime  in  respect  of  passengers,  their  luggage  and  their  vehicles  and  the  rules  on 
insurance or other financial security is governed by Regulation (EU) No 392/2009, as well as the 

280 Cf. Masutti, Anna / Tomasello, Filippo. International Regulation of Non-Military Drones, 2018, p. 187. 

137 

 
 
 
                                                           
Articles of the Athens Convention set out in Annex I of that Regulation and the provisions of the 
IMO Guidelines set out in Annex II of the same Regulation. It should be noted that the Regulation 
is not exactly identical to the Athens Convention.281 

Depending  on  the  volume  and  type  of  damage  and  whether  the  damage  was  due  to  a  shipping 
incident, liability is either strict with certain admissible defences or fault-based. In cases where the 
damage was due to a shipping incident, the burden of proof regarding fault is shifted to the carrier. 
Liability  rules  relate  only  to  incidents  that  occurred  in  the  course  of  carriage,  for  which  the 
claimant bears the burden of proof. Presumptions of fault or neglect of a party or the allocation of 
the  burden  of  proof  to  a  party  do  not  prevent  evidence  in  favour  of  that  party  from  being 
considered. 

While  the  basis  of  liability,  either  in  contract  or  in  tort,  is  not  specified  in  Regulation  (EU)  No 
392/2009 or the provisions of the Athens Convention included in Annex I of that Regulation, the 
Convention is designed to be the sole legal base, within its subject matter, for passengers’ claims 
against the carrier. No action for damages for the death of or personal injury to a passenger, or for 
the loss of or damage to luggage, can be brought against a carrier or performing carrier otherwise 
than in accordance with the Athens Convention.  

Compulsory  insurance  or  other  security  applies  to  ships  licensed  to  carry  more  than  twelve 
passengers.  

(b) Interplay with the AI Liability proposal 

The Regulation does not regulate liability for damage, other than loss suffered as a result of death 
or personal injury to a passenger or loss or damage to luggage, due to an incident occurring in the 
course  of  carriage,  nor  liability  of  other  parties  than  the  carrier  such  as  navigation  service 
providers.  For  example,  the  liability  of  the  operator  of  an  autonomous  AI-enabled  vessel  for 
damage,  e.g.  physical  injuries  or  property  damage,  suffered  by  a  third  party,  e.g.  a  surfer  or 
swimmer  rammed  by  the  AI-enabled  vessel,  would  not  be  covered  by  the  existing  EU  rules. 
Instead, EU law leaves the regulation of liability to the tort law of the Member States. 

The  future  proposal  on  liability  for  AI  could  apply  to  autonomous  AI-enabled  ships.  It  would 
adapt  national  liability  rules  to  ensure  that  injured  persons,  including  in  particular  third  parties 
who  do  not  have  a  contract  of  carriage,  can  claim  compensation  despite  the  autonomy,  opacity, 
complexity and the lack of predictability and explainability characterising some of the AI-systems 
used in these technologies. 

If the Commission would decide, at the stage of the targeted review, to adopt harmonised rules on 
strict  liability  of  users/operators  of  certain  AI-enabled  technologies,  it  could  be  clarified,  at  that 
stage,  that  such  rules  are  without  prejudice  to  the  rules  on  carrier  liability  for  damage  to 
passengers of sea-going vessels and their luggage under Regulation (EU) No 392/2009. 

281 See subsequent point for further explanations on the Athens Convention. 

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3.2. Athens Convention relating to the Carriage of Passengers and their Luggage by Sea and 
the 2002 Protocol 

(a) Description 

The 1974 Convention and its 2002 protocol address the transport of passengers and their luggage 
by  sea.  It  lays  down  mandatory  rules  on  the  substantive  conditions  of  liability  (strict  v.  fault-
based),  the  burden  of  proof,  insurance  requirements  and  defences.  According  to  Article  14,  no 
action  for  damages  governed  by  the  Athens  Convention  shall  be  brought  against  a  carrier 
„otherwise than in accordance with this Convention“. Within its subject matter, the Convention is 
designed  to  be  the  sole  legal  base  for  passengers’  claims  against  the  carrier.  Any  contractual 
provision  purporting  to  relieve  a  person  liable  under  the  Convention  of  liability  towards  the 
passenger, to prescribe a lower limit of liability, or to shift the burden of proof which rests on the 
carrier are void. 

The  EU  itself  and  several  Member  States  are  parties  to  the  2002  Protocol  to  the  Athens 
Convention. Only a few Member States are still parties to the 1974 Convention itself: EE, IE, LU 
and PL. 

(b) Interplay with the AI Liability proposal 

See previous point on Regulation (EU) No 392/2009. 

3.3. Directive 2009/20/EC on the insurance of shipowners for maritime claims 

(a) Description 

This  Directive  establishes  a  legal  framework  applicable  to  the  insurance  of  shipowners  for 
maritime  claims  in  order  to  make  economic  operators  act  more  responsibly  and  to  improve  the 
quality of merchant shipping. It applies to ships of 300 gross tonnage or more (except warships or 
State owned or operated ships). Member States are to impose that ships flying their flag be insured 
by  their  owners  and  other  ships  be  insured  when  they  enter  ports  under  the  Member  States’ 
jurisdiction. 

(b) Interplay with the AI Liability proposal 

Directive 2009/20/EC does not include any rules on substantive liability conditions or the burden 
of  proof.  If  the  Commission  would  decide,  at  the  stage  of  the  targeted  review,  to  lay  down  a 
harmonised  mandatory  insurance  regime  for  certain  AI-enabled  technologies,  overlap  with 
Directive 2009/20/EC would need to be avoided. 

3.4.  Regulation  (EC)  No  391/2009  and  Directive  2009/15/EC  on  ship inspection  and  survey 
organisations 

(a) Description 

139 

 
 
 
 
Regulation (EC) No 391/2009 establish measures to be followed by organisations entrusted with 
the inspection, survey and certification of ships for compliance with the international conventions 
on  safety  at  sea  and  prevention  of  marine  pollution282.  Directive  2009/15/EC  complements  this 
safety  regime  by  establishing  measures  to  be  followed  by  Member  States  in  relation  to  those 
organisations. 

(b) Interplay with the AI Liability proposal 

Regulation (EC) No 391/2009 does not determine liability or the burden of proof.283 It has solely 
the preventive purpose of ensuring safety on sea and preventing marine pollution. While Directive 
2009/15/EC  requires  MS  to  formalise  a  ‘working  relationship’  with  ship  inspection  and  survey 
organisations, inter alia by agreeing on the MS entitlement to  financial compensation from  such 
organisations  if  liability  arising  out  a  marine  casualty  is  imposed  on  that  MS  vis-à-vis  injured 
parties,  the  Directive  does  not  determine  the  conditions  or  the  burden  of  proof  under  extra-
contractual liability rules. 

The  AI  liability  proposal  complements  the  EU  legal  framework  regarding  safety  at  sea,  by 
ensuring an effective compensation under extra-contractual liability rules when AI is involved in 
causing damage. 

3.5. Directive 2009/18/CE on the investigation of accidents in the maritime transport sector 

(a) Description 

Directive 2009/18/EC serves the purpose of establishing facts that lead to a maritime accident and 
then to safety recommendations. Its purpose is to learn from accidents, in order to prevent similar 
accidents from happening in the future 

(b) Interplay with the AI Liability proposal 

Directive  2009/18/EC  and  the  AI  liability  proposal  are  complementary.  The  former  has  an 
exclusively preventive function and clarifies that conclusions and safety recommendations derived 
from  the  investigation  of  maritime  incidents  should  not  determine  liability  or  apportion  blame. 
The latter serves to enable effective liability claims. 

3.6. 1910 Brussels Collision Convention 

(a) Description 

282 International Convention for the Safety of Life at Sea of 1 November 1974 (SOLAS 74); International Convention 
for the Prevention of Pollution from Ships of 2 November 1973 (MARPOL). 
283 While recital 7 states  that  ship inspection and  survey organisations  should be subject to global joint and several 
liability, there is no corresponding legal provision. The question of joint and several liability question is in any event 
distinct from the question of substantive liability conditions and the burden of proof. 

140 

 
 
 
 
                                                           
23 Member States284 are parties to the 1910 Brussels Collision Convention, which only lays down 
principles for the collision between sea-going vessels or between sea-going vessels and vessels of 
inland  navigation.  It  addresses  the  compensation  for  damages  caused  to  the  vessels,  or  to  any 
things or persons on board thereof (Article 1). It includes a fault-based liability regime, for which 
it  regulates  the  conditions  (referring  to  the  fault  “of  the  vessel”).  While  it  does  not  expressly 
prohibit concurring claims under different liability regimes, it might be interpreted as an exclusive 
regulation  of the extra-contractual liability issues within its  scope, because pursuant  to  Article 1 
“compensation … shall be settled in accordance with” the provisions of the Convention, and only 
specific  questions,  namely  the  determination  of  the  meaning  and  effect  of  any  contract  or 
provision of law which limits the liability of the owners of a vessel towards persons on board, are 
“left  to  the  law  of  each  country”  (cf.  Article  4(4)).  By  contrast,  issues  not  regulated  by  the 
Convention  are  left  to  national  law,  for  instance  the  question  of  how  to  attribute  fault  and 
causation to a liable entity and – apart from a prohibition of presumptions of fault (Article 6) – the 
rules governing the proof of the relevant facts. 

Pursuant to Article 2 of the 1910 Brussels Collision Convention, the damages are borne by those 
who  suffered  them  if  the  collision  is  accidental,  caused  by  force  majeure,  or  if  the  cause  of  the 
collision is left in doubt.  

The Convention does not address situations where a sea-going vessel causes damage to a person or 
to property, which is not on another vessel. 

(b) Interplay with the AI Liability proposal 

If the Commission would decide, at the stage of the targeted review, to adopt harmonised rules on 
strict  liability  of  users/operators  of  certain  AI-enabled  technologies,  liability  for  collisions 
involving  sea-going  vessels  could  be  carved  out  from  this  possible  strict  liability  regime  to 
preserve the fault-based regime laid down in the 1910 Brussels Collision Convention. It could also 
be clarified that the envisaged measures to ease the burden of proof cannot lead to liability if the 
cause  of  a  collision  falling  under  the  scope  of  the  1910  Brussels  Collision  Convention  is  left  in 
doubt, because such an outcome would conflict with Article 2 of that Convention. 

3.7.  1960  Convention  relating  to  the  unification  of  certain  rules  concerning  collisions  in 
inland navigation 

(a) Description 

AT,  DE,  FR,  NL,  HU,  PL  and  RO  are  parties  to  the  Convention  relating  to  the  unification  of 
certain rules concerning collisions in inland navigation. It applies to a collision of ships on inland 
waterways on the territory of a contracting state.  It  provides for fault-based liability  for damage 
caused  to  the  ships  involved  and  to  persons  and  objects  on  board.  It  specifically  excludes  legal 
presumptions of fault. Issues not regulated by the Convention are left to national law, for instance 
the  question  of  how  to  attribute  fault  and  causation  to  a  liable  entity  and  –  apart  from  the 

284 DE, AT, BE, FR, CY, HU, IE, MT, NL, RO, IT, DK, PT, EL, SE, PL, FI, EE, ES, LV, LU, HR and SI. 

141 

 
 
 
                                                           
prohibition of presumptions of fault (Article 2(2), second sentence) – the rules governing the proof 
of relevant facts. 

The  Convention  does  not  address  situations  where  a  sea-going  vessel  causes  damage,  nor  any 
situations where damage is caused to a person or to property, which is not on a vessel of inland 
navigation. 

(b) Interplay with the AI Liability proposal 

As  the  preferred  policy  option  on  AI  Liability  does  not  involve  any  presumptions  of  fault,  this 
option  is  compatible  with  the  1960  Convention  relating  to  the  unification  of  certain  rules 
concerning collisions in inland navigation. 

3.8.  Hague-Visby  Rules  (International  Convention  for  the  Unification  of  Certain  Rules  of 
Law  relating  to  Bills  of  Lading  (Hague  Rules,  1924)  amended  in  1968  by  the  Protocol  to 
Amend the International Convention for the Unification of Certain Rules of Law relating to 
Bills of Lading of 25 August 1924) 

(a) Description 

BE, HR, CY (only Hague rules), DK, DE, FI, FR, HU (only Hague rules), IE (only Hague rules), 
IT,  LV,  LT,  NL,  PL,  PT  (only  Hague  rules),  SI  (only  Hague  rules)  and  SE  are  parties  to  this 
Convention. Pursuant to Article II of the Hague Visby Rules, under every contract of carriage of 
goods by sea the carrier, in relation to the loading, handling, stowage, carriage, custody, care and 
discharge of such goods, is subject to the responsibilities and liabilities and entitled to the rights 
and immunities set out in these rules. Article IV sets out defences against the carrier’s or the ship’s 
liability,  e.g.  with  respect  to  damage  arising  from  neglect  of  the  ship  master,  latent  defects  not 
discoverable  by  due  diligence,  acts  of  god  or  war,  etc.,  and  any  other  cause  arising  without  the 
actual fault of the carrier, as well as limits capping the amount recoverable in the case of damage 
to  shipped  goods.  Pursuant  to  Article  IV  bis,  these  limits  and  defences  apply  to  contractual  and 
extra-contractual  claims  against  the  carrier  in  respect  of  loss  or  damage  to  goods  covered  by  a 
contract of carriage.  

(b) Interplay with the AI Liability proposal 

The Hague-Visby Rules guarantee ship-owners, which may include also managers and operators 
of ships, the right to limit their liability, including their extra-contractual liability vis-à-vis injured 
third parties. Therefore,  if the Commission  would decide, at  the stage of the targeted review, to 
adopt harmonised rules on strict liability of users/operators of certain AI-enabled technologies, it 
could be clarified, at that stage, that these rules are without prejudice to the limitations laid down 
in, or stipulated in accordance with, those Conventions. 

3.9. Budapest Convention on the Contract for the Carriage of Goods by Inland Waterway 
(CMNI) 

142 

 
 
 
 
(a) Description 

BE, BG, CZ, DE, FR, HR, LU, HU, NL, RO, SK are parties to the CMNI. PL and PT have signed 
but not ratified. The CMNI is applicable to contracts between a carrier and a shipper on the cross-
border carriage of goods. The carrier is strictly liable for loss resulting from loss or damage to the 
goods  transported  unless  he  can  show  that  the  loss  was  due  to  circumstances,  which  a  diligent 
carrier could not have prevented and the consequences of which he could not have averted (Article 
16(1)).  Claims  based  on  Member  States’  extra-contractual  tort  liability  rules  may  lay 
independently from the carrier’s liability under the contract of carriage. However, the maximum 
limits of liability as well as exonerations from liability set out in the CMNI, e.g. where the damage 
results from acts of the shipper or consignee, apply in any action in respect of loss or damage to or 
delay in delivery of the goods covered by the contract of carriage, whether the action is founded in 
contract, in tort or on some other legal ground (Article 22). Any contractual stipulation intended to 
exclude, limit or increase the liability, within the meaning of the CMNI, of the carrier, the actual 
carrier or their servants or agents, or to shift the burden of proof are void (Article 25).  

(b) Interplay with the AI Liability proposal 

If the Commission would decide, at the stage of the targeted review, to adopt harmonised rules on 
strict  liability  of  users/operators  of  certain  AI-enabled  technologies,  carrier  liability  for  loss 
resulting from loss or damage to  goods transported on inland waterways could be carved out,  at 
that  stage,  from  a  that  possible  strict  liability  regime  to  avoid  conflicts  with  the  Budapest 
Convention on the Contract for the Carriage of Goods by Inland Waterway. In that eventuality, it 
could  also  be  clarified  that  the  ratifying  Member  States  can  maintain  the  fault-based  liability 
regime prescribed by international law for collisions in inland navigation, which would be without 
prejudice  to  the  harmonisation  of  strict  liability  vis-à-vis  injured  parties  not  on  board  of  the 
colliding vessels. The AI-specific alleviations of the claimant’s burden of proof envisaged under 
the  preferred  fault-based  liability  do  not  include  presumptions  of  fault  and  are  therefore 
compatible with the Budapest Convention. 

3.10. 1957 Liability Limitation Convention 

(a) Description 

FR, NL, ES, BE, DE, PL and PT are parties to the 1957 Liability Limitation Convention. It does 
not provide for a substantive liability regime, but allows the owner of a sea-going vessel to limit 
his liability, whatever the basis of liability may be, for loss of life or personal injury of persons on 
that vessel, or damage to goods carried on that vessel. It also allows the owner to limit liability in 
respect of loss of life of, or personal injury to, any other person, whether on land or on water, loss 
of  or  damage  to  any  other  property  or  infringement  of  any  rights  caused  by  the  act,  neglect  or 
default of any person on board the ship for whose act, neglect or default the owner is responsible 
or any person not on board the ship for whose act, neglect or default the owner is responsible.  

(b) Interplay with the AI Liability proposal 

143 

 
 
 
There is no overlap between the envisaged AI-specific alleviations of the burden of proof and the 
1957  Liability  Limitation  Convention.  If  the  Commission  would  decide,  at  the  stage  of  the 
targeted  review,  to  adopt  harmonised  rules  on  strict  liability  of  users/operators  of  certain  AI-
enabled technologies, it could be clarified, at that stage, that these rules are without prejudice to 
the limitations laid down in, or stipulated in accordance with, those Conventions. 

3.11. 1976 Convention on Limitation of Liability for Maritime Claims 

(a) Description 

DK, FI, FR, SE, ES and PL are parties to the Convention. It allows the owner, charterer, manager 
and operator of a sea-going vessel to limit their liability for damage related to the operation of a 
maritime vessel, whether the basis of liability may be contractual or extra-contractual in nature. 

(b) Interplay with the AI Liability proposal 

The  same  considerations  as  with  respect  to  the  1957  Liability  Limitation  Convention  apply,  cf. 
previous point. 

3.12. Strasbourg Convention on the Limitation of Liability in Inland Navigation 

(a) Description 

DE, HU, LU and NL are parties to this Convention. It applies to inland waterways and allows the 
ship owner to limit his liability, whatever the basis of liability may be. 

(b) Interplay with the AI Liability proposal 

The  same  considerations  as  with  respect  to  the  1957  Liability  Limitation  Convention  apply,  cf. 
3.6. 

3.13. International Convention on Civil Liability for Bunker Oil Pollution Damage 

(a) Description 

Council Decision 2002/762/EC authorised the Member States, in the interest of the EU, to accede 
to  this  Convention.  This  authorisation  was  necessary  because  Articles  9  and  10  of  the  Bunkers 
Convention affect the Brussels I Regulation. All Member States are parties to this Convention. It 
regulates  the  strict  liability  of  the  ship  owner  for  damage  by  oil  pollution  caused  by  seagoing 
vessels  and  seaborne  craft  that  carry  oil  but  not  as  bulk  cargo.  It  regulates  the  ship  owner’s 
liability for oil pollution damage exclusively (Article 3(5)). 

(b) Interplay with the AI Liability proposal 

144 

 
 
 
 
 
There  is  no  overlap  or  conflict  between  the  envisaged  AI-specific  alleviations  of  the  burden  of 
proof and the International Convention on Civil Liability for Bunker Oil Pollution Damage. 

4. Rail transport 

4.1. Regulation (EU) 2021/782 on rail passengers’ rights and obligations 

(a) Description 

Pursuant  to  the  recast  Regulation  (EU)  2021/782  on  rail  passengers’  rights  and  obligations,  the 
liability of railway undertakings for passengers and their luggage is governed by the provisions of 
COTIF included in Annex I to the Regulation (see next point for further explanations on COTIF). 
Pursuant  to  Article  14  of  Regulation  (EU)  2021/782,  railway  undertakings  are  to  be  adequately 
insured  or  have  adequate  guarantees  to  cover  its  liabilities,  in  accordance  with  Article  22  of 
Directive  2012/34/EU.  The  latter  Article  requires  adequate  insurance  or  guarantees  also  for 
covering liability in respect of third parties. 

(b) Interplay with the AI Liability proposal 

Regulation (EU) 2021/782 does not cover liability for damage caused to third parties, i.e. persons 
other  than  passengers  or  things  other  than  luggage,  e.g.  a  driver  or  pedestrian  hit  by  an 
autonomous AI-enabled train. Additionally, it does not address liability of providers of AI-enabled 
rail  traffic  management  services,  e.g.  for  damage  caused  by  a  collision  between  trains  due  to 
conflicting signals given by an autonomous AI-software. 

The future proposal on liability for AI could apply to autonomous AI-enabled trains and rail traffic 
management  systems.  It  would  adapt  national  liability  rules  to  ensure  that  injured  persons, 
including  in  particular  third  parties  who  do  not  have  a  contract  of  carriage,  can  claim 
compensation  despite  the  autonomy,  opacity,  complexity  and  the  lack  of  predictability  and 
explainability characterising some of the AI-systems used in these technologies. 

In  certain  limited cases,  there could  be overlaps  or conflicts  between the  measures  envisaged as 
part of the future proposal on liability for AI and Regulation (EU) 2021/782 in conjunction with 
COTIF. For instance, AI-specific alleviations of the claimant’s burden of proof regarding liability 
for damage caused by AI-enabled autonomous trains may not be compatible with Article 46(2) of 
COTIF,  pursuant  to  which  the  carrier’s  liability  for  articles  stowed  on  the  outside  of  a  vehicle 
transported on the train requires that “it is proved that the loss or damage results from an act or 
omission, which the carrier has committed either with intent  to  cause such a loss or damage or 
recklessly and with knowledge that such loss or damage would probably result”. Moreover, if the 
Commission would decide, at the stage of the targeted review, to adopt harmonised rules on strict 
liability  of  users/operators  of  certain  AI-enabled  technologies,  extending  that  possible  strict 
liability regime to passengers’ luggage in cases where the passenger has not also suffered death or 
personal injury could conflict with COTIF, because in such cases, COTIF provides for fault-based 
liability and excludes claims subject to other conditions. Moreover, in case a possible future strict 
liability regime for AI would include a closed list of defences available to the operator of the AI-
enabled  system  that  caused  the  damage,  the  specific  defences  guaranteed  by  Regulation  (EU) 

145 

 
 
 
2021/782 in conjunction with COTIF would nevertheless have to be preserved. In order to ensure 
legal  certainty,  the  future  proposal  on  liability  for  AI  could  clarify,  at  the  stage  of  the  targeted 
review,  that  the  provisions  of  Regulation  (EU)  2021/782  in  conjunction  with  COTIF  take 
precedence in case of a conflict.  

4.2. Convention concerning International Carriage by Rail (COTIF) 

(a) Description 

The  EU  acceded  to  COTIF  through  an  agreement  concluded  with  the  Intergovernmental 
Organization  for  International  Carriage  by  Rail  (OTIF)  on  23  June  2011.  All  Member  States 
except  Cyprus  and  Malta  are  signatories.  COTIF  sets  out  uniform  rules  concerning  contracts  of 
international  carriage  of  passengers  (CIV,  Appendix  A  to  COTIF)  or  international  carriage  of 
goods (CIM, Appendix B to COTIF) by rail. It provides for the carrier’s strict liability for death or 
personal injury of passengers. Carriers are exempted from strict liability if the damage was caused 
by force majeure, by the passenger’s fault or by a third party under certain circumstances. Carriers 
are  also  liable  for  damage  caused  to  passengers’  luggage.  Strict  liability  for  damage  to  luggage 
only applies if there is also a damage to the passenger, death or personal injury as well as in the 
case of registered luggage, under certain conditions. In the other cases, fault-based rules set out in 
the Convention apply. COTIF also contains provisions specifying what the parties to the various 
liability claims have to prove. 

With respect to contracts of international carriage of goods, CIM provides for the carrier’s liability 
for loss or damage resulting from the loss of, or damage to, the transported goods. The carrier is 
relieved of this liability to the extent that the loss or damage was caused by the fault of the person 
entitled, by an inherent defect of the goods, force majeure, or other specified risks, e.g. inadequate 
packaging, the nature of the goods, etc. Article 25 CIM regulates the burden of proof, and Articles 
30 et seq. provide for limitations of liability. 

In  all  cases  where  these  uniform  rules  apply,  any  action  in  respect  of  liability,  on  whatever 
grounds,  may  be  brought  against  the  carrier  only  subject  to  the  conditions  and  limitations  laid 
down in these uniform rules (Article 52 of Regulation (EU) 2021/782). 

(b) Interplay with the AI Liability proposal 

For  explanations  regarding  the  relationship  between  COTIF  and  the  measures  envisaged  in  the 
framework of the AI Liability proposal, see the previous point on Regulation (EU) 2021/782. 

Finally,  it  should  be  mentioned,  for  the  sake  of  completeness,  that  the  EU  declaration  on  the 
exercise of competence between the EU and its Member States is annexed to the Council decision 
to  sign  and  conclude  the  COTIF  accession  agreement.  The  EU  also  provided  a  list  with  EU 
secondary law in  the field covered by the COTIF and pointed out  that the Union competence is 
subject to continuous development. The COTIF accession agreement also contains a disconnection 
clause  in  Art.  2  according  to  which  “without  prejudice  to  the  object  and  the  purpose  of  [the 
COTIF] to promote, improve and facilitate international traffic by rail and without prejudice to its 
full application with respect to other Parties to [the COTIF], in their mutual relations, Parties to 

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[the COTIF] which are Member States of the Union shall apply Union rules and shall therefore 
not apply the rules arising from [the COTIF] except in so far as there is no Union rule governing 
the particular subject concerned”. 

Both  the  declaration  of  competences  and  the  disconnection  clause  refer  to  EU  rules  that  have 
already been  adopted. However, the Union is  not  bound by that declaration and can  exercise its 
competence and adopt  new rules with  regard to  matters covered by the  COTIF.285 Nevertheless, 
deviations from CIV seem to require a textual or substantive amendment to the recently adopted 
Regulation  (EU)  2021/782,  because  pursuant  to  Article  13  of  that  Regulation,  the  liability  of 
railway undertakings in respect of passengers and their luggage is governed by the CIV provisions 
reproduced in Annex I of the same Regulation. 

4.3. Directive (EU) 2016/798 on railway safety 

(a) Description 

Directive  (EU)  2016/798  serves  the  purpose  of  establishing  facts  that  lead  to  a  railway  accident 
and  then  to  safety  recommendations.  Its  purpose  is  to  learn  from  accidents,  in  order  to  prevent 
similar accidents from happening in the future. 

(b) Interplay with the AI Liability proposal 

Directive  (EU)  2016/798  and  the  AI  liability  proposal  are  complementary.  The  former  has  an 
exclusively preventive function and clarifies that conclusions and safety recommendations derived 
from the investigation of railway incidents should not determine liability or apportion blame. The 
latter serves to enable effective liability claims. 

285  Art.7  of  the  COTIF  accession  agreement  provides  that  “The  scope  of  the  competence  of  the  Union  shall  be 
indicated in general terms in a written declaration made by the Union at the time of the conclusion of this Agreement. 
That declaration may be modified as appropriate by notification from the Union to OTIF. It shall not replace or in any 
way  limit  the  matters  that  may  be  covered  by  the  notifications  of  Union  competence  to  be  made  prior  to  OTIF 
decision-making by means of formal voting or otherwise”. 

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Annex 7 

Interplay with the AI Act  

The  characteristics  of  AI  were  identified  in  the  AI  Act  Impact  Assessment  as  driving  specific 
problems:  safety  and  fundamental  rights  risks,  difficulties  of  enforcement,  legal  uncertainty  and 
fragmentation that can lead to mistrust in AI technologies. 

The AI Act addresses these problems by proposing measures, in line with the EU product safety 
legislation framework, to: 

- 

- 

- 

- 

set  requirements  specific  to  certain  AI  systems  and  obligations  on  all  value  chain 
participants  in  order  to  ensure  that  a  limited  number  of  AI  systems,  that  contradict  EU 
values are prohibited, that high-risk AI systems placed or used on the EU market  are safe 
and  respect  the  existing  law  on  fundamental  rights  and  Union  values  and  that  certain  AI 
systems that directly interact with citizens are subject to transparency obligations;  
ensure legal certainty to facilitate investment and innovation in AI by making it clear what 
essential requirements, obligations, as well as conformity and compliance procedures must 
be followed to place, or use a high-risk AI system in the Union market;  
enhance  governance  and effective  enforcement of the  existing law on  fundamental  rights 
and safety requirements applicable to AI systems by establishing  clear rules for relevant 
authorities on conformity assessment and ex post monitoring procedures and the division 
of governance and supervision tasks between national and EU levels;  
facilitate  the  development  of  a  single  market  for  lawful,  safe  and  trustworthy  AI 
applications and prevent market fragmentation. 

The AI Act has as general objective the creation of the conditions for the development and use of 
trustworthy  artificial  intelligence  in  the  Union,  which  is  shared  by  this  initiative.  Given  this 
complementarity, the IA accompanying the proposed AI Act concluded that “only a combination 
of the AI horizontal framework with future liability rules can fully address the problems listed in 
this  impact  assessment  specifically  in  terms  of  […]  legal  certainty  and  single  market  for 
trustworthy AI”. It stressed that “effective liability rules will also provide an additional incentive to 
comply  with  the  due  diligence  obligations  laid  down  in  the  AI  horizontal  initiative,  thus 
reinforcing the effectiveness and intended benefits of the proposed initiative.”286  

Therefore,  the  AI  Act  is  a  relevant  component  of  the  baseline  scenario,  because  it  plays  an 
essential role in creating an ecosystem of trust for AI. The relationship between this proposal and 
civil liability is described along six main aspects.  

1.  Reducing risks and liability-related costs 

Clear and specific safety rules and standards for AI-systems and AI-enabled products are likely to 
reduce the risk of accidents and thus liability-related costs. They may lead to a reduction of safety 
risks, facilitate insurance coverage of liabilities and, in the long run, reduce insurance costs. This 
will  be  particularly  relevant  for  professional  entities  covered  by  the  AI  Act,  such  as  producers 
integrating AI in products, as well as all other ‘providers’ and ‘users’.  

286  Commission  SWD(2021) 84 final,  Impact  assessment  accompanying  the  proposal  for  a  Regulation  of  the 
European  Parliament  and  of  the  Council  laying  down  harmonised  rules  on  artificial  intelligence  (Artificial 
Intelligence Act), p. 88 (https://eur-lex.europa.eu/legal-content/SV/TXT/?uri=CELEX:52021SC0084).  

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In  this  respect,  reference  should  for  instance  be  made  to  the  AI  Act’s  requirements  on  risk 
management  (Article  9),  data  governance  (Article  10),  human  oversight  (Article  14),  accuracy, 
robustness  and  cybersecurity  (Article  15),  quality  management  system  (Article  17),  conformity 
assessment (Article 19) and corrective actions (Article 21).  

However, in spite of these enhanced safety rules for AI and the sought-after reduction of accidents, 
AI-related  liability  cases  –  and  thus  the  relevance  of  the  associated  problems  –  may  still  occur, 
during the baseline period. As the market for AI-enabled products and services is expected to grow 
substantially, and AI  technologies  can be implemented in  more and more situations and sectors, 
the  economic  and  societal  relevance  of  the  identified  problems  will  be  important.  The  Deloitte 
study concluded that the sum of the AI market share affected by liability issues will increase at a 
compound annual growth rate of 44% – 56% until 2025.287 The safety requirements applicable to 
AI systems  will likely increase safety  and reduce risks but  they  will not  remove liability-related 
issues  given  the  expected  exponential  increase  in  absolute  terms  of  the  AI  market  potentially 
affected  

While adding to the overall cost of doing business for manufacturers, liability rules may provide 
some  additional  incentive  to  comply  with  the  due  diligence  obligations  laid  down  in  the  AI  Act 
and prevent damages from happening,. 

2.  Setting standards of care relevant for establishing fault 

As  explained  in  section  1.2.  above,  all  jurisdictions  in  the  EU  require  some  misconduct  as  a 
prerequisite  of  fault  liability.  The  benchmark  for  determining  what  counts  as  ‘misconduct’  is 
usually determined by  the legal  system as  a whole, based on some objective standards (more or 
less a comparison with what a ‘reasonable person’ would have done). This assessment is done by 
courts after the damage happened but it becomes easier and more predictable if the legal system 
provides  for  specific  rules  of  conduct  that  were  breached.  These  duties  of  care  are  usually 
themselves at least partially introduced to prevent harm (for example traffic regulations). 288 

In this context, the requirements and obligations introduced by the AI Act for providers and users 
of  high-risk  AI  systems  are  meant  to  minimize  risks  to  safety  and  fundamental  rights.  Non-
compliance with those requirements and obligations could be considered by courts to determine if 
the liable person had demonstrated a faulty behaviour. An example is the obligations of users in 
Article 29 of AI Act: to use the systems in accordance with the instructions of use, to ensure that 
input data (when it is under the control of the user) is relevant in view of the intended purpose and 
to monitor the operation of the AI system. Another example is the obligation of providers of AI 
systems  in  Article  21:  to  take  the  necessary  corrective  actions  when  the  AI  system  is  not  in 
conformity with requirements set by the AI Act. 

For  non-high  risk  AI  systems,  the  AI  Act  supports  the  development  of  the  voluntary  codes  of 
conduct  that in  principle, if those codes of conduct  would exist in the specific sector or specific 
applications, the courts might look into them in order to determine the duties of care. 

The  present  initiative  will  incentivise  compliance  with  the  duties  of  care  set  by  the  AI  Act  for 
high-risk AI systems. For this purpose, it proposes to introduce a presumption of causality with the 
damage for non-compliance with requirements and obligations designed to prevent damage: if the 
victim can prove that the liable person did  not  comply  with  a requirement  set  by AI Act,  which 

287 Deloitte, op. cit., p. 121. 
288  See  Karner/Koch/Geistfeld,  Comparative  Law  Study  on  Civil  Liability  for  Artificial  Intelligence,  November 
2020, p. 39 et seq. (https://op.europa.eu/publication/manifestation_identifier/PUB_DS0921157ENC). 

149 

 
 
 
                                                           
 
was  meant  to  prevent  the  damage  that  occurred,  then  the  court  can  presume  that  the  non-
compliance led to the damage. 

However,  in  light  of  the  characteristics  of  AI  and  the  problem  drivers  explained  above,  this 
presumption  could  be  effectively  used  only  if  the  victim  can  prove  non-compliance  with  the  AI 
Act. This concerns cases where this non-compliance can be proven from elements external to the 
AI system itself (for example because the system was used in circumstances not allowed under the 
instructions of use). Moreover, for non-high risk AI systems, there are no particular requirements 
set  by  the  AI  Act  that  could  be  interpreted  as  duties  of  care.  Therefore,  additional  measures  are 
needed to ensure the effectiveness of liability claims. 

3. 

Information potentially relevant in civil proceedings 

The AI Act provides for information to be included in the technical documentation of high-risk AI 
systems (Article 11 in conjunction with Annex IV of the AI Act), automatic logging capabilities 
and traceability (Article 12), as well as transparency and information obligations vis-à-vis users of 
high-risk AI systems (Article 13)289.  

While  these  measures  are  conceived  and  useful  to  increase  transparency  for  the  sake  of  harm 
prevention or ex-post checks and improvements, they are not designed to ensure that despite the 
involvement of AI it is made easier for injured persons to substantiate the conditions of existing 
liability claims. In particular, the AI Act does not give injured persons a direct right to access the 
documented  or  logged  information.  They  can  however  request  access  to  documents  held  by 
national authorities or bodies under the conditions defined by the relevant existing rules. 

Having  access  to  such  information,  in  the  framework  of  court  proceedings,  would  help  these 
persons to  establish, for instance, that the defendant did not  comply with their obligations under 
the AI Act (e.g. as regards the quality of training data, testing, or human oversight), which would 
trigger  the  envisaged  presumption  of  causality  included  in  policy  option  1.  This  means  that  the 
court  could  presume  that  the  non-compliance  with  the  obligation  caused  the  damage  and  the 
burden to rebut this presumption would be on the professional operator.  

In discrimination cases, access to parameters and weights applied by an AI-system may also allow 
the  injured  person  to  show  –  with  the  help  of  an  expert  –  that  the  applied  criteria  are  likely  to 
correlate  with  criteria  protected  by  the  fundamental  right  to  non-discrimination/equal  treatment. 
This could help to meet the prima facie threshold for establishing indirect discrimination under the 
existing Union or national legislation in that field. Likewise, access to input data, output data and 
the internal states of an AI-system could enable injured persons to establish – with the help of an 
expert – which input is most likely to have triggered the relevant (harmful) output.  

Access to logged information on the functioning of AI-systems could thus provide a basis, in the 
court  proceedings,  for  proving  the  causal  link  between  a  negligent  human  behaviour  and  the 
relevant damage. In accordance with relevant procedural law, the competent national court could 
order that such disclosure would be subject  to  stringent  safeguards to  ensure proportionality and 
protect  the  legitimate  interests  of  all  parties  concerned,  for  instance  confidential  information, 
intellectual property rights and trade secrets. 

4.  Explaining how or why an AI system arrived at a certain harmful output  

289 The AI Act defines the ‘user’ as ‘any natural or legal person, public authority, agency or other body using an AI 
system under its authority, except where the AI system I used in the course of a personal non-professional activity.’- 
Article 3 (4). 

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The requirements of the AI Act are designed to enable the effective monitoring and supervision of 
ex  ante  safety  requirements.  While  these  requirements  have  the  potential  to  also  contribute  to 
helping victims establish that a specific human wrongful behaviour – or a defect – caused an AI 
output that caused the damage, they are not conceived for this purpose. They are designed to allow 
supervisory authorities to understand and monitor AI systems to ensure that only safe products are 
allowed on the market. Their aim is not for instance to allow victims to prove for the purposes of 
their  liability  claims  that  a  specific  input  led  to  a  specific  output,  which  caused  the  harm  to  the 
victim. Moreover, they are for the most part limited in scope to high-risk AI systems. Therefore, 
despite  the  envisaged  provisions  on  the  disclosure  of  information  to  be  recorded  or  logged 
pursuant to the AI Act, the specific AI characteristics may still prevent victims from substantiating 
their liability claim. In particular, the opacity, complexity and autonomy of certain AI-systems can 
make  it  very  challenging  for  victims,  in  court  proceedings,  to  establish  the  internal  decision-
making  processes  of  that  system,  which  may  be  necessary  in  order  to  prove  the  causal  link 
between a human behaviour and the harmful output of an AI-system.  

The targeted alleviation of the burden of proof would mean ultimately in practice that it is for the 
defendant to prove (to the level depending on national law) either that their own behaviour has not 
been  at  fault  (for  example  that  they  followed  the  instructions  of  use)  or  that  another  cause  than 
their behaviour has caused the damage. The defendant is in a better position than the victim to use 
the  technical  documentations  at  their  disposal  or  which  they  can  procure  based  on  contractual 
arrangements with the seller or provider to explain how the AI reached the harmful output. 

5.  AI used in special risk circumstances 

Another avenue to address the difficulty of proving how the use of certain AI led to the damage is 
by using strict liability. This would imply that the law determines the liable person for the harm 
caused by the AI-equipped product or service, irrespective of a fault of that person. The law would 
usually attribute that liability because the liable person has decided to expose the public to a risk 
and derives a benefit from it (for example by using that product or by providing that service). In 
this case, the victim would no longer have to prove the misconduct of  the liable person but only 
the fact that the damage arose in connection to the risk posed by the ‘behaviour’ of the AI.  

Taking  into  account  the  diverse  existing  national  strict  liability  regimes  and  the  need  of  a 
proportionate approach, a harmonised strict liability for AI system would need to consider both the 
AI systems’ characteristics but also the risk of their use in practice, in particular to high-ranking 
legal interests (life, health or property) of potential victims. 

Therefore, such an analysis would  require information  about  the market  developments  regarding 
the  rollout  of  products  and  services  driven  by  AI-systems  with  the  characteristics  that  challenge 
existing  liability  rules:  a  high  degree  of  behavioural  autonomy  (low  level  of  human  oversight), 
opacity (complexity + lack of transparency), continuous adaptation and a lack of predictability. In 
addition,  the  assessment  would  need  to  take  into  account  the  risk-profile  and  operating 
environment  of  the  products  and  services,  in  particular  whether  they  can  cause  harm  to  high-
ranking  legal  interests of the public at  large and, to  the extent such information  is  available, the 
incidence rate of accidents caused by such AI systems. 

An  important  aspect  influencing  the  rollout  of  such  product  and  services  likely  to  meet  the  risk 
profile for strict liability will be the regulatory measures meant to prevent harm: the AI Act, the 
General  Product  Safety  Regulation,  the  MPR,  future  measures  under  the  ‘old  approach’  safety 
legislation. 

6.  Different legal tools to address risks 

The AI Act, in line with the  product  safety legislation approach, in general, employs tools that 
address the risks posed by high-risk AI by imposing requirements on ‘potential wrongdoer’, either 

151 

 
 
with  the  aim  of  prevention  (authorisations,  registrations,  certifications)  or  compliance  control 
(monitoring, reporting, administrative sanctions, etc.). 

While  this  approach  is  appropriate  for  the  objectives  pursued  by  the  AI  Act,  these  tools  do  not 
provide  individual  avenues  for  compensation  to  those  that  nevertheless  suffered  harmful 
consequences  of  AI  systems.  The  AI  Act  measures  apply  to  the  entity  subject  to  requirements 
under the AI Act or to the competent authorities but they will not compensate the victim of harm. 
This  is  why  the  Commission  has  committed  to  look  also  into  the  issue  of  liability  for  AI  in  the 
present Impact Assessment and in the Impact Assessment for the PLD.    

Liability  tools  analysed  under  the  present  Impact  Assessment  have,  as  primary  purpose,  the 
compensation of victims that have suffered harm, with the involvement of AI. Secondly, they aim 
to  incentivise  a  prudent  behaviour  and  thus  compliance  with  regulatory  requirements  (including 
those set by the AI Act). By clarifying the rules applicable to such cases and ensuring efficiency of 
compensation claims, the present initiative aims to support societal trust and the uptake of the AI, 
which are the same objectives as those pursued by the AI Act. 

7.  Conclusion 

In  line  with  the  Commission’s  staged  approach  and  the  complementary  nature  of  safety  and 
liability  rules,  the  provisions  of  the  AI  Act  are  designed  to  reduce  and  mitigate  safety  and 
fundamental  rights  risks  while  the  present  initiative  is  complementing  it  by  facilitating 
compensation for justified liability claims in case AI systems produce harm to victims.  

The  AI  Act’s  envisaged  safety,  transparency  and  human  oversight  requirements  are  meant  to 
minimise safety and fundamental rights risks and support the roll-out of advanced, i.e. increasingly 
autonomous, complex, flexible and learning-based, AI-systems. Systems with such characteristics 
are likely to become more widespread. This also means that the negative effect of liability gaps on 
societal trust and consumer uptake will become increasingly relevant under the baseline scenario. 
This situation is illustrated by the case-studies developed together with the Joint Research Centre 
(Annex 13), where the difficulty of identifying and proving a faulty behaviour of a person as the 
cause of an accident is explained concretely.  

As questions  of liability  are not  covered by the other already adopted proposals  relevant  for AI, 
legal  uncertainty  regarding  the  interpretation  and  applicability  of  existing  liability  rules,  and  the 
likelihood  that  fragmented  AI-specific  liability  rules  will  be  adopted  by  some  MS,  will  persist 
under the baseline scenario.  

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Annex 8 

AI-specific fundamental rights concerns –  
Overall Commission policy approach and the role of the AI liability 
proposal  

1. Fundamental rights challenges linked to AI 

The  use  of  AI  with  its  specific  characteristics  (e.g.  opacity,  complexity,  dependency  on  data, 
autonomous behaviour) can adversely affect a number of fundamental rights enshrined in the EU 
Charter of Fundamental Rights (‘the Charter’). In the context of the AI Act, the Commission for 
instance  identified  risks  regarding  the  right  to  human  dignity  (Article  1),  respect  for  private  life 
and  protection  of  personal  data  (Articles  7  and  8),  non-discrimination  (Article  21)  and  equality 
between  women  and  men  (Article  23).  As  the  specific  characteristics  of  AI  challenge  existing 
liability rules – which often require the victim to prove fault in a human action or omission and a 
causal link between a human’s wrongful behaviour and the damage – they present a risk regarding 
the right to an effective remedy and a fair trial (Article 47). 

2. Role of the AI liability proposal with respect to fundamental rights risks 

One of the most important functions of civil liability rules is to ensure that victims of harm can 
claim compensation. By guaranteeing effective compensation, these rules also give an incentive to 
potentially liable persons to prevent harm, in order to avoid liability. 

Existing  civil  liability  rules  provide  for  the  compensation  of  damage  caused  by  harm  to  legal 
interests  corresponding  to  some  of  the  most  basic  fundamental  rights,  such  as  the  right  to  life 
(Article 2 of the Charter), the right to the physical and mental integrity (Article 3), and the right to 
property  (Article  17).  In  addition,  depending  on  each  Member  State’s  civil  law  system  and 
traditions, victims can claim compensation for harm to other legal interests, such as violations of 
personal  dignity  (Articles  1  and  4  of  the  Charter),  the  right  to  liberty  and  security  of  person 
(Article 6), respect of private and family life (Article 7), the right to equality (Article 20) and non-
discrimination (Article 21).290 

Where existing liability rules allow victims to claim compensation for damage, this same right is 
in  principle  available  also  where  AI  is  involved  in  causing  harm.  However,  the  specific 
characteristics  of  AI  can  make  it  prohibitively  difficult  or  even  impossible  to  use  these  existing 
rights successfully. With the AI liability proposal, the Commission aims to ensure that victims of 
harm caused by AI have an equivalent level of protection under civil liability rules as victims of 

290  Member  States’  rules  on  right  to  compensation  for  damage  caused  by  discrimination  or  unequal  treatment  are 
closely linked with the existing EU acquis on these rights, see 3.4. below for details. 
Furthermore, under Article 82 GDPR, any person who has suffered material or non-material damage as a result of an 
infringement of the General Data Protection Regulation has the right to receive compensation from the controller or 
processor  for  the  damage  suffered.  This  right  is  linked  to  the  fundamental  right  to  the  protection  of  personal  data 
(Article 8 of the Charter). 

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damage caused without the involvement of AI. This means that the proposal will enable effective 
private enforcement – consistent with the existing system of civil liability and compensable harm 
–  of  fundamental  rights  and  preserve  the  right  to  an  effective  remedy  where  the  AI-specific 
fundamental rights risks have materialised.  

2.1. Disclosure of information  

The envisaged rules on the disclosure of information to be documented/logged under the AI Act 
will  help  victims  of  damage  to  meet  the  burden  of  proof.  The  logging  and  documentation 
obligations of the AI Act apply, in the context of pending civil proceedings, with respect to ‘high 
risk’ AI systems.  This  includes uses  of AI posing a high risk of fundamental rights  breaches, in 
particular  regarding  the  rights  to  equality  and  non-discrimination  (e.g.  AI  systems  used  for  the 
purposes of recruitment and staff matters, access to education, access to essential services through 
credit  scoring,  etc.).291  The  proposal  on  civil  liability  for  AI  would  enable  victims  of 
discrimination  to  leverage  that  information  to  substantiate  their  compensation  claims.  If  the 
defendant refuses without just motivation to disclose information, the facts that might have been 
proven  (e.g.  the  application  of  criteria  correlating  with  criteria  protected  by  the  right  to  non-
discrimination,  or  unequal  treatment)  would  be  presumed.  This  rebuttable  presumption  will  for 
instance help victims of discrimination to meet the prima facie threshold under the existing non-
discrimination/equal treatment Directives (see 3.4. below). In accordance with relevant procedural 
law, the competent national court could order that the disclosure of information would be subject 
to stringent safeguards to ensure proportionality and protect the legitimate interests of all parties 
concerned, for instance confidential information, intellectual property rights and trade secrets.   

2.2. Presumption of causality:  

If the victim establishes (where necessary using information to be disclosed in accordance with the 
first measure described above) that the defendant did not comply with their obligations under the 
AI Act, it would be presumed that harm of a type the relevant obligations were intended to prevent 
(e.g.  discrimination)  was  caused  by  that  non-compliance.  For  example,  where  the  victim  shows 
that the defendant did not comply with their obligation to minimize the risk of discrimination by 
ensuring high quality training, validation and testing data, it would be presumed that this failure to 
comply  with  the  AI  Act  entailed  the  discrimination  for  which  compensation  is  sought.  This 
rebuttable  presumption  could  thus  help  victims  to  overcome  the  AI-specific  difficulty  of 
establishing  a  link  between  specific  input  parameters  and  a  harmful  output,  linked  to  the 
probabilistic nature and complexity of certain AI systems. 

2.3. Targeted alleviation of the burden of proof 

A targeted alleviation of the burden of proof would ensure that the victim would not have to prove 
how  or  why  an  AI-system  arrived  at  the  relevant  (harmful)  output,  as  this  could  be  excessively 
difficult  due  to  the  specific  characteristics  of  AI.  This  measure  would  apply  irrespective  of 
whether the relevant AI-system is qualified as ‘high-risk’ by the AI Act. It could thus help victims 
of discrimination also outside of the areas identified as high risk in Annex III of the AI Act. 

291 See 3.1. below for further details on the AI Act. 

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2.4.  Clarifications  regarding  the  purpose  and  scope  of  the  proposal  on  AI  liability  – 
complementarity with other Commission policy areas 

The scope of the AI liability proposal is limited to matters of civil liability. Civil liability rules are 
by  nature  complaint-based  and  intervene  only  once  damage  has  materialised.  Accordingly, 
preventive regulatory and supervisory requirements aimed directly at avoiding fundamental rights 
breaches (such as discrimination), are outside the scope of this initiative. However, as explained in 
the subsequent  sections, preventive and public enforcement  tools are delivered by other existing 
and  future  policy  instruments,  namely  the  AI  Act,  the  General  Data  Protection  Regulation,  the 
Digital Services Act and the non-discrimination/equal treatment acquis. The AI liability proposal 
complements  these  other  strands  of  the  Commission’s  AI  policy  with  a  view  to  ensuring  an 
effective protection of victims’ right to compensation under private law, including in the case of 
fundamental rights breaches. 

3. Complementarity and synergies with other strands of the Commission policy addressing 
fundamental rights concerns linked to AI 

3.1. AI Act 

The  complementarity  and  synergies  between  the  AI  liability  initiative  and  the  proposed  AI  Act 
have  already  been  described  in  the  dedicated  Annex  7.  The  present  section  looks  specifically  at 
how these instruments work together to address in particular the fundamental rights risks of AI. 

The AI Act provides a regulatory framework enhancing governance and effective enforcement of 
existing law on fundamental rights.  It seeks to ensure a high level of protection for fundamental 
rights and aims to address various sources of risks through a clearly defined risk-based approach.  

The AI Act notably establishes a list of prohibited AI practices comprising AI systems the use of 
which  is  considered  unacceptable  as  contravening  Union  values,  for  instance  by  violating 
fundamental  rights.  The  prohibitions  namely  cover  practices  that  have  a  significant  potential  to 
manipulate persons through subliminal techniques or exploit vulnerabilities of specific vulnerable 
groups such as children or persons with disabilities in order to materially distort their behaviour in 
a  manner  that  is  likely  to  cause  them  or  another  person  psychological  or  physical  harm.292  The 
proposal  also  prohibits  AI-based  social  scoring  for  general  purposes  done  by  public  authorities. 
Finally, the use of ‘real time’ remote biometric identification systems in publicly accessible spaces 
for the purpose of law enforcement is also prohibited unless certain limited exceptions apply. 

With a set of requirements for trustworthy AI and obligations on all value chain participants, the 
AI Act promotes the protection of the rights protected by the Charter. The obligations for ex ante 
testing, risk management and human oversight will facilitate the respect of fundamental rights by 
minimising the risk of erroneous or biased AI-assisted decisions in critical areas such as education 

292  Other  manipulative  or  exploitative  practices  affecting  adults  that  might  be  facilitated  by  AI  systems  could  be 
covered by the existing data protection, consumer protection and digital service legislation that guarantee that natural 
persons are properly informed and have free choice not to be subject to profiling or other practices that might affect 
their behaviour. 

155 

 
 
 
                                                           
and  training,  employment,  important  services,  law  enforcement  and  the  judiciary.  These 
mechanisms  apply  in  particular  to  stand-alone  AI  systems  with  fundamental  rights  implications 
listed in Annex III of the AI Act. This covers for instance AI systems intended to be used in areas 
such as access to education, recruitment, access to essential services (e.g. through credit scoring), 
law  enforcement,  migration,  asylum,  border  control,  administration  of  justice  and  democratic 
processes.293  For  these  AI  systems,  the  AI  Act  establishes  a  new  compliance  and  enforcement 
system  involving  a  comprehensive  ex-ante  conformity  assessment  through  internal  checks, 
combined with a strong ex-post enforcement.  

As regards the risk of  AI-induced discrimination in  particular, the  AI Act  complements existing 
Union law on non-discrimination (see 3.4. below) with specific requirements that aim to minimise 
the risk of algorithmic discrimination, in particular in relation to: 

- 
-  obligations  for 

the design and the quality of data sets used for the development of AI systems; 

testing,  accuracy,  robustness,  risk  management, 

transparency, 

documentation and human oversight throughout the AI systems’ lifecycle. 
In  case  infringements  of  fundamental  rights  still  happen,  the  increased  transparency  and 
traceability of AI systems afforded by the AI Act, coupled with ex post controls, will improve the 
conditions for seeking redress. The AI Act namely enables competent authorities, users and other 
interested  people  to  exercise  enhanced  oversight  over  those  AI  systems  posing  high  risks  to 
fundamental  rights.  Moreover,  AI  providers  will  be  obliged  to  inform  national  competent 
authorities about serious incidents or malfunctioning that constitute a breach of fundamental rights 
obligations  as  soon  as  they  become  aware  of  them.  National  competent  authorities  will  then 
investigate  the  incidents  or  malfunctioning,  collect  all  the  necessary  information  and  regularly 
transmit it to the Commission with adequate metadata. 

However, the AI Act does not include specific provisions on individuals’ claims for compensation 
for  AI-induced  fundamental  rights  breaches.  These  private  enforcement  needs  will  be  addressed 
by the complementary proposal on AI liability. 

293 Annex III namely lists the following: 
-AI systems intended for the remote biometric identification of natural persons; 
- AI systems intended to be used as safety components in the management and operation of road traffic and the supply 
of water, gas, heating and electricity; 
-  AI  systems  used  in  education  or  vocational  training,  notably  for  determining  access  or  assigning  persons  to 
educational and vocational training institutions or to evaluate persons on tests as part of or as a precondition for their 
education; 
- AI systems used in employment,  workers management and access to self-employment, notably for the recruitment 
and selection of persons,  for making decisions on  promotion and termination and  for task allocation,  monitoring or 
evaluation of persons in work-related contractual relationships; 
-  AI  systems  used  in  the  area  of  access  to  and  enjoyment  of  essential  private  and  public  services  and  benefits 
necessary for people to fully participate in society or to improve one’s standard of living (e.g. AI systems intended to 
be used to evaluate the creditworthiness of natural persons); 
- AI systems intended for certain uses of by law enforcement authorities; 
-  AI  systems  intended  for  certain  uses  by  public  authorities  in  the  area  of  migration,  asylum  and  border  control 
management; 
- AI systems intended to assist a judicial authority in researching and interpreting facts and the law and in applying the 
law to a concrete set of facts. 

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3.2. Digital Services Act 

Internet users are exposed to ever-increasing risks and harms online  – from the spread of illegal 
content  and  activities,  to  limitations to  express  themselves  and  other  societal  harms.  AI-enabled 
systems  play  a  pivotal  role  for  online  services,  e.g.  for  user  profiling  and  content 
recommendations. Specific groups or persons may be vulnerable or disadvantaged in their use of 
online services because of their gender, race or ethnic origin, religion or belief, disability,  age or 
sexual  orientation.  They  can  for  instance  be  disproportionately  affected  by  (unconscious  or 
conscious)  biases  embedded  in  notification  systems,  as  well  as  replicated  in  automated  content 
moderation tools used by platforms.  

The  proposed  Digital  Services  Act294  addresses  these  risks  through  a  modern,  future-proof 
governance  framework  effectively  safeguarding  the  rights  and  legitimate  interests  of  all  parties 
involved,  most  of  all  Union  citizens.  The  proposal  will  mitigate  discriminatory  risks  and 
contribute  to  the  protection  of  the  rights  of  the  child,  the  right  to  human  dignity  as  well  as  the 
protection  of  personal  data  and  privacy  online.  It  namely  defines  clear  responsibilities  and 
accountability  for providers of intermediary services,  and in  particular online platforms, such as 
social  media  and  marketplaces.  By  setting  out  clear  due-diligence  obligations  for  certain 
intermediary  services,  the  Digital  Services  Act  seeks  to  improve  users’  safety  online  across  the 
entire Union and improve the protection of their fundamental rights.  

Recognising the particular impact of very large online platforms on our economy and society, the 
proposal sets  a higher standard of transparency and accountability on how the providers of such 
platforms  moderate  content,  on  advertising  and  on  algorithmic  processes.  It  sets  obligations  to 
assess the systemic risks their systems pose and to develop appropriate risk management tools to 
protect  the  integrity  of  their  services  against  the  use  of  manipulative  techniques.  The  Digital 
Services  Act  also  includes  specific  obligations  relating  to  recommender  systems  used  by  very 
large online platforms and systems to display online advertising on their online interface. 

The AI liability proposal does not interfere with the liability exemptions laid down in the Digital 
Services  Act.  It  does  not  create  new  claims  or  harmonise  the  substantive  conditions  of  existing 
claims,  but  comes  in  where  MS’  liability  rules  do  offer  an  avenue  to  compensation,  i.e.  in 
situations not falling under the liability exemptions harmonised by the Digital Services Act. This 
might for instance be the case where claims are not based on the alleged illegality of third-party 
information,  but  on  damage  induced  by  the  functioning  of  the  AI  systems  used  by  such 
providers.295 

3.3. Data protection acquis 

294 Proposal COM/2020/825 final for a Regulation of the European Parliament and of the Council on a Single Market 
For Digital Services (Digital Services Act) and amending Directive 2000/31/EC. 
295 Cf. in this respect footnote 156 of advocate general Øe’s opinion in case C-682/18, Frank Peterson v. Google LLC, 
YouTube LLC, e.a., ECLI:EU:C:2020:586: "[I]t is possible that, where a service provider controls an algorithm, that 
service provider may be held liable for the damage caused by the functioning of that algorithm in itself. [...] I repeat 
that the exemption under Article 14(1) of Directive 2000/31 covers only liability for stored information." 

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The  General  Data  Protection  Regulation  (Regulation  (EU)  2016/679)  and  the  Law  Enforcement 
Directive (Directive (EU) 2016/680) aim to protect the fundamental rights and freedoms of natural 
persons,  and  in  particular  their  right  to  the  protection  of  personal  data,  whenever  their  personal 
data  are  processed.  This  covers  the  processing  of  personal  data  through  ‘partially  or  solely 
automated  means’,  which  includes  any  AI  system  that  processes  personal  data.  Users  that 
determine the purpose and means of the AI processing (‘data controllers’) have to comply with a 
number  of  data  processing  principles  such  as  lawfulness,  transparency,  fairness,  accuracy,  data 
minimisation,  purpose  and  storage  limitation,  confidentiality  and  accountability.  On  the  other 
hand, natural  persons, whose personal  data are processed, have a number of rights,  for instance, 
the right to access, correction, not to be subject to solely automated decision-making with legal or 
similarly significant effects unless specific conditions apply, to obtain human intervention, and to 
contest the decision. Stricter conditions also apply for the processing of sensitive data, including 
biometric data for identification purposes, while processing that poses high risk to natural persons’ 
rights  and  freedoms  requires  a  data  protection  impact  assessment.  Regarding  civil  liability,  any 
person  who  has  suffered  material  or  non-material  damage  as  a  result  of  an  infringement  of  the 
General Data Protection Regulation has the right  to receive compensation from the controller or 
processor for the damage suffered (Article 82). 

3.4.  Non-discrimination/equal  treatment  acquis  and  ongoing  work  to  in  relation  to 
discrimination risks linked to AI 

(a) Existing instruments 

The EU equality acquis prohibits discrimination based on a number of protected grounds (such as 
racial and ethnic origin, religion, sex, age, disability and sexual orientation) and in specific context 
and sectors (for example, employment, education, social protection, access to goods and services). 
This  existing  acquis  has  been  complemented  with  the  new  EU  Accessibility  Act  setting 
requirements for the accessibility of goods and services, to become applicable as of 2025. Users of 
AI systems are bound by the following equality Directives: 

-  Directive  2006/54/EC  relating  to  the  implementation  of  the  principle  of  equal 
opportunities  and  equal  treatment  of  men  and  women  in  matters  of  employment  and 
occupation; 

-  Directive  2004/113/EC  relates  to  the  implementation  of  the  principle  of  equal 
treatment between men and women in the access to and supply of goods and services 
and  thus  deals  with  gender  equality  in  the  consumption  market  (the  fields  of  media, 
advertising and education are excluded from this Directive’s scope of application); 
-  Directive  92/85/EEC  relating  to  workplace  safety  and  health  for  pregnant  and 

breastfeeding women and women who have recently given birth; 

-  Directive  2019/1158/EU  (work-life  balance),  79/7/EEC  (social  security)  and 
2010/41/EU  (the  self-employed)  extending  the  protection  of  the  principle  of  gender 
equality in matters related to the labour market; 

-  Directive  (EU)  2019/882  relating  to  accessibility  requirements  for  products  and 

services. 

158 

 
 
 
-  The  Racial  Equality  Directive  2000/43/EC  implementing  the  principle  of  equal 
treatment between persons irrespective of racial or ethnic origin (the ‘Racial Equality 
Directive’), prohibiting discrimination based on racial or ethnic origin in employment, 
social protection, including social security and healthcare, social advantages, education 
and the access to and supply of goods and services available to the public. The material 
scope of this directive therefore extends beyond that of the gender acquis, as it includes 
education. 

-  The  Employment  Equality  Directive  2000/78/EC  establishing  a  general  framework 
for  equal  treatment  in  employment  and  occupation,  prohibiting  discrimination  in  the 
field  of  employment  and  occupation  based  on  religion  or  belief,  disability,  age  and 
sexual orientation (the ‘Employment Framework Directive’).  

It is important to note that the EU non-discrimination legal framework is not complete. It applies 
in  relation  to  discrimination  based  on  certain  grounds  (i.e.  gender,  racial  or  ethnic  origin, 
disability, age, religion or belief, and sexual orientation) and in specific areas. When it comes to 
discrimination based on disability, age, religion or belief and sexual orientation, EU law prohibits 
discrimination  in  the  area  of  employment/occupation  but  not  in  other  areas,  such  as  education, 
social protection and access to and supply of goods and services. A 2008 Commission proposal for 
a  horizontal  equal  treatment  directive  (COM/2008/0426  final)  aimed  to  fill  this  gap  but  the 
required unanimity had so far not been reached in Council. 

(b) Provisions on redress and burden of proof – interplay with AI liability proposal 

Pursuant  to  the  gender  Equality  and  Non-discrimination  Directives,  infringements  of  the 
prohibition of discrimination must be met with effective, proportionate and dissuasive sanctions, 
which  may  include  compensation  being  paid  to  the  victim.  The  Directives  do  not  prescribe 
specific  measures  and  allow  Member  States  to  decide  on  suitable  remedies  for  achieving  the 
objectives pursued. Depending on the legal avenue chosen, these can take various forms, such as a 
fine,  compensation,  an  injunction  for  the  wrongdoer  to  perform  or  refrain  from  certain  action, 
publicising the wrongdoing, requiring an apology or imposing criminal sanctions. 

Based on the consideration that discrimination is difficult to proof and that the person that may be 
responsible for the alleged discrimination (e.g. a professional user of an AI system) is typically in 
possession of the information needed to prove a claim, the Equality Directives envisage a shift of 
the  burden  of  proof.  Once  the  victim  establishes  sufficient  facts  (prima  facie  evidence)  for 
presuming discrimination,  the burden of proof shifts  to  the defendant, who has to  show that the 
difference  in  treatment  is  not  discriminatory.  The  key  effect  of  this  adaptation  of  the  burden  of 
proof in non-discrimination cases is that it alleviates the burden on victims to show a clear causal 
link between the protected ground and the harm. The burden of proof shifts even if the causation 
between  the  protected  ground  and  the  harm  is  only  probable  or  likely.  The  Court  of  Justice  has 
clarified  that  the  victim  can  meet  the  prima  facie  threshold  for  instance  by  providing  general 
statistical evidence of discrimination.296 The Court also recognised that although the Directives do 
not give the victim a right to access relevant information, the fact that an employer refuses to give 

296 cf. e.g. Judgment of 8 May 2019, Villar Láiz. C-274/18, ECLI:EU:C:2019:828. 

159 

 
 
                                                           
the victim access to requested information is ‘among the factors which may be taken into account’ 
to establish a prima facie case of discrimination.297  

The defendant can rebut the presumed discrimination either by proving that there was no causal 
link  between  the  prohibited  ground  and  the  differential  treatment,  or  by  demonstrating  that 
although  the  differential  treatment  is  related  to  the  prohibited  ground,  it  has  a  reasonable  and 
objective justification. If the alleged discriminator is unable to prove either of the two, they will be 
liable for discrimination. 

As far as civil liability rules are concerned, the AI liability proposal will complement the existing 
mechanisms to facilitate compensation in accordance with the Equality Directives: 

- 

- 

the provisions on the disclosure of information about AI systems will help the victim to 
meet the prima facie threshold to trigger the shift of the burden of proof and  
the targeted alleviation of the burden of proof as regards the question how or why an 
AI  system  arrived  at  a  certain  output  will  dispense  victims  from  the  need  to  prove  a 
link  between  the  use  of  a  protected  ground  of  discrimination  and  a  discriminating 
action, even where this is not guaranteed by the existing acquis. 

(c) Further relevant policy measures and ongoing analysis 

The Commission’s pending legislative proposal on pay transparency proposal (lex specialis to the 
Gender Equality Directive 2006/54/EC), strengthens the tools for workers to claim their rights and 
facilitates access to justice. Employers are required to provide pay related anonymised data upon 
employee  request  and  employees  will  have  the  right  to  compensation  for  discrimination  in  pay. 
The  existing  reversal  of  the  burden  of  proof  under  Directive  2006/54/EC)  is  re-enforced,  to  the 
effect that employers who do not respect their pay transparency obligations would have to prove 
that  there  was  no  discrimination  in  relation  to  pay.  Therefore,  the  burden  of  proof  shifts  to  the 
defendant  without  requiring  the  worker  to  establish  even  a  prima  facie  case  of  discrimination. 
These provisions are highly relevant with respect to AI-induced algorithmic discrimination, as the 
(even  prima  facie)  burden  of  proof  would  be  particularly  difficult  to  meet  for  victims  where 
unequal pay or other forms of discrimination are triggered by an opaque AI system.  

As part of the ongoing efforts to achieve a ‘Union of Equality for All’, the European Commission 
has  included  legislative  measures  aimed  at  strengthening  the  role  and  independence  of  equality 
bodies in its work programme for 2022. These bodies play a key role in upholding the right of all 
persons to be protected. The initiative is scheduled for the second half of 2022. In preparation of 
this upcoming initiative, the Commission is exploring certain avenues relevant to address possible 
challenges posed by the use of AI systems that may lead to discrimination. Such avenues include 
the role of equality bodies in relation to collective complaint mechanisms and strategic litigation, 
effective  mechanisms  for  cooperation  with  relevant  stakeholders  and  duty  bearers,  a  reinforced 
advisory and awareness raising role, etc.  

Finally, as announced in the EU Anti-Racism Action Plan 2020-2025, the Commission is looking 
into  possible  gaps  related  to  the  EU  legislation  prohibiting  racial  and  ethnic  discrimination,  in 

297 Judgment of 19 April 2012, Galina Meister v Speech Design Carrier Systems GmbH C-415/10 EU:C:2012:217 [44], [47]. 

160 

 
 
                                                           
particular  in  the  area  of  law  enforcement.  The  use  of  AI  will  be  taken  into  consideration  when 
executing this gaps assessment. 

161 

 
 
 
 
 
Annex 9 

The types of compensable (in particular immaterial) harm and the 
admissibility of contractual exclusions/limitations of liability – 
Member States’ legal approaches/traditions and reasons for not 
harmonising these aspects specifically for AI 

Context and introduction 

In its legislative own-initiative resolution on a civil liability regime for artificial intelligence298, the 
European Parliament requested the Commission to evaluate, taking into account existing national 
rules and legal traditions, whether certain aspects relating to liability for AI should be covered by 
the future initiative. The Parliament inquired in particular whether: 

 

 

the inclusion of immaterial damage in an AI-specific EU instrument is legally sound and 
necessary; 
there is a need to include regarding contractual exclusions/limitations of liability in an AI-
specific initiative. 

This Annex provides an overview of the Commission’s findings following up on these requests by 
the European Parliament, and explains the proposed regulatory choices, taking into account these 
findings. 

In  Section  A,  Member  States’  approaches  and  legal  traditions  regarding  the  compensation  for 
immaterial damage are summarised. For practical reasons and reasons of proportionality, this IA 
cannot incorporate a full and exhaustive description of the relevant legal framework. Instead, the 
main  findings  are  highlighted,  pointing  to  illustrative  examples  of  national  laws  and  identifying 
trends and differences. On this basis, it is explained why the preferred policy option on AI liability 
does not include provisions on the types of compensable harm. 

Section B reports the main findings regarding the question of contractual exclusions / limitations 
of  liability,  and  explains  why  the  preferred  policy  option  on  liability  for  AI  does  not  seek  to 
harmonise this question either. 

A.  

Immaterial harm 

Examples of relevant provisions (non-exhaustive): Austria: Art. 1323, 1324 ABGB; Denmark: 
§ 26  Erstatningsansvarslov;  Finland:  Chap. 5  §  6  Liability  Act;  Germany:  Section.  253  para.  1 
BGB:  Greece:  Art.  57,  59,  299,  932,  933  CC;  Italy:  Art. 2043,  2059  CC,  Art. 185  CP;  the 
Netherlands: Art. 6:106,  6:162, 6:95 et seqq. BW; Poland: Art. 448 Civil Code; Portugal: Art. 483 
(1), 496 CC; Spain; Art. 1902 CC, Art. 110 (3) Código Penal; Sweden: Chap. 2 § 3 Liability Act. 

298 European Parliament resolution of 20 October 2020 with recommendations to the Commission on a civil liability 
regime for artificial intelligence (2020/2014(INL)). 

162 

 
 
 
                                                           
1. 

Background: the approach proposed by the European Parliament 

The European Parliament proposed that, in line with strict liability systems of the Member States, 
a  harmonised  civil  liability  regime  for  AI  should  cover  violations  of  the  important  legally 
protected  rights  to  life,  health,  physical  integrity  and  property.  In  addition,  the  Parliament 
considered that such a regime should also incorporate “significant immaterial harm that results in 
a verifiable economic”. However, before adopting such approach, the Commission should analyse 
the  legal  traditions  in  the  MS  and  their  existing  national  laws  that  grant  compensation  for 
immaterial harm, in order to evaluate if the inclusion of immaterial harm in AI-specific legislative 
acts  is  necessary  and  if  it  contradicts  the  existing  Union  legal  framework  or  undermines  the 
national law of the Member States”.299 

In the draft proposal annexed to its resolution, the European Parliament specifies that “significant 
immaterial harm should be understood as meaning harm as a result of which the affected person 
suffers considerable detriment, an objective and demonstrable impairment of his or her personal 
interests and an economic loss calculated having regard, for example, to annual average figures 
of past revenues and other relevant circumstances”.300 

2. 

The relevant categories of harm 

The  concepts  needed  to  discuss  the  questions  raised  by  the  European  Parliament  –  ‘harm’, 
‘immaterial  harm’,  ‘damage’,  ‘loss’  –  are  not  harmonised  across  MS  legal  systems.  Different 
terminology is used, sometimes interchangeably, and the attached meaning can vary from one MS 
to  another.  To  nevertheless  develop  a  consistent  comparative  overview,  a  broad  concept  of 
‘damage’  is  used  in  this  Annex,  referring  to  any  type  of  loss,  harm  or  injury.  Where  a  more 
granular terminological differentiation is needed, specific explanations are made to that effect. 

For the purposes of this summary, immaterial or non-material damage refers to losses which do 
not relate to a person’s assets, wealth or income and, as such, cannot be quantified in an objective 
manner  by  reference  to  a  market  price  or  value.301  Such  losses  are,  for  example,  damage  to 
reputation, discrimination, loss of confidentiality, psychological harm and mental suffering. This 
type of damage is different from the concept of pure economic loss. 

Pure economic loss usually refers to financial loss suffered by a party, which is not the result of 
any  damage  to  that  party’s  own  person  or  property.  Instead,  the  damage  arises  from  economic 
relationships  in  which  the  party  is  involved.  This  concept  is  distinct  from  the  concept  of 
consequential  economic  loss,  i.e.  loss  that  arises  from  physical  injury  or  property  damage. 
However, one should bear in mind that there is no universally accepted definition or description of 
pure economic loss in MS’ or EU law.302 

3.  

Summary of national legal traditions and approaches 

299 Point 19 of the resolution. 
300 Annex B, recital 16. 
301 Cf. e.g. the Opinion of Advocate General Wahl in case C-371/12, Petillo v Unipol, ECLI:EU:C:2013:652, para. 38, 
with  reference  to  Cf.  Horton  Rogers,  W.V.  (ed.),  Damages  for  Non-Pecuniary  Loss  in  a  Comparative  Perspective, 
European Centre of Tort and Insurance Law, Springer Verlag, Wien New York: 2001, p. 246. 
302  Van  Bar/Drobnig,  Study  on  Property  Law  and  Non-contractual  Liability  Law  as  they  relate  to  Contract  Law, 
p. 134. 

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3.1.  Basic distinctions and trends 

The comparative law study commissioned for this impact assessment highlighted that  “there are 
important differences throughout Europe when it comes to recognizing which damage triggers tort 
claims  in  the  first  place  (specifically  evident  in  the  case  of  pure  economic  loss).  Furthermore, 
jurisdictions differ with regard to which consequences of an initial harm will be indemnified at all. 
The range and  extent of remedies available are  equally divergent,  in  particular  (but  clearly not 
limited to) the extent of compensation for immaterial harm.”303 

While all Members States’ civil law systems know the concept of non-material damage, there is 
no uniform  approach to  its  recoverability.  Broadly  speaking, Members States follow one of two 
models: some do not differentiate between material and immaterial damage and simply consider 
both  damages  equally  recoverable.  Especially  Napoleonic  legal  systems  apply  this  approach. 
Other Member States only accept compensation for immaterial damage if expressly provided for 
by law. Especially Germanic legal systems apply this approach.304 

There  is  also  no  uniform  approach  as  to  whether  the  question  of  compensable  harm  should  be 
addressed differently under fault-based and strict liability.305 

Some  legal  systems  treat  compensation  for  immaterial  harm  differently  under  contract  law  and 
under tort law. Developments usually start in tort law and later expand to contract law. In several 
Member  States,  one  can  observe  an  increasing  acceptance  of  compensation  for  immaterial 
damage.306 

3.2.  Legal systems that generally allow recovery of immaterial damage 

The  first  category  of  Member  States,  i.e.  those  that  generally  allow  recovery  for  immaterial 
damage, does not distinguish between material and immaterial damage. This category includes, for 
example, Belgium, France, Luxembourg, Spain, Hungary and Slovenia. The starting point is that 
all damage or damage in general is recoverable. There is little distinction between cases of fault 
liability  and  cases  of  strict  liability.  Recovery  serves  (also)  the  purpose  to  ease  non-material 
suffering  and  compensate  the  irreparable.307  That  is  not  to  say  that  these  legal  systems  do  not 
apply  restrictions  on  the  recovery  of  immaterial  damage.  In  Portugal,  for  example,  loss  is  only 
recoverable if it is sufficiently severe to deserve legal protection.308 

3.3.  Legal systems that only allow recovery for immaterial damage in certain cases 

The  second  category  of  Member  States  only  allows  recovery  of  immaterial  damage  if  expressly 
provided  for  by  law.  For  example,  Italy,  Germany,  Poland,  Austria,  the  Netherlands,  Estonia, 

303 Comparative Law Study, op.cit., p. 9, 105. 
304 Ibid., p. 108. 
305 Von Bar et al. (eds), Principles, Definitions and Model Rules of European Private Law – Draft Common Frame of 
Reference (DCFR), p. 3052-3059. 
306 Martin-Casals in: The Borderlines of Tort Law: Interactions with Contract Law, London 2019, p. 731(733). 
307 Ibid., p. 731. 
308  Van  Bar/Drobnig,  Study  on  Property  Law  and  Non-contractual  Liability  Law  as  they  relate  to  Contract  Law, 
p. 109. 

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Lithuania  and  the  Nordic  Countries  follow  this approach.  These  Member  States  depart  from  the 
opposite premise: immaterial damage is generally non-recoverable.  

However,  there  are  a  number  of  “exceptions”,  in  which  the  law  expressly  provides  for  the 
recovery  of  immaterial  damage.  One  such  situation  is  if  the  infringed  rights  deserve  special 
protection. In this sense, most legal systems allow recovery if the tort infringes certain protected 
rights such as health and bodily integrity, liberty or the right to sexual self-determination. This is 
true,  for  instance,  for  Italy,  Poland,  Greece,  the  Czech  Republic,  Slovakia,  Germany,  the 
Netherlands, Estonia and the Nordic Countries. Similarly, Italy generally permits recovery if the 
tort also constitutes a crime or if the tort causes a permanent and negative detrimental effect on a 
person’s  life  and  social  interaction  with  others.  The  Spanish  legal  system,  which  generally 
considers immaterial damage as recoverable, also expressly provides for compensation in case of 
criminal activity.309 

Intention  or  gross  negligence  may  also  justify  compensation  for  immaterial  damage.  This  is  the 
case  in  Austria  and  the  Netherlands.  In  cases  of  intentional  damage,  the  tort  will  often  also 
constitute a crime.310 

The Member States also frequently grant compensation for immaterial damage if recovery would 
otherwise be difficult or impossible. Poland, Estonia and the Nordic Countries allow recovery if 
the tort concerns incorporeal rights of personality. The Netherlands permit recovery if the harming 
party violates the reputation of the dead. 

In  the  context  of  the  private  enforcement  of  intellectual  property  rights  (IPRs),  Directive 
2004/48/EC311 requires national judicial authorities to take into account, when setting the damages 
to  be paid  to  the rightholder for an infringement  of  IPRs, elements  other than economic factors, 
such  as  the  moral  prejudice  caused  to  the  rightholder  by  that  infringement.  However,  as  the 
Directive  gives  MS  the  option  to  provide  instead  for  a  compensation  of  IPR  infringements  by 
lump sum  payments  (based e.g. on hypothetical  royalties or fees), it did  not  force MS to  depart 
from their traditional approach regarding the recoverability of immaterial harm. 

3.4.  Damage to or loss of data 

Currently, Member States mostly do not consider data as property. Accordingly, damage to or loss 
of data does not give rise to a tort claim under general rules. However, Article 82 of the General 
Data Protection Regulation312 gives any person who has suffered material or non-material damage 
as  a  result  of  an  infringement  of  that  Regulation  the  right  to  receive  compensation  from  the 
controller  or  processor  for  the  damage  suffered.  While  there  is  a  recent  tendency  in  national 

309 Martin-Casals in: ibid., The Borderlines of Trot Law: Interactions with Contract Law, London 2019, p. 732; van 
Bar/Drobnig, Study on Property Law and Non-contractual Liability Law as they relate to Contract Law, p. 109, 112. 
310 Martin-Casals in: ibid., The Borderlines of Trot Law: Interactions with Contract Law, London 2019, p. 732. 
311  Directive  2004/48/EC  of  the  European  Parliament  and  of  the  Council  of  29  April  2004  on  the  enforcement  of 
intellectual property rights (OJ L 157, 30.4.2004, p. 45). 
312 Regulation (EU) 2016/679  of the European Parliament and of the Council of 27 April 2016 on the protection of 
natural persons with regard to the processing of personal data and on the free movement of such data, and repealing 
Directive 95/46/EC (General Data Protection Regulation) (OJ L 119, 4.5.2016, p. 1). 

165 

 
 
                                                           
legislation, court decisions and doctrine to ascribe proprietary attributes to data,313 it needs to be 
kept in mind that the right to the protection of personal data is an inalienable fundamental right, 
which is not compatible with its assimilation to property.  

3.5.  Damage caused by discrimination 

A  vast  majority  of  Member  States  provides  for  compensation  awards  in  the  case  of  unlawful 
discrimination.314  Legal  mechanisms  to  ensure  effective,  proportionate  and  dissuasive  sanctions 
were notably developed to transpose the EU’s equality Directives (see in this respect Annex 8 on 
fundamental rights risks of AI). However, the amounts of damages, in particular for non-pecuniary 
harm linked to discrimination, have been pointed out as insufficient by experts and vary widely. In 
a  recent  comparative  analysis  of  non-discrimination  and  gender  equality  law  in  Europe315, 
prepared on behalf of DG JUST by the European network of legal experts in gender equality and 
non-discrimination, the remedies available in MS in the case of infringements of EU directives in 
non-discrimination and gender equality have been summarised. According to a  2015 report from 
the European Network of Equality Bodies, Equinet, almost all MS provide for Compensation for 
immaterial damage in discrimination cases316. It is important to note however that the EU equality 
Directives are incomplete and do not cover all possible areas and/or grounds of discrimination. 

3.6. 

Immaterial harm suffered by legal persons 

Most  Member  States  accept  immaterial  damage  of  legal  persons,  e.g.  in  cases  of  injury  to 
reputation.  France,  Belgium,  Spain,  Austria,  Italy,  Hungary,  Slovenia,  Poland,  the  Netherlands, 
Greece, Portugal and the Nordic countries have adopted this approach. On the contrary,  e.g. the 
German  legal  system  only  allows  legal  persons  to  claim  for  immaterial  damage  in  a  restricted 
number of cases.317 

3.7. 

Personality rights 

The Member States largely accept that tort liability may arise out of the infringement of the right 
to one’s name or the unauthorised use of one’s image. Many legal systems also protect these rights 
after the death of the concerned party (post mortem).  Legal systems that permit legal persons to 
claim damages for immaterial harm accept that these legal persons are also entitled to personality 
rights.318 

3.8. 

Personal injury of others 

If  a  person  is  killed,  most  but  not  all  European  systems  grant  close  relatives  compensation  as 
secondary  victims.  The  requirements  differ  substantially.  Austria,  for  example,  only  permits 

European 

313 Stepanov, Introducing a property right over data in the EU: the data producer’s right – an evaluation, International 
Review of Law, Computers & Technology 2020, p. 65 et seqq. 
314 Von Bar et al., DCFR, pp. 237 et seq., pointing to Greek law as the only exceptional case where civil law does not 
provide for compensation, but only penal sanctions (ranging from administrative fines to imprisonment) apply. 
315 
in 
https://www.equalitylaw.eu/publications/comparative-analyses. 
316 See table at page 19 of the report by K. Wladasch, ‘The sanctions regime in discrimination cases and its effects’, 
available 
https://migrate.equineteurope.org/wp-content/uploads/2015/12/sanctions_regime_discrimination_-
_final_for_web.pdf 
317 Von Bar et al., DCFR, pp. 3059 et seq. 
318 Ibid., pp. 3061-3066. 

non-discrimination, 

network 

equality 

experts 

gender 

legal 

and 

at: 

of 

166 

 
 
                                                           
compensation if the primary victim was killed by an act of gross negligence. Personal injury other 
than death, however, does generally not lead to compensation for close relatives. In addition, the 
amounts of compensation vary significantly among the Member States.319 

3.9. 

Pure economic loss 

Some  Member  States  distinguish  the  concept  of  pure  economic  loss  from  other  types  of  losses. 
Others  do  not  view  it  as  a  separate  type  of  damage.  Regardless  of  this  distinction,  most  legal 
systems accept compensation for pure economic loss in case of intentional conduct. However, the 
situation is different for negligent behaviour. If such conduct is apparent, the Member States that 
conceptualize  pure  economic  loss  do  not  universally  accept  compensation  for  this  type  of  loss. 
Instead, this matter remains contested.320 

(a)  Member States that acknowledge the notion of pure economic loss 

In Germany and Austria, which distinguish the notion of pure economic loss from other types of 
economic loss like property damage, recovery of pure economic loss is possible only if statutory 
law  explicitly  allows  it.  This  stems  from  the  fact  that  often,  an  injured  party  can  only  raise  tort 
laws claims if the other party has breached certain protected interests. Mere economic interests are 
not included among such protected interests. In Germany, the victim can recover pure economic 
loss  if  the  other  party’s  conduct  was  intentional  and  contrary  to  public  policy.  The  Dutch  legal 
system,  on  the  other  hand,  operates  on  a  more  pragmatic  case-by-case  basis  and  focuses  on  the 
socio-economic implications of recovery.321 

If  a  contracting  party  incurs  pure  economic  loss,  the  legal  systems  that  allow  its  recovery  in 
principle need to strike a delicate balance between the contractual risk allocation agreed upon by 
the parties and the default rules of compensation in tort law.322 

If  a  non-contracting  party  incurs  pure  economic  losses,  the  situation  can  also  be  complex.  If  a 
primary  victim  incurs  personal  injury,  a  secondary  victim  linked  to  this  person  may  suffer 
economic losses (relational economic loss). For example, the death of a family member can result 
in  losses  for  other  family  members  that  were  financially  depended  on  the  deceased  relative.  In 
such cases, recovery is generally impossible except for close relatives. Similarly, there are cases in 
which the loss that the primary victim would normally incur is actually incurred by a secondary 
victim (transferred loss). For example, damage to a leased car primarily constitutes damage to the 
lessor’s  property;  however,  it  also  results  in  an  economic  loss  of  the  lessee.  In  such  cases  and 
subject  to  certain  conditions,  the  secondary  victim  may  also  recover  the  economic  loss.323  The 
recovery of pure economic loss is also revenant if a third party relies on the negligent provision of 
information or defective services.324 

(b)  Member States that do not acknowledge the notion of pure economic loss 

319 Comparative Law Study, op.cit., p. 22, 44. 
320 Martin-Casals in: ibid., The Borderlines of Trot Law: Interactions with Contract Law, London 2019, pp. 745 et seq. 
321 Ibid., p. 746. 
322 Ibid., p. 747 et seq. 
323 Ibid., p. 749 et seq. 
324 Ibid., p. 750-752. 

167 

 
 
                                                           
Member States in which pure economic loss is not distinguished from other types of losses, such 
as  Poland,  France,  Italy,  Greece  and  Spain,  approach  this  issue  differently.  Claims  for  pure 
economic loss are possible but often limited. In France, a party can generally not raise tort claims 
if  contractual  claims  exist  (non-cumul  rule).  This  principle  is  also  the  starting  point  in  Belgium 
and  Luxembourg.325  Other  countries  limit  the  conditions  under  which  an  injured  part  can 
successfully bring forward such claims. For example, Poland and Greece limit the circle of victims 
eligible  for  the  compensation  to  directly  affected  parties.  Spain  applies  rules  of  certainty  to 
damages and limits the scope of liability.326 

3.10.  Breach of an absolute right 

The  recoverability  of  pure  economic  loss  question  is  closely  intertwined  with  the  question  of 
whether tort liability requires the breach of absolute rights or other protected legal interests. Such 
requirement  is  expressly  imposed  in  Section 823(1)  of  the  German  BGB.  Others,  like  e.g.  the 
Austrian, Dutch and Estonian legal systems, follow a similar approach. Conversely, the majority 
of Member States does  not  require the breach of absolute rights.  For instance,  Belgium, France, 
Malta, Luxembourg, Spain, Italy, the Czech Republic, Slovakia, Hungary, Poland and Slovenia do 
not establish such condition. Some of these legal systems also protect relative rights arising from 
contractual  agreements  to  a  certain  degree  under  tort  liability.  For  example,  this  is  the  case  in 
France. 

4. 

Conclusion: Reasons for not including provisions on the types of compensable harm 
on the preferred policy option on AI liability 

In light of the analysis of national legal traditions and existing approaches, it may be true that if a 
legal system does not universally permit recovery of immaterial damage, victims may not always 
be able to claim compensation for their immaterial damage. Nevertheless, it is appropriate to leave 
the  question  of  whether  and  to  what  extent  immaterial  harm  gives  rise  to  civil  liability  claims 
outside of the scope of the AI liability initiative, for the following reasons: 

First,  the  specific  characteristics  of  AI  (such  as  an  increasing  degree  of  autonomy,  complexity, 
opacity, and a lack of transparency and predictability) do not alter the existing types or concepts of 
damage.  The  recoverability  of  immaterial  harm  therefore  does  not  form  part  of  the  AI-specific 
issues to be addressed by the AI liability initiative. The significant divergences between national 
legal  systems  as  outlined  above  do  not  relate  to  these  AI-specific  characteristics.327  During  a 
Workshop  with  MS  representatives,  those  that  took  a  position  were  in  favour  of  leaving  the 
definition of compensable harm to national law. 

Second,  Member  States  have  developed  varying  concepts  and  approaches  as  regards  the 
compensation  for  immaterial  damage.  Novel  concepts  such  as  “significant  immaterial  harm 
resulting in a verifiable economic loss” will not easily fit into the existing legal regimes.  

325  Van  Bar/Drobnig,  Study  on  Property  Law  and  Non-contractual  Liability  Law  as  they  relate  to  Contract  Law, 
p. 205. 
326 Martin-Casals in: ibid., The Borderlines of Trot Law: Interactions with Contract Law, London 2019, p. 752-754 
327 The Expert Group on Liability and New Technologies concluded: “[G]enerally speaking, AI and other emerging 
digital technologies do not call into question the existing range of compensable harm per se.”, Expert Group Report, 
p. 19. 

168 

 
 
                                                           
Third, treating AI-induced damage  as  a category apart when it comes  to  its  recoverability could 
upset the coherence of the existing national rules concerning the types of compensable harm. The 
prevailing  divergences  between  MS’  approaches  and  legal  traditions  apply  horizontally 
irrespective  of  whether  immaterial  damage  was  caused  with  the  involvement  of  AI  systems  or 
not.328  Harmonising  this  aspect  in  the  AI  liability  proposal  would  potentially  lead  to  a  situation 
where victims can claim compensation for certain types of harm, but only if AI was involved in 
causing  it.  In  addition  to  concerns  regarding  the  coherence  of  such  an  approach,  it  might  also 
represent an uneven playing field  for AI-enabled products and services. Such an outcome would 
not be aligned with the policy objective to promote the roll-out of lawful and safe AI in Europe. 

Fourth, it is doubtful whether the current lack of harmonisation of the types of compensable harm 
amounts  to  an  internal  market  barrier  justifying  harmonised  measures.  According  to  previous 
research,  businesses  tend  to  think  that  the  recoverability  of  immaterial  damage  does  not  play  a 
large  role  in  selecting  their  place  of  business  within  the  EU,  despite  the  significant  differences 
between Member States.329 

Fifth,  recoverability  of  damage  in  tort  law  has  implications  for  the  recoverability  of  damage  in 
contract law. Harmonising the recoverability in tort law could lead to wider gaps between contract 
and tort law. Countries that treat recoverability of immaterial damage in tort and contract law the 
same or at least similar might face difficulties implementing such EU measures into their national 
law. 

B.  

Contractual exclusions / limitations of liability waivers 

Examples  of  relevant  provisions  (non-exhaustive):  Czech  Republic:  § 2898  CC;  France: 
Art. 1231-3,  1245-14  Code  civil;  Germany:  § 276  para. 3,  § 278,  §  307  No.  7  BGB;  Greece: 
Art. 332 CC; Italy: Art. 1229 CC; Art. 12, 18 Portugal: Decreto-Lei No. 249/99; Spain: Art. 1255 
CC. 

1. 

Background and scope of the summary 

In  its  Resolution  of  20 October  2020,  the  European  Parliament  requested  the  Commission  “to 
evaluate  the  need  for  legal  provisions  at  Union  level  on  contracts  to  prevent  contractual  non-
liability clauses”.330 

The  Product  Liability  Directive  includes  such  provision  in  its  Art. 12:  “The  liability  of  the 
producer  arising  from  this  Directive  may  not,  in  relation  to  the  injured  person,  be  limited  or 
excluded by a provision limiting his liability or exempting him from liability.”331 

328  The  Comparative  Law  Study  commissioned  for  this  impact  assessment  emphasised  that  “while  claims  for 
compensation  invariably  require  that  the  victim  incurred  some  harm,  the  range  of  compensable  losses  and  the 
recognized heads of damage will not be different in AI cases than in any other tort scenario.”, cf. Comparative Law 
Study, pp. 9 and 105. 
329  Van  Bar/Drobnig,  Study  on  Property  Law  and  Non-contractual  Liability  Law  as  they  relate  to  Contract  Law, 
p. 108. 
330 Para. 22. 

169 

 
 
 
                                                           
Liability waivers are primarily relevant in the context of contractual claims. However, almost all 
Member  States  permit  certain  limitations  of  non-contractual  (tort)  liability  as  well,  although  the 
detailed conditions vary. The possibility to exclude or limit liability has the potential to undermine 
the  protection  that  the  liability  regime  intends  to  provide.  In  the  context  of  the  AI  liability 
initiative,  the  following question  arises:  Should  the  EU  also  regulate  and  thereby  harmonize  the 
ability  to  limit  or  exclude  liability  specifically  for  damage  caused  by  AI?  Prior  to  making  this 
policy  choice,  a  thorough  stocktaking  of  existing  regulations  on  an  EU  as  well  as  on  a  national 
level  is  essential.  This  section  summarises  the  Commission  services’  findings  regarding  the 
admissibility,  under  existing  EU  or  national  law,  of  contractual  waivers  in  the  context  of  tort 
liability  claims.  It  also  takes  into  account  legal  systems  that  preclude  claimants  from  invoking 
contractual and non-contractual claims at the same time (non-cumul principle). 

The summary is focuses on liability waivers that the parties agreed upon prior to the onset of the 
damage. Liability waivers that the parties agreed upon after the onset of the damage are a different 
issue. This includes questions such as whether the waiver bars an injured party from bringing its 
claim  from  a  procedural  point  of  view.  In  any  case,  the  introduction  of  a  new  liability  regime 
would not interfere with the possibility to settle a dispute amicably outside of a court. 

2. 

Summary of the existing rules governing the admissibility of contractual exclusions / 
limitations of tort liability 

There  is  no  uniform  treatment  of  contractual  liability  waivers  among  the  Member  States  in  the 
context  of  tort  law.  Almost  all  legal  systems  allow  such  contractual  agreements  to  some  extent 
(but  see  2.1.  for  exceptions).  However,  they  impose  several  restrictions,  a  violation  of  which 
renders the waiver  null and void.  Broadly speaking, contractual  liability  waivers  will usually be 
effective only if the parties limit  their scope to  situations of ordinary  negligence  and damage to 
property. 

2.1.  Legal systems that do not allow liability waivers in tort law 

While  most  Members  States  accept  contractual  liability  waivers  of  tort  claims  at  least  under 
certain  conditions,  the  French  legal  system  has  adopted  a  more  restrictive  approach.  Extra-
contractual  liability  is  part  of  France’s  ordre  public.  Therefore,  except  for  special  cases, 
contractual agreements that attempt to limit or exclude liability for torts committed in the future, 
are  invalid.  In  other  Member  States  such  as  Spain,  Italy,  Bulgaria  and  Portugal,  academic 
literature at least discusses a similarly restrictive approach.332 The fact that under the French legal 
system, a party can only bring a claim under tort law if no contract exists (non-cumul principle) is 
relevant  to  understand this approach. However, the inadmissibility of contractual waivers of tort 
liability is not a necessary consequence of the non-cumul principle, as shown e.g. by the Belgian 

331 Council Directive of 25 July 1985 on the approximation of the laws, regulations and administrative provisions of 
the Member States concerning liability for defective products, 85/374/EEC. 
332 Von Bar et al., Principles, Definitions and Model Rules of European Private Law, p. 3551 et seqq; Bar/Drobnig, 
Study on Property Law and Non-contractual Liability Law as they relate to Contract Law, p. 168. 

170 

 
 
                                                                                                                                                                                     
legal  system,  which  also  acknowledges  the  non-cumul  principle  but  permits  waivers  of  tort 
liability.333  

2.2.  Legal systems that allow liability waivers in tort law under certain restrictions 

While  the  majority  of  Members  States  permits  the  parties  to  agree  on  liability  waivers,  all 
Members  States  substantially  restrict  the  use  of  such  clauses  in  the  context  of  tort  law.  Due  to 
these  limits,  the  outcome  does  often  not  differ  greatly  from  the  French  approach.  The  main 
restrictions  of  contractual  limitations  /  exclusions  of  liability  are  summarised  in  the  subsequent 
points. 

(a) 

Intention or gross negligence 

All  Members  States  prevent  the  parties  from  excluding  or  limiting  liability  in  the  case  of 
intentionally  harmful  conduct.  For  example,  § 276  para. 3  of  the  German  BGB  provides:  “The 
obligor may not be released in advance from liability for intention.” Intentionally causing harm to 
another party will often also constitute a crime. Limiting liability for such actions conflicts with 
the principles of morality that are often also part of the ordre public. 

Many  legal  systems  also  prohibit  liability  waivers  for  instances  of  gross  negligence.  This  is  the 
case, for example, in the Czech Republic, Italy and Greece. In Austria, different opinions on this 
matter exist.334 Proving intentional damage will often be difficult for the claimant. Therefore, the 
injured  party  will  face  difficulties  if  the  damaging  party  has  excluded  its  liability  for  grossly 
negligent conduct. This is different in Member States that do not permit such waivers leading to 
greater protection of the injured party.  

The  situation  is  more  heterogeneous  with  respect  to  acts  of  auxiliaries.  Some  jurisdictions  are 
more  liberal  with  regards  to  intentional  or  grossly  negligent  conduct  of  auxiliaries  and  permit 
waivers  excluding  or  limiting  liability  for  such  acts.  For  example,  Germany  has  adopted  this 
approach. Other Members States such as Italy apply the same or at least similar rules for principals 
and auxiliaries. Consequently, liability waivers cannot extend to damage intentionally caused by 
auxiliaries.  The  same  is  true  for  grossly  negligent  conduct  of  auxiliaries  if  the  principal  cannot 
restrict his or her liability in such cases.335 

(b) 

Personal injury 

Generally, the provisions regulating waivers for personal injuries are stricter than those regarding 
waivers for property damage. Some countries such as Spain and Poland do not allow waivers of 
liability for personal injury. The Spanish legal system goes even further and prohibits clauses that 
limit  liability  for  acts  which  violate  moral  integrity,  fundamental  rights  or  human  dignity,  or 
amount to an abuse of a dominant position. Protection of bodily integrity and other fundamental 
rights  is  often  part  of  the  ordre  public.  Therefore,  parties  cannot  limit  their  protection  by 

333  Von  Bar  et  al.,  Principles,  Definitions  and  Model  Rules  of  European  Private  Law,  p.  3551;  Martin-Casals, 
Comparative  Report,  in:  The  Borderlines  of  Tort  Law:  Interactions  with  Contract  Law,  London  2019,  p. 784;  van 
Bar/Drobnig, Study on Property Law and Non-contractual Liability Law as they relate to Contract Law, p. 166 et seq. 
334  Martin-Casals,  Comparative  Report,  in:  The  Borderlines  of  Tort  Law:  Interactions  with  Contract  Law,  London 
2019, p. 782 et seq. 
335 Ibid., p. 783. 

171 

 
 
                                                           
contractual  agreements.336  Similarly,  the  Unfair  Contract  Terms  Directive  prohibits  the  use  of 
general  terms “excluding or limiting the legal liability of  a seller or supplier in  the event  of  the 
death of a consumer or personal injury to the latter resulting from an act or omission of that seller 
or supplier”.337 

(c) 

Product liability 

Pursuant  to  Article  12  of  the  Product  Liability  Directive,  the  parties  can  generally  not  agree  on 
contractual  liability  waivers  for  claims  arising  from  that  Directive.  The  Members  States  have 
implemented this or a similar provision into their national law. 

(d)  General terms and conditions 

Some Members States limit the possibility to include liability waivers in one’s general terms and 
conditions. For example, under the German legal system, a party cannot exclude liability for injury 
to  life, body or health  and in  case of gross fault in  its general  terms  and conditions  (Art. 309(7) 
BGB).  Austria,  Greece  and  Portugal  have  adopted  a  similar  approach.338  The  Unfair  Contract 
Terms  Directive  prohibits  general  terms  with  the  object  or  effect  of  “excluding  or  limiting  the 
legal liability of a seller or supplier in the event of the death of a consumer or personal injury to 
the latter resulting from an act or omission of that seller or supplier”.339 

(e) 

Liability of professionals vis-à-vis private individuals 

There  is  a  general  tendency  towards  applying  stricter  rules  to  liability  waivers  invoked  by 
professionals  vis-à-vis  consumers.  Some  legal  systems  consider  certain  waivers  as  unfair  or 
abusive specifically in a B2C situation.340 

(f) 

Dangerous activities 

There is also a  general tendency to apply stricter rules to liability waivers in cases of dangerous 
activities,  e.g.  in  Poland.  In  Germany,  liability  waivers  can  often  not  extend  to  cases  of  strict 
liability.341 

(g) 

Interpretation of contractual liability waivers 

Usually, waivers focus on contractual liability. Whether a waiver also extents to tortious liability 
is  a  matter  of  interpretation  of  the  contract.  In  this  regard,  MS  approach  the  interpretation  of 
contractual liability waivers differently. Some legal systems such as Greece generally assume that 
waivers  are  intended  to  cover  both  contractual  and  tortious  liability.  Other  jurisdictions  such  as 

336 Ibid., p. 783. 
337 Council Directive 93/13/EEC of 5 April 1993 on unfair terms in consumer contracts, Annex, No. 1 lit. a. 
338  Van  Bar/Drobnig,  Study  on  Property  Law  and  Non-contractual  Liability  Law  as  they  relate  to  Contract  Law,  p. 
167. 
339 Council Directive 93/13/EEC of 5 April 1993 on unfair terms in consumer contracts, Annex, No. 1 lit. a. 
340  Martin-Casals,  Comparative  Report,  in:  The  Borderlines  of  Tort  Law:  Interactions  with  Contract  Law,  London 
2019, p. 782 et seq. 
341  Van  Bar/Drobnig,  Study  on  Property  Law  and  Non-contractual  Liability  Law  as  they  relate  to  Contract  Law, 
p. 169. 

172 

 
 
                                                           
Germany  apply  a more restrictive approach and look more  closely at the  wording  of the waiver 
and the intent of the parties. 

3. 

Conclusion:  Reasons  for  not  including  provisions  on  contractual  exclusions  / 
limitations of liability in the preferred policy option on AI liability 

In  light  of  the  foregoing  analysis,  it  is  proposed  not  to  regulate  the  admissibility  of  contractual 
liability waivers in the AI liability initiative. The following arguments support this conclusion: 

First,  the  specific  characteristics  of  AI  (such  as  an  increasing  degree  of  autonomy,  complexity, 
opacity,  and a lack of transparency and predictability) do not  affect  the  admissibility of liability 
waivers.  None  of  the  differences  examined  above  is  linked  to  these  specific  characteristics.  The 
tendency to dismiss waivers in case of certain dangerous activities could not easily be applied to 
AI  systems,  because  the  risk  attached  to  such  systems  depends  on  each  individual  software 
application and the situation in which it is used. Therefore, it would not be coherent to address the 
admissibility of liability waivers under the premise of AI-specific adaptations of existing liability 
rules.  Instead,  according  to  national  legal  traditions,  this  matter  is  regulated  on  a  horizontal, 
technology-neutral basis. 

Second,  Member  States’  approaches  to  address  the  admissibility  of  liability  waivers  are  highly 
heterogeneous.  Some  jurisdictions  include  explicit  legislative  provisions;  others  rely  on 
jurisprudence  and  academic  literature.  In  some  legal  systems,  certain  sub-questions  are  still 
subject  to  debate.  Therefore,  a  regulation  of  this  matter  in  the  AI  liability  initiative  would 
represent a significant intervention into the structure of national law and legal traditions. 

Third, the national provisions and rules on the admissibility are often intertwined with other legal 
matters  and  general  principles  of  law.  Often,  the  rules  are  derived  from  explicit  provision  that 
regulate  the  admissibility  of  waivers  in  the  context  of  contractual  agreements.  Sometimes,  the 
issue  of  liability  waivers  even  relates  to  questions  of  fundamental  rights  and  ordre  public. 
Therefore, a regulation of this matter could interfere with general and even fundamental principles 
of law within the respective jurisdiction. 

Fourth, although the legal systems apply different provisions and rules, as explained in the second 
argument, the result is often similar. In all legal systems, a party can only limit its liability for a 
number of cases, often only for property damages caused by ordinary negligence. Even if the legal 
provision or the rules established by jurisprudence and academic literature differ, the results will 
frequently  be  the  same.  Injured  parties  would  not  be  left  unprotected  even  if  an  AI-specific 
instrument would not harmonise the matter at an EU level. Therefore, there is no urgent need to 
align the national approaches through harmonised provisions. 

Fifth,  the  preferred  policy  option  does  not  provide  for  a  harmonisation  of  the  substantive 
conditions of liability, or introduce a harmonised claim for compensation. The envisaged measures 
are limited to targeted adaptations regarding the burden of proof. Differently from the PLD, there 
are hence not  claims arising from  the AI liability  initiative, the exclusion or limitation  of which 
would  need  to  be  regulated.  Rather,  the  preferred  option  comes  in  where  the  victim  can  in 

173 

 
 
 
principle  invoke  an  extra-contractual  liability  claim  in  accordance  with  the  general  national 
liability rules. 

174 

 
 
 
 
Annex 10  

Detailed explanations and results regarding the assessment and 
comparison of policy options (multi-criteria analysis)  

This  Annex provides  further detailed  explanations on the assessment of policy options and their 
comparison  by  means  of  a  multi-criteria  analysis.  It  complements  Sections  6  and  7  of  the  main 
impact assessment report. 

A.  Mapping of impacts and criteria for effectiveness, efficiency, coherence and 

proportionality 

The  following  tables  provide  an  overview  of  the  criteria  used  to  assess  the  policy  options  with 
respect  to  the  main  impact  assessment  categories  (effectiveness,  efficiency,  coherence  and 
proportionality). In line with the internal market objective of the initiative, the impacts are for the 
most part economic in  nature. However, the initiative would also  have social impacts,  primarily 
connected to the compensation of victims and the fact that effective liability rules can contribute to 
improved safety of AI-enabled products and services, by providing an incentive to prevent harm. 
The choice of significant impacts retained for deeper assessment is based on stakeholders’ views 
and the objective to usefully inform political decision-making.  

1. 

Criteria for assessing effectiveness 

First specific objective (increase legal certainty and address associated internal market obstacles) 

Stakeholder group 
Potentially liable companies, in 
particular companies operating AI-
enabled products or providing AI-
enabled services (differentiating by 
company size) 
Insurance companies 

Success criteria 

Data sources 

- Increased level of legal certainty, leading to  
improved conditions for (in particular cross-border) 
business activities/investments (reduced costs,  
more investment and financing security etc.) 
 qualitative assessment 
- Increased level of legal certainty, leading to  
improved conditions for offering insurance  
coverage, in particular for cross-border activities; 
emergence of new market opportunities for 
insurance companies. 
 qualitative assessment 

- economic analysis by supporting studies 
- stakeholder feedback (public consultation, 
bilateral, webinars, supporting studies) 

- economic analysis in supporting studies 
- stakeholder feedback (public consultation, 
bilateral, webinars, supporting studies) 

Second specific objective (prevent fragmentation and associated internal market obstacles) 

Stakeholder group 
Potentially liable companies, in 
particular companies operating AI-
enabled products or providing AI-
enabled services (differentiating by 
company size) 

Insurance companies 

Success criteria 

Data source 

- no emergence of fragmented liability rules for AI  
at MS level  improved conditions for  
cross-border business activities / investments  
(reduced costs, more investment and financing 
security,...) 
 qualitative assessment 
- Improvement of the conditions for offering 
insurance coverage, in particular for cross-border 
activities; emergence of new market opportunities  
for insurance companies. 
 qualitative assessment 

- economic analysis by supporting studies 
- stakeholder feedback (public consultation, 
bilateral, webinars, supporting studies) 

- economic analysis by supporting studies 
- stakeholder feedback (public consultation, 
bilateral, webinars, supporting studies) 

Third specific objective (ensure that victims suffering harm caused by AI have the same level of protection as 
victims suffering harm caused by other technologies) 

Stakeholder group 

Success criteria 

Data source 

175 

 
 
 
 
 
Victims of damage caused by AI 
(citizens, consumers, companies) 

Victims of damage caused by AI 
(citizens, consumers, companies) 

Companies in the European  
AI-sector (differentiating by  
company size) 

- The effectiveness of liability claims is not  
diminished by the specific characteristics of AI  
(opacity, autonomy, complexity, lack of  
predictability, etc.) 
 qualitative assessment 
- Liability rules provide an effective incentive to 
potentially liable persons (in particular users and 
providers of AI systems) to prevent harm 
Increase in the level of societal trust and consumers’ 
willingness to take up AI-technologies.  

- supporting studies 
- Qualitative assessment by Commission 
services 

- supporting studies 
- Qualitative assessment by Commission 
services 
- Supporting studies (in particular  
behavioural analysis) 
- stakeholder feedback (public  
consultation, bilateral, webinars,  
supporting studies) 

2. 

Criteria for assessing efficiency 

2.1. 

Preliminary methodological clarifications on costs and benefits 

2.1.1. Quantification challenges 

Regarding the assessment of costs and benefits, the future-oriented nature as well as the specific 
subject-matter  of  this  initiative  impose  a  number  of  methodological  limitations.  As  already 
explained in Annex 4 (analytical methods) with respect to the supporting studies, quantified data 
on costs and benefits was in many cases not available because: 

-  Products  and  services  that  are,  first,  powered  by  AI  systems  with  the  specific 
characteristics  challenging  liability  rules  (high  degree  of  autonomy,  opacity,  complexity, 
etc.), and second, capable of causing damage giving rise to civil liability claims, are for the 
most  part  not  yet  rolled-out  on  the  market.  No  data  on  the  prices,  margins,  specific 
characteristics, etc. of such products and services are yet available as a basis for measuring 
the impacts of adapting civil liability rules. 

-  There is no sufficiently robust basis (yet) for estimating the damage that may be caused by 
AI-enabled products and services, as there are no relevant statistics on cases of damage or 
compensation  yet.  It  is  therefore not  possible either to  quantify the cost  of compensation 
(i.e. the cost of repairing damage caused) that might be re-allocated from the victim to the 
liable party due to the policy measures. 

-  Stakeholders were generally not in a position to provide quantified estimates of the impacts 
of  either  the  identified  problems  under  the  baseline  scenario  or  the  policy  options. 
Although a number of business stakeholders confirmed that they do see legal uncertainty 
and  future  fragmentation  regarding  liability  for  AI  as  a  challenge,  it  was  too  early  to 
quantify the impact of these problems on their cost structure. 

2.1.2. Mitigating actions 

Various actions were implemented in an effort to address the scarcity of quantified data: 

(a)  The consultant tasked with economic analysis (Deloitte) took a number of measures to obtain 
additional stakeholder feedback (wider outreach, prolongation of consultation activities, semi-
structured interviews to complement the surveys) and carried out additional research to draw 
conclusions based on available literature, studies and economic theory. While these measures 
strengthened  the  qualitative  economic  analysis  and  allowed  for  relevant  conclusions  to  be 
drawn, they did not allow a sufficiently stable quantification of specific costs and benefits. 

176 

 
 
 
(b) In  close  collaboration  with  better  regulation  experts  of  DG  JUST  and  experts  of  the  JRC, 
several approaches to obtaining quantified estimates were undertaken in order to complete the 
– largely qualitative – data delivered by the supporting studies: 

-  A  use-case  based  modelling  of  the  costs  of  insurance  covering  the  liability  risks  linked 
with  the  use  of  relevant  AI-enabled  products  or  the  provision  of  relevant  AI-enabled 
services was explored. Despite reaching out to several insurance companies and attempting 
to  procure  relevant  data  from  other available sources  (databases, surveys, etc.), it proved 
impossible  to  obtain  sufficient  data  on  insurance  products  covering  this  type  of  liability 
risks.  While  input  from  insurance  companies  confirmed  that  they  are  working  on 
developing  AI-specific  insurance  products  or  covering  AI-related  liability  risks  through 
existing ‘all-risk’ policies, they were not able/willing to provide datasets that would have 
allowed a modelling of insurance costs for the purposes of this IA. 

-  A quantification of the economic impact of legal fragmentation regarding liability for AI 
was  pursued  based  on  a  macro  approach,  to  show  namely  the  loss  in  trade  due  to  legal 
fragmentation. The approach consisted in estimating the impact that discrepancies between 
MS’ national liability rules applicable to AI have an intra-EU cross-border trade. For the 
purposes of these estimations, a gravity model of trade was explored in which, in addition 
to  traditional factors explaining trade between two countries (GDP,  cost  of trade, etc.), a 
‘legislation distance’ score would have been added. However, the available information on 
MS’ intentions regarding  future legislative measures on AI liability  proved insufficiently 
detailed  to  implement  this  approach.  While  several  MS  envisage  such  measures  in  their 
national  AI  strategies,  no  information  which  was  sufficiently  stable  and  detailed  for 
modelling  purposes  could  be  obtained.  Input  from  MS  was  sought  both  in  bilateral 
exchanges  and  in  a  Workshop  on  the  topic  of  Liability  for  AI,  but  these  efforts  did  not 
yield  sufficiently  comprehensive  and  detailed 
to  support  a  robust 
quantification of the economic impact of legal fragmentation. 
In  addition,  in  order  to  illustrate  the  economic  impact  of  the  envisaged  policy  measures 
designed  to  address  the  specific  proof-related  problems  in  AI-related  claims,  a  micro-
economic approach was  pursued by the digital  economy team of the JRC.  This approach 
consisted  in  assessing  the  variations  in  the  supply  curve  (legal  uncertainty  affecting 
businesses)  and  the  demand  curve  (lack  of  consumer  trust  affecting  consumer  demand), 
building on a dataset for robotic vacuum cleaners.  

information 

- 

2.1.3.  Available quantified estimates 

The quantification efforts have yielded the following results (see Annex 4 and the economic study 
for further details): 

(a) Production  value  affected  by  internal  market  obstacles  linked  to  legal  uncertainty  and 

legal fragmentation regarding liability for damage caused by AI (use-cases) 

For  the  baseline,  the  EU27  intra-trade  production  value  potentially  affected  by  internal  market 
obstacles  linked  to  legal  uncertainty  and  legal  fragmentation  was  estimated  for  the  markets 

177 

 
 
represented  by  six  relevant  use-cases342.  These  estimated  shares  amount  to  EUR 829  million  in 
2021 and ca. EUR 3 billion in 2029. However, due to a lack of precise information regarding the 
degree  of  perceived  legal  uncertainty  and  the  extent  to  which  national  measures  would  increase 
legal fragmentation under the baseline scenario, it was not possible to develop a robust estimate of 
the extent to which those production values would be reduced due to liability-related problems: 

Source: Deloitte analysis 

(b) Overall market value affected by liability-related internal market obstacles 

Still regarding the baseline scenario, beyond the six use-cases, AI market shares affected by legal 
uncertainty  and/or  legal  fragmentation  regarding  liability  for  AI  were  estimated  by  economic 
sector.  These  estimated  shares  range  from  EUR 1,119  to  EUR 3,459  billion  in  2020,  and  from 
EUR 10,204 to EUR 21,342 billion in 2025: 

342  AI-enabled  autonomous  vehicles,  autonomous  drones/delivery  robots,  AI-enabled  road  traffic  management 
systems,  AI-enabled  warehouse 
services,  AI-enabled  automated 
robot,  AI-enabled  medical-diagnosis 
lawnmowers/vacuum cleaners 

178 

EU27 production value [in €m per year]AI enabled products20212029202120291. AI enabled autonomous vehicles4                         2 618                   1                         679                      2. AI-enabled autonomous drones2 879                   5 151                   555                      993                      3. AI-enabled traffic management systems262                      1 002                   29                        109                      4. AI-enabled industrial robots233                      3 244                   12                        168                      5. AI-enabled medical diagnosis devices586                      2 784                   210                      997                      6. AI-enabled lawnmowers191                      357                      23                        43                        Total4 155                   15 157                 829                      2 989                   TotalPotential liability obstacles 
 
 
 
 
 
                                                           
Source: Deloitte analysis 

However, it has to be acknowledged that these estimates do not as such represent a quantification 
of  the  relevant  problems  because  they  do  not  express  the  extent  to  which  the  affected  market 
shares  will  be  reduced  due  to  legal  uncertainty  and  fragmentation  regarding  liability.  It  was  not 
possible to develop a robust estimate of this metric, due to the uncertain factors mentioned under 
the previous point (a). 

(c) 

Costs of claiming compensation linked to the specific challenges of AI: estimates by 
experts in ICT and liability law 

In the framework of the supporting economic study (Deloitte), legal experts estimated the costs for 
legal and technical experts needed to claim compensation, based on the existing national liability 
rules in 13 representative legal systems, when AI is involved in causing harm compared to cases 
not involving AI.343 Certain damage scenarios involving a range of AI use cases (autonomous AI-
enabled  motor  vehicles,  drones,  traffic  management  systems,  warehouse  robots,  post-surgical 
infection  analysis  devices,  robotic  lawnmowers  and  fire  detection  systems)  or,  respectively,  the 
corresponding (i.e. functionally equivalent) ‘non-AI’ technologies  were posited for this purpose. 
The following tables show the estimated costs of technical expertise, which are the most relevant 
cost factor for assessing the AI-specific challenges of claiming compensation and the impacts of 
policy options designed to address those challenges344: 

-  Data on costs for technical experts in AI-related cases (for all AI applications covered) 

Costs for technical experts (total EUR) 

AT 

BE 

15 000 – 40 000 

>1 000 – 10 000 

343 For details see Economic Study, pp. 48 et seq. and Annex B. 
344 Legal experts provided more granular use-case specific estimates for the damage scenarios involving ‘traditional’ 
use cases, whereas only global estimates could be provided for cases involving AI. 

179 

 
 
 
 
 
                                                           
BG 

DE 

DK 

ES 

FR 

IE 

IT 

PL 

PT 

RO 

SK 

UK 

408 – 2 040 

>500 – 1 800 

10 000 

4 000 – 6 000 

15 000 – 75 000 

4 000 – 6 000 

10 000 – 20 000 

4 500 – 5 000 

1 000 – 10 000 

2 000 – 10 000 

400 

12 000 – 36 000 

- 
- 

Source: Survey completed by legal experts for the supporting economic study (Deloitte) 
Costs of technical experts for traditional liability cases (not involving AI) 

Country  Motor 

vehicles 

Remotely 
piloted 
drones 

Traffic 
manageme
nt systems 

Industrial 
appliances 

Medical 
devices 

Lawnmow
ers 

Fire detection 
systems 

AT 

BE 

BG 

DE 

DK 

ES 

FR 

IE 

IT 

PL 

PT 

RO 

SK 

5 000 – 
15 000 

5 000 – 15 
000 

15 000 – 20 
000 

15 000 – 20 
000 

15 000 – 20 
000 

5 000 – 15 
000 

5 000 – 15 000 

1 000 

5 000 

2 000 

3 000 

10 000 

1 000 

10 000 

204 – 1 
020 

500 – 1 
800 

204 – 1 020  204 – 1 020  204 – 1 020  204 – 1 020  204 – 1 020  204 – 1 020 

500 – 1 800  500 – 1 800  500 – 1 800  500 – 1 800  500 – 1 800  500 – 1 800 

2 000 

2 000 

10 000 

4 000 

4 000 

4 000 

10 000 

1 000 – 3 
000 

2 500 – 5 
000 

1 000 – 3 
000 

2 000 – 3 
500 

1 000 – 3 
000 

1 000 – 3 
000 

1 000 – 3 000 

10 000 – 
50 000 

10 000 – 50 
000 

10 000 – 50 
000 

10 000 – 50 
000 

10 000 – 50 
000 

10 000 – 50 
000 

10 000 – 50 000 

1 000 – 2 
000 

3 000 

2 000 – 3 
000 

1 000 – 2 
000 

2 000 – 4 
000 

1 000 – 2 
000 

1 000 – 2 000 

5 000 – 
15 000 

5 000 – 15 
000 

5 000 – 15 
000 

5 000 – 15 
000 

5 000 – 15 
000 

5 000 – 15 
000 

5 000 – 15 000 

800 

1 000 

1 100 

300 

1 200 

1 000 

850 

800  

800 

2 000 

1 000 

3 000 

400 

2 000 

600 – 800   600 – 1 200  600 – 800   500 – 700   800 – 1 500  600 – 800   600 – 800  

300 

300 

300 

300 

300 

300 

300 

Source: Survey completed by legal expert for the supporting economic study (Deloitte) 

180 

 
 
The following differences between the respective costs of technical  expertise to  be advanced by 
victims  provide  an  idea  of  the  scale  of  the  challenges  faced  by  victims  due  to  the  specific 
characteristics of AI345: 

Difference between costs of technical expertise needed in AI-related cases compared to ‘traditional’ cases 

Costs for technical 
experts 

+20% / +113%  

difference to be 
determined 

+100% 

difference to be 
determined 

+100% 

+190% / +80% 

+50% 

+30% / +20% 

+100% / +25% 

+430% / +490% 

+0% / +600% 

+226% / +820% 

+33% 

AT 

BE 

BG 

DE 

DK 

ES 

FR 

IE 

IT 

PL 

PT 

RO 

SK 

Source: Deloitte 

(d)  Quantified estimates of the impacts of policy options on the costs linked to the burden 

of proof borne respectively by victims and liable parties 

The estimates summarised under the previous point (c) were used to approximate, in a first step, 
the cost linked to the burden of proof under current liability rules, due to the specific challenges of 
AI. The additional costs of technical expertise in cases involving AI compared to other cases are 
suitable as a proxy for this quantification, because they reflect the difficulty of attributing liability 
given  the  specific  opacity/lack  of  transparency,  behavioural  autonomy,  complexity  and  limited 
predictability,  etc.  of  certain  AI  systems.346  On  that  basis,  quantified  estimated  were  generated 
regarding, on the one hand, the cost savings that each policy option can bring for victims, and on 
the  other  hand,  the  possible  increase  of  costs  linked  to  the  burden  of  proof  for  the  liable  party. 
More specifically, the following steps were implemented for this quantification: 

345  In  order  to  enable  this  comparison,  an  average  of  all  the  data  on  non-AI  cases  per  country  was  calculated.  The 
respective first values in the following table refer to the difference between the minimum fees (non-AI vs. AI) and the 
second one refers to the difference between the maximum fees. 
346 For detailed explanations given by the experts for their estimates, see Economic Study, pp. 52 et seq. 

181 

 
 
 
 
                                                           
(i)  Firstly, the difference was calculated between, on the one hand, the average estimated costs to 
be  advanced  by  victims  for  technical  expertise  in  cases  where  AI  was  involved  in  causing 
damage, and on the other hand, the same average in cases not involving AI.347 Based on the 
estimates  by  legal  experts  shown  in  the  tables  above,  the  average  difference  amounts  to 
EUR 4149. 

(ii) Secondly, reasoned assumptions were made regarding the extent (expressed as a percentage) to 
which each policy option will alleviate victims’ costs linked to the burden of proof (see point 
3.2. below for details). The estimated percentage was applied to the average AI-specific cost of 
technical expertise, to obtain a quantified estimate of this benefit. 

(iii)Thirdly,  further  reasoned  assumptions  were  made  regarding  the  extent  (expressed  as  a 
percentage)  to  which  each  policy  option  would  lead  to  a  transfer  of  the  cost  linked  to  the 
burden  of  proof  to  the  defendant  (i.e.  the  allegedly  liable  party)  (see  point  3.2.  below  for 
details). The estimated percentage was applied to the average quantified reduction of costs to 
be  advanced  by  victims,  to  obtain  a  quantified  estimate  also  of  the  ‘burden  of  proof  costs’ 
attached to the policy options for liable parties.348  

These  estimates  should  not  be  misconstrued  as  a  quantification  of  the  problem  that  the  specific 
characteristics of AI can make it prohibitively difficult, or even impossible, to meet the burden of 
proof.  In  particular,  they  do  not  take  into  account  the  cases  in  which  liability  claims  would  not 
pursued in the first place based on current liability rules, because the victim either cannot identify 
the liable party or considers the prospect of a successful claim insufficient to justify legal action. 
The  preferred  policy  option  will  help  victims  also  in  the  latter  cases,  by  overcoming  the 
compensation  gaps  induced  by  the  specific  characteristics  of  AI.  This  benefit  is  reflected  in  the 
previous row (‘reduced AI induced compensation gaps’). 

(e) 

Quantified estimates of the impact of policy options on the EU AI market value 

Regarding the impacts of policy options, the economic study delivered estimates, for the markets 
represented  by  the  six  use-cases  (see  point  2.1.3.(a)  above),  of  the  impact  that  the  envisaged 
measures would have on the cross-border trade affected. Policy options involving a combination 
of  measures  to  ease  the  burden  of  proof  and  a  harmonisation  of  strict  liability  for  certain  AI 
applications (like PO2 and PO3) were estimated to increase the production value of the affected 
cross-border trade by 5-7%, leading to the following impact estimates net of the baseline: 

347 Where high and low estimates were submitted for a certain legal system, the average of the difference between the 
respective high and low estimates was calculated. 
348  The  policy  options  are  not  expected  to  entail  additional  litigation  costs  for  private  persons  as  potentially  liable 
parties. These stakeholders are likely to defend themselves against liability claims using the same type of arguments 
and  evidence  as  under  the  existing  burden  of  proof  rules.  For  example,  they  might  seek  to  avoid  liability  by 
demonstrating that they acted diligently and in accordance with the instructions of use accompanying an AI-enabled 
product.  Contrary  to  potentially  liable  businesses,  which  may  have  special  knowledge  and  be  subject  to  certain 
requirements regarding the functioning and ‘inner workings’ of an AI system (in particular under the AI Act), private 
persons  would  not  have  to  base  their  defense  on  an  analysis  of  the  functioning  of  such  a  system.  The  envisaged 
alleviation  of  victims’  burden  of  proof  regarding  the  ‘inner  workings’  of  AI  systems  is  therefore  not  expected  to 
prompt potentially liable private persons to commission technical expertise. 

182 

 
 
                                                           
Source: Deloitte estimation 
Note:  In  this  figure,  Option  1  represents  a  non-binding  instrument,  Options  2  and  3 
represent different combinations of alleviations of the burden of proof with strict liability, 
and  Option  4  represents  the  discarded  policy  option  applying  strict  liability  to  all  AI 
systems that challenge the current liability rules. 

On the basis of the estimated incremental impact on AI market values and the estimates regarding 
the overall AI market affected by legal uncertainty and fragmentation under the baseline scenario, 
the policy options are expected to deliver an increase of the AI market value in the EU of between 
ca.  EUR 500mln  and  ca.  EUR 1.1bln.  These  values  are  obtained  by  multiplying  the  estimated 
shares of the AI market affected by legal uncertainty and fragmentation regarding civil liability in 
2025  under  the  baseline  scenario  (low  and  high  scenarios  assumed  by  the  economic  study 
supporting this  IA) with the estimated impact  of the policy options (+5%). This  percentage  was 
determined conservatively, taking into account the estimated impact  generated by a combination 
of measures to ease the burden of proof with a harmonisation of strict liability limited to certain AI 
applications  (cf.  Economic  Study,  pp.  195  et  seq.).  In  the  supporting  study,  policy  options 
including  these  elements  were  estimated  to  increase  the  production  value  of  the  affected  cross-
border by 5-7 %, for the six use-cases analysed specifically by that study (AI-enabled autonomous 
vehicles,  autonomous  drones/delivery  robots,  AI-enabled  road  traffic  management  systems,  AI-
enabled  warehouse  robot,  AI-enabled  medical-diagnosis  services,  AI-enabled  automated 
lawnmowers/vacuum  cleaners).  In  order  to  quantify  the  overall  economic  benefits  generated  by 
the policy options (not limited to the six use-cases), a conservative extrapolation of this estimate 
was  applied  to  the  relevant  market  shares  of  all  sectors  affected  by  legal  uncertainty  and 
fragmentation, taking into account  also  that the preferred PO does not  include the strict  liability 
element  assumed  for  the  supporting  study  with  respect  to  a  small  number  of  specific  AI 
applications. 

For  illustration  purposes,  the  Joint  Research  Centre  has  provided  a  complementary  micro-
economic quantification of the envisaged measures to ease the victim’s burden of proof, based on 
the use-case of robotic vacuum cleaners. This analysis reaches the conclusion that these measures 

183 

 
 
 
would generate an increase in consumer welfare of EUR 11.5-19.12mln and in total welfare349 of 
EUR 30.11-53.74mln for this product category alone in the EU-27.350 

(f) 

Quantified estimates of the incremental changes in insurance premiums that might be 
caused by the policy options  

Quantified estimates of the incremental  changes in insurance premiums that might be caused by 
the policy options were generated based on: 

- 

- 

- 

available information on annual premiums paid for general liability insurance (EUR 42bn 
in 2019)351; 
input from insurance stakeholders to the effect that AI-related liability risks can largely be 
covered by existing general liability insurance policies352; 
estimates  of  the  extent  to  which  policy  options  could  shift  the  burden  of  compensating 
damage caused by AI from the victim to the party responsible for that damage.353 

The following steps were followed to obtain these quantified estimates: 

(i)  In  a  first  step,  the  EU  AI  market  size  (low  estimate  =  EUR 3.473bn;  high  estimate  = 
EUR 10.737bn in 2020354) was divided by the overall market value of the EU economy (ca. 
14 trillion355). 

(ii)  In  a  second  step,  the  percentages  thus  obtained  (0.002%  and  0,007%)  were  used  to 
approximate  the  share  of  liability  insurance  premiums  linked  to  AI-related  economic 
activities. This step is based on the considerations that premiums not linked to the AI market 
are  not  affected  by  the  AI  liability  initiative  in  the  first  place.  Given  the  overall  annual 
premiums paid for general liability insurance, these shares amount to EUR 10.67mln (based 

in 

data, 

2019 

p. 48, 

Europe, 

Figures, 

European 

Insurance 

349  Sum  of  vacuum  cleaners  consumer  surplus,  vacuum  cleaner  sellers‘  profits  and  third  party  victims  receiving  a 
compensation). 
350 See Annex 11 for details on the methodology and results of this quantification effort. In terms of methodological 
limitations, it is important to acknowledge that this quantification approach focused on civil liability rules outside the 
scope of the PLD, in line with the scope of the AI liability initiative.  
351 
Insurance 
https://www.insuranceeurope.eu/publications/689/european-insurance-in-figures-2019-data/download/EIF+2021.pdf. 
The latest available data on the overall annual premiums paid for general liability insurance is from 2019 and includes 
the  UK  insurance  market.  Nevertheless,  it  provides  a  suitable  basis  for  approximating  the  possible  increase  of 
insurance premiums due to the policy options. 
352 See e.g. Insurance Europe’s reply to the public consultation: “…insurance can lessen the negative consequences of 
accidents  involving  AI  by  ensuring  that  the  victim  receives  compensation.  There  are  already  many  such  insurance 
solutions available in the European insurance market. Protection against material damage incurred by AI generally 
falls  within  the  remit  of  general  liability  insurance  policies,  which  are  sold  on  an  all-risks  basis.”  Similarly  the 
German Insurance Association (GDV) submitted that “the voluntary insurance market works well in providing actors 
of  all  kinds  with  appropriate  liability  insurance.”  Likewise,  a  leading  insurance  company  explained  in  a  bilateral 
exchange  with  the  Commission  services  that  AI  liability  will  be  likely  be  incorporated  as  additional  feature  into 
existing insurance policies, and that the increasing integration of AI in business activities is not expected to lead to an 
increase of premiums on a general level. 
353 In this respect, see clarifications regarding the ‘cost of compensation’ below under this heading. 
354 Cf. Economic Study, p. 100.  
355 While the EU economy was slightly smaller in 2020 due to the exceptional economic shock caused by the Covid-
19  pandemic  (cf.  Statista,  https://www.statista.com/statistics/279447/gross-domestic-product-gdp-in-the-european-
union-eu/),  it  is  appropriate,  for  the  purposes  of  assessing  the  relevant  market  shares,  to  assume  a  more  regular 
development of the EU economy. 

184 

 
 
 
                                                           
on the low estimate of the AI market size) or EUR 32.21mln (based on the high estimate of 
the AI market size). 

(iii) Economic analysis and stakeholder feedback indicate that, during an initial transitional period, 
the  scarcity  of  relevant  actuarial  data  on  AI  liability  risks  will  make  it  more  difficult  for 
insurers to calculate premiums compared to insured activities not involving AI. In a third step, 
the  shares  of  annual  premiums  linked  to  AI  were  therefore  multiplied  by  two,  to  take  into 
account the initial need for insurers to allow for sufficient risk margins with respect to AI. The 
shares thus  adjusted  amount  to  EUR 21.34mln (based on the low estimate of the AI market 
size) and EUR 64.42mln (based on the high estimate of the AI market size). This step ensures 
that  the  final  cost  estimate  are  conservative  even  during  an  initial  stage  of  scarce  actuarial 
data.  As  more  data  becomes  available  with  the  increasing  market  rollout  of  AI-enabled 
products  and  services,  the  need  to  allow  for  added  uncertainty-induced  risk-margins  will 
likely dissipate quickly. 

(iv) In a fourth step, the share of general liability insurance premiums on which the policy options 
can have an impact was further narrowed down, by excluding the share of premiums that can 
be  allocated  to  insured  events  to  which  these  policy  options  would  not  apply.  This  step 
involves estimating the share of insured events that are either devoid of factual uncertainty or 
subject to strict liability under the baseline scenario. In such cases, the AI-specific problems 
to  be  addressed  by  the  AI  liability  initiative  do  not  materialise.  The  policy  options  are 
designed to apply  only  where those AI-specific  problems  arise, and only in  those cases  can 
these  options  hence  have  an  impact  on  insurance  premiums  compared  to  the  baseline.  It  is 
estimated that the policy options are relevant for one third of the adjusted liability insurance 
premiums linked to AI, taking into account the following: 

-  Some  MS  have  broad  strict  liability  regimes  covering  damage  caused  by  ‘dangerous’ 

activities or things, or even more broadly any damage caused by things. 

-  The  potential  to  cause  damage  varies  depending  on  the  purpose,  context  of  use  and 
operating  mode  of  AI  systems.  Not  all  AI-related  economic  activities  are  involve  a 
significant  liability risk,  and only AI systems  with certain  specific characteristics (highly 
autonomous  behaviour,  opacity,  low  predictability,  complexity,  etc.)  pose  particular 
challenges in terms of allocating liability.356 

On  this  basis,  the  shares  of  the  premiums  paid  for  general  liability  insurance  on  which  the 
policy options can have an impact amount to EUR 7.11mln (based on the low estimate of the 
AI market size) or EUR 21.47mln (based on the high estimate of AI market size). 

(v) In a fifth step, the incremental effect of each policy option on the determined relevant shares of 
insurance  premiums  is  estimated  in  terms  of  percentages.  For  the  percentages  assumed 
respectively, see below under point 3.2. (Efficiency). 

(vi) Finally, in a sixth step, a multiplier is applied to the changes in insurance premiums, to obtain 
estimated  impacts  for  2025.  In  light  of  the  following  factors,  it  is  appropriate  to  multiply  the 

356 Cf. in this respect the supporting economic study for reasoned estimations of market shares affected by liability-
related problems, Economic Study, pp. 104 et seq. 

185 

 
 
                                                           
changes in insurance premiums linked to each policy option by five to approximate the impact 
of the policy options in 2025: 

-  Until 2025, the EU AI market is expected to grow six to ten-fold357, whereas for the overall 
EU  economy  will  likely  see  moderate  growth  rates  around  two  percent  in  2023-2025 
following post-Covid-19 catch-up effects with faster growth in 2022.358 

-  As more and more AI-enabled products and services are rolled out over the coming years, 
the difficulties  linked to the lack of actuarial data are expected to  diminish.  Insurers will 
thus  be  able  to  estimate  liability  risk  with  increasing  precision,  and  calculate  premiums 
reflecting the real risk exposure. 

2.1.3.  Consequences for the assessment of efficiency 

In light of the scarcity of quantified data or estimates, the costs and benefits attached to the policy 
options  are  largely  assessed  qualitatively,  in  terms  of  trends,  taking  into  account  all  available 
information.  On  this  basis,  the  policy  options  are  compared  using  qualitative  scales  rather  than 
quantified  impact  metrics.  The  quantified  estimates  available  are  taken  into  account  in  that 
framework. 

2.1.4.  Cost of compensation: 

As regards the cost of compensation (i.e. the cost of repairing damage caused), it is assumed that 
damage and compensation offset one another in absolute economic terms, the rationale of liability 
rules  being  to  “make  the  victim  whole”.  In  line  with  the  Commission’s  political  objective  to 
ensure that victims of damage caused with the involvement of AI systems have the same level of 
protection  as  victims  of  damage  caused  by  other  technologies  (specific  objective  3),  the  policy 
options  necessarily  involve  a  certain  re-allocation  of  the  costs  of  compensation.  This  effect  is 
intended  to  materialise  namely  to  the  extent  that  the  specific  characteristics  of  AI  would  have 
caused a justified claim to fail under the baseline scenario, because of the difficulty of proving the 
conditions  of liability under the current  rules. As this effect  is  in  line with the policy objectives 
and also  achieves  an efficient cost-allocation (to  the person best  placed to prevent  damage  from 
occurring),  it  is  not  regarded  as  an  undesired  impact  or  undue  burden  for  the  purposes  of  this 
impact  analysis.  The  impact  of  the  policy  options  on  the  effectiveness  of  liability  claims  is 
nevertheless  relevant  for  assessing  the  equitable  distribution  of  costs  and  benefits  between 
stakeholders,  which  is  a  separate  important  criterion  for  assessing  efficiency.  The  ‘cost  of 
compensation’ is therefore mentioned in the efficiency assessment as a category apart, specifically 
to reflect the described redistribution effect of the policy options.  

2.1.5.  Environmental benefits: 

In terms of methodological limitations, it is furthermore acknowledged that there is no sufficient 
basis  for  assessing  possible  indirect  environmental  benefits  that  might  be  achieved  through  an 
increased  uptake  of  AI.  On  a  general  level,  it  is  expected  that  AI-solutions  can  generate 
efficiencies and contribute to the innovation of environmentally friendly technologies. However, 

357 Ibid., p. 32. 
358 See Statista, https://www.statista.com/statistics/267898/gross-domestic-product-gdp-growth-in-eu-and-euro-area/  

186 

 
 
 
                                                           
these effects are too far removed from the envisaged adaptations of liability rules to assess them 
even approximatively in terms of impacts of those measures. 

2.2.  Overview of the relevant types of costs and benefits 

The following tables provide an overview of the relevant types of costs and benefits considered for 
the assessment of the policy options as to their efficiency: 

Direct costs 

Type of costs 

Substantive compliance costs, e.g. for 
mandatory insurance coverage 
 Type of impact: Economic  

Indirect costs  Costs of legal uncertainty and 

fragmentation – to the extent that a policy 
option fails to address the problems 
identified under the baseline, the 
associated costs would also persist (e.g. 
transaction, compliance, familiarisation, 
information costs, higher insurance costs, 
loss of business opportunities, increased 
cost of capital) 
 Type of impact: Economic 
- Litigation: Increased costs for 
substantiating liability claims persisting 
to the extent that a policy options fails to 
address the AI-specific difficulties to 
meet the burden of proof 
- Litigation: costs linked to meeting the 
burden of proof 

- Adjudication: Increased costs on the 
justice systems due to an increased 
number of civil actions 
 Type of impact: Economic 
Redistribution of costs of compensation 
and / or partial redistribution of the costs 
linked to the burden of proof from the 
victim to the liable party 
 Type of impact: Economic 

Regulatory costs 

Stakeholders affected 

Data source 

- Potentially liable companies 
(differentiating according to size), 
in particular companies 
using/operating AI-enabled 
products or providing AI-enabled 
services falling under a harmonised 
strict liability regime 

- Potentially liable companies 
(differentiating according to size), 
in particular companies 
using/operating AI-enabled 
products or providing AI-enabled 
services, in particular cross-border. 
- Insurance companies 

- economic analysis by 
supporting studies 
- stakeholder feedback (public 
consultation, bilateral, 
webinars, supporting studies) 
- Quantified estimate of 
incremental changes of 
insurance premiums; otherwise 
qualitative assessment. 
- economic analysis by 
supporting studies 
- stakeholder feedback (public 
consultation, bilateral, 
webinars, supporting studies) 

- Litigation: Victims of damage 
caused by AI-applications, 
including companies, public 
entities and private individuals 

- Costs linked to the burden of 
proof: potentially liable parties 

- Adjudication: judiciary, public 
budget  

- economic analysis by 
supporting studies 
- stakeholder feedback (public 
consultation, bilateral, 
webinars, supporting studies) 
- Quantified estimates of costs 
linked to the burden of proof; 
otherwise qualitative 
assessment. 

Liable parties (companies and 
private individuals) 

Qualitative assessment based 
on supporting studies and 
stakeholder feedback 

Type of benefit 

Lower costs linked to the burden of 
proof (e.g. analysis by IT experts)  
 Type of impact: Economic / 
social 

Regulatory benefits 

Stakeholders affected 
- Victims of damage caused by AI-
applications, including companies, 
consumers and citizens 

Guarantee of the right to an 
effective remedy 

- Victims of damage caused by AI-
applications, including companies, 
consumers and citizens 

Cost savings regarding the 
transaction, compliance, 

- Potentially liable companies 
(differentiating according to size), 

187 

Data source 

- economic and legal analysis by 
supporting studies 
- stakeholder feedback (public 
consultation, bilateral, webinars, 
supporting studies) 
- Quantified estimates of the 
reduction of costs to be advanced 
by victims due to the specific 
challenges of AI. 
- legal analysis by supporting 
studies 
- stakeholder feedback (public 
consultation, bilateral, webinars, 
supporting studies) 
- economic analysis by supporting 
studies 

Enforcement 
costs 

Cost of 
compensation 

Direct benefits 

 
 
 
 
 
 
 
 
 
 
 
 
familiarisation, information and 
capital costs as well as insurance 
costs caused by legal uncertainty 
and fragmentation 

in particular companies 
using/operating AI-enabled 
products or providing AI-enabled 
services, in particular cross-border. 

- stakeholder feedback (public 
consultation, bilateral, webinars, 
supporting studies) 

Market efficiency: increased 
business opportunities 
Wider economic benefits: Increased 
uptake of AI-technologies, due to 
increased societal trust and 
increased legal certainty / reduced 
legal fragmentation 

Indirect benefits 

- Insurance companies 

- Companies in the European AI-
sector; citizens 

Improved competitiveness of the 
European AI sector 
Improved safety of AI-enabled 
products and services, due to 
effective incentive to prevent harm 

- Companies in the European AI-
sector; 
- Victims of damage caused by AI-
applications, including companies, 
consumers and citizens 

- economic analysis by supporting 
studies 
- stakeholder feedback (public 
consultation, bilateral, webinars, 
supporting studies) 
- Quantified estimates of the impact 
of the policy options on the AI 
market value in the EU. 

3. 

Criteria for assessing coherence 

The coherence of the policy options is assessed taking into account the following criteria: 

- 

- 

consistency and synergetic interplay with the other initiatives forming part of the follow-up 
to  the White Paper on AI, with  particular focus  on the  complementarity  with  the AI Act 
and the PLD review; 
consistency  with  the  rationale  and  approaches  of  existing  national  liability  systems, 
enabling a friction. 

4. 

Criteria for assessing proportionality 

With respect to the proportionality criterion, it has been assessed in particular whether 

- 

- 

the measures  envisaged  under the different policy  options  are suitable limited to  achieve 
the specific objectives; 
the  measures  are  limited  to  the  minimum  intervention  necessary  to  achieve  those 
objectives, or whether less intrusive measures could be equally effective; 

-  whether the impacts of the measures are overall justified given the expected benefits. 

B.  Methodology for determining the preferred policy option: multi-criteria analysis 

The  policy  options  were  assessed  and  compared  as  to  their  effectiveness,  efficiency,  coherence 
and proportionality, differentiating between the stakeholders affected by the respective impacts. 
Given the varied nature of the criteria applied as well as the difficulty of quantifying most of the 
impacts,  a  multi-criteria  analysis  (MCA)  methodology  is  applied  for  the  purposes  of  this 
comparison.  The  respective  scores  are  subsequently  represented  in  a  matrix showing  the  overall 
result. In order to ensure the robustness and test the sensitivity of that result, different weights are 
applied. 

C. 

Comparative assessment and scoring of policy options 

188 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
The  following  sections  present  an  overview  of  the  scores  given  to  the  policy  options  for  their 
respective efficiency, effectiveness, coherence and proportionality. Explanations are provided with 
a focus on the differentiating elements. Each IA criterion is scored on a scale from -5 to +5 for the 
purposes of the comparative ranking. Different scores are given as regards the implementation of 
the respective measures by way of a binding instrument (most likely a Directive) or a non-binding 
one (recommendation). 

1. 

Effectiveness 

1.1. 

Specific objectives 1 (ensure legal certainty) and 2 (prevent fragmentation) 

(a) 

Policy Option 1 

The  targeted,  risk-based  alleviations  of  the  burden  of  proof  envisaged  under  Option  1  would 
address the major sources of legal uncertainty identified in the problem analysis, which are linked 
to  the  AI-specific  difficulties  of  meeting  the  burden  of  proof  under  fault-based  liability  rules. 
Option  1  would  do  so  in  a  harmonised  manner,  ensuring  a  consistent  minimum  level  of  AI-
specific  alleviations  of  the  burden  of  proof  at  national  level.  It  would  thereby  achieve  the  more 
general objective to improve conditions for cross-border business activities involving AI.  

Example359: A start-up based in MS A considers to provide educational services for autistic children in 
another MS (B). The company uses a social robot equipped with AI-enabled software modules that can 
autonomously interact with the children and adapt to their individual behaviour. As it is not a producer, 
the company does not fall under the  PLD. Due to the harmonised measures envisaged under PO1, the 
company nevertheless is aware that in the case of an accident, it may be required by B’s civil courts to 
prove how or why the robot’s AI-systems came to the output that caused the accident. This  knowledge 
facilitates the company’s choice between alternative technological solutions, as it provides an incentive 
to favour transparent and explainable AI-systems. Moreover, PO1 enables the company to estimate its 
liability  risks  more  accurately,  and  thus  to  develop  a  more  robust  profitability  and  cost-management 
model to obtain financing. By the same token, the increased legal certainty afforded by PO1 helps the 
company to get more appropriately priced liability insurance coverage. When rolling out its services in 
additional  MS,  the  company  has  reduced  legal  information  and  transaction  costs  as  it  can  rely  on 
harmonised  adaptations  of  the  burden  of  proof.  The  increased  level  of  legal  certainty  and  lower 
fragmentation  may  thus  incentivise  and  facilitate  it  for  the  company  to  roll  out  its  services  across 
borders. 

Legal certainty and reduced fragmentation would benefit start-ups and other SMEs in particular, 
as they are disproportionately affected by legal uncertainty and fragmentation.  

However, there is currently still a certain degree of uncertainty as to whether measures to ease the 
burden  of  proof  will  by  themselves  be  sufficient  to  ensure  legal  certainty  and  prevent 
fragmentation completely. In light of the technological, regulatory and market developments over 
the  coming  years  regarding  AI-systems  posing  a  risk  to  high-ranking  legal  interests,  MS  might 
come to the conclusion that there is a need for AI-specific strict liability rules. If they enact such 
rules  at  national  level  –  which  policy  option  1  allows,  it  cannot  be  ruled  out  that  the  legal 
fragmentation  expected  under  the  baseline  scenario  might  materialise  to  some  extent.  For  these 
reasons, policy option 1 is given a score of 4 for its effectiveness regarding specific objectives 1 
and  2,  assuming  that  this  option  would  be  implemented  through  a  binding  EU  instrument.  By 

359 For a more detailed description of the technology involved in this use-case, see Annex 13. 

189 

 
 
 
                                                           
contrast, if the measures to ease the burden of proof were to be merely recommended to MS, it is 
not expected that a significant harmonisation effect would be achieved. It is highly uncertain how 
many  MS  would  follow  the  recommendation,  and  even  amongst  those  that  do  take  up  the 
recommendations,  approaches  to  implementing  it  are  unlikely  to  be  aligned  given  the  diverging 
legal traditions in the area of civil and procedural law. The implementation of policy option 1 by 
means of a recommendation is therefore given a score of 1 regarding specific objective 2 (prevent 
fragmentation). As a recommendation may, because of its non-binding nature, prompt only a few 
MS to address the issue of legal uncertainty in their national law in the way it is recommended, 
this approach is given a score of 1 regarding specific objective 1 (legal certainty).  

(b) 

Policy Option 2 

A  harmonised  strict  liability  regime,  possibly  coupled  with  mandatory  insurance,  is  in  principle 
suitable  to  ensure  legal  certainty  and  prevent  fragmentation.  Provided  that  the  AI-enabled 
technologies  covered  by  that  regime  can  be  specified  with  a  high  degree  of  precision,  the 
companies operating / using those technologies could have an even clearer and consistent basis for 
assessing their liability risk. This would benefit start-ups and other SMEs in particular, as they are 
disproportionately affected by legal uncertainty and fragmentation.360 However, the time horizon 
and modalities of the roll-out of technologies with a relevant risk profile and degree of autonomy 
are not yet known. It would therefore, at this point in time, be to a certain degree challenging to 
assess the risk profile of those technologies, and to  specify them in  a legislative instrument  in  a 
way  that  ensures  a  maximum  of  legal  certainty  regarding  the  scope  of  the  harmonised  strict 
liability regime. This consideration would apply all the more with respect to a possible mandatory 
insurance regime covering strict liability. The obligation to ensure insurance coverage is a market 
entry  requirement,  which  means  that  it  is  crucial  to  enable  market  participants  to  assess  with  a 
high degree of certainty whether they fall under this requirement or not.  

Example361:  A  company  based  in  Belgium  wants  to  provide  outdoor  cleaning  services  using  AI-enabled 
cleaning robots in the Netherlands, France, Luxembourg and Germany. The robots have the size of a small 
car  and  are  intended  to  move  autonomously  (without  direct  or  constant  human  control  or  supervision), 
including in public spaces accessible to unwitting third parties. PO2 could clarify that this activity would 
be  subject  to  strict  liability,  possibly  coupled  with  a  mandatory  insurance  coverage  in  all  MS.  It  would 
thereby  support  a  more  predictable  and  certain  financial  planning  and  cost  management,  and  liability-
related costs would be limited to the insurance premiums. For insurers, harmonised strict liability would 
enable a more robust risk assessment and accurate pricing. 

In light of these elements, policy option is given a score of 3 regarding specific objective 1 (legal 
certainty),  and  a  score  of  4  regarding  specific  objective  2  (prevent  fragmentation),  assuming  it 
would be implemented through a binding instrument (Directive). For the same reasons as set out 
above  for  policy  option  1,  a  non-binding  instrument  is  unlikely  to  achieve  those  objectives  to  a 
significant extent even if it includes also recommendations to provide for strict liability, possibly 
coupled  with  mandatory  insurance.  This  sub-option  is  therefore  given  the  same  effectiveness 
scores as policy option 1.  

360 Economic Study, pp. 155 and 156. 
361 For a more detailed description of the technology involved in this use-case, see Annex 13. 

190 

 
 
 
                                                           
(c) 

Policy option 3 (staged approach) 

During  the  first  stage,  Option  3  would  be  equally  effective  as  Option  1.  Deferring  the  possible 
harmonisation of strict liability until there is more certainty about the technological and regulatory 
context defining the risk-profile and other conditions of deployment of the AI-enabled technologies 
with a potential ‘strict liability profile’ (operational environment, safety requirements, user profile, 
etc.)  is  conducive  to  ensuring  legal  certainty  and  uniform  implementation  of  the  possible  strict 
liability  regime  (objectives  1  and  2),  as  it  allows  to  specify  its  material  and  personal  scope  with 
greater  precision.  For  these  reasons,  policy  option  3  is  given  a  score  of  4  for  its  effectiveness 
regarding  specific  objectives  1  and  2.  The  non-binding  sub-option  is  scored  consistently  with 
options 1 and 2, as the same considerations and limitations apply. 

1.2. 

Specific  objective  3  (ensure  consistent  level  of  victim  protection;  increase  level  of 
societal trust and consumer uptake) 

(a) 

Policy option 1 

The  measures  to  ease  the  burden  of  proof  under  policy  option  1  would  effectively  prevent  AI-
induced compensation gaps, and thus be suitable to ensure that victims suffering harm caused by AI 
– whether they are consumers or businesses – have the same level of protection as victims harmed 
by other technologies. Victims would be relieved of having to overcome the characteristic opacity 
of  certain  AI-systems  to  prove  their  claims.362  Consequently,  they  would  spend  less  on  technical 
expertise and have better prospects of making a successful claim.363  

Example364:  In  the  previous  example  involving  an  autonomous  cleaning  robot  with  remote  human 
supervision (see 2.6.), PO1 would allow the victim to gain access to information held, in accordance with 
the proposed AI Act, by the provider or the user of the relevant AI-systems. This information could include 
e.g.  logged  information  on  inputs,  outputs  or  internal  states  of  the  AI  subsystems,  or  information  on  the 
suitable operating environment and human oversight requirements. On this basis, an expert could analyse 
the  correlations  between  input  parameters  and  the  output  signals that  caused  the  robot to  crash.  It  may 
thus  be  possible  to  discard  certain  causes  of  the  accident.  To  the  extent  that  the  required  causal  link 
between an action or omission of the potentially liable person and the damage would remain obscured by 
the  opacity  and  the  lack  of  explainability  of  the  AI  systems  involved,  PO1  would  alleviate  the  victim’s 
burden of proof to prevent that these characteristics lead to a lower level of victim protection. Moreover, if 
the victim, likely with the help of an expert analysing the available information on the AI-systems involved, 
can establish that the liable person (e.g. the company using the robot to provide cleaning services) did not 
comply with their obligations under the AI Act, the liable person’s fault would be presumed under PO1. As 
regards claims under the PLD against the final producer of the robot or manufacturers of individual AI-
components, the PLD revision would provide the injured party with access to technical information held by 
the producer, and ensure that the latter cannot avoid liability based on the development risk defence if the 
AI-systems at issue were by design unpredictable. Taken together, these measures would thus ensure that 
the involvement of AI does not lead to a lower level of protection of the injured person. 

The  behavioural  economics  study  commissioned  for  this  IA  has  shown  that  the  perceived  low 
likelihood  of  compensation  and  the  difficulty  to  determine  who  is  liable  count  amongst  the  most 

362 Economic Study, p. 133. 
363 Ibid. 
364 For details, see Annex 13. 

191 

 
 
 
                                                           
relevant  reasons  for  low  levels  of  consumer  trust  in  and  societal  acceptance  of  AI.365  It  has  also 
confirmed that consumers who perceive liability rules as appropriate to protect victims of harm are 
significantly more willing to take up such products and services366 and that a liability regime where 
the  burden  of  proof  has  been  adapted  in  favour  of  the  victim  ranks  higher  in  the  perception  of 
consumers  than  a  regime  where  the  victim  bears  the  full  burden  of  proof.  Option  1  is  therefore 
expected to effectively contribute – together with the already proposed adaptations of safety rules – 
to  increasing  the  level  of  societal  trust  in  AI-enabled  products  and  services  and  consumers’ 
willingness to take up such products and services. By preventing liability deficits, Option 1 would 
provide  an  effective  incentive  to  prevent  harm  and  thus  drive  safety-enhancing  innovation  and 
contribute indirectly to people’s overall level of safety.  

Indirect social impacts: By preventing liability deficits, PO1 would provide an effective incentive 
to  prevent  harm  and  thus  drive  safety-enhancing  innovation  and  contribute  indirectly  to  people’s 
overall  level  of  safety.367  This  mechanism  would  apply,  firstly,  to  businesses  subject  to  specific 
safety requirements – in particular the user and provider under the AI Act. Secondly, by ensuring 
the  effectiveness  of  general  liability  rules  under  national  law,  the  incentive  effect  of  PO1  could 
extend  to  any  stakeholders  whose  actions  or  omissions  may  have  contributed  to  the  causation  of 
damage,  such  as  e.g.  providers  of  labelled  training  or  testing  data.368  Moreover,  behavioural 
research has shown that adapting the burden of proof in favour of the injured party makes people 
more  likely  to  consider  that  victims  receive  just  compensation  and  that  the  legal  framework  is 
reasonable, predictable and transparent.369 By promoting effective  access  to justice, PO1 is  hence 
likely to increase societal trust in the justice system. 

Given that the risk-profile and operating parameters of future AI-systems are not fully known at the 
present, stage, it is however not entirely certain (yet) that measures to ease the burden of proof will, 
in combination with the revision of the PLD, be sufficient to fully ensure that victims are protected 
equally well when suffering harm caused by AI. For these reasons, policy option 1 is given a score 
of  4  for  its  effectiveness  regarding  specific  objective  3.  By  contrast,  if  the  measures  envisaged 
under  this  policy  option  would  be  merely  recommended  to  MS  in  a  non-binding  instrument,  the 
degree to which this specific objective could be achieved would hinge on the implementation rate 
by MS and thus be uncertain. As it is highly likely that a significant number of MS would not act on 
the  suggestions  in  way  they  would  be  recommended,  this  sub-option  is  given  a  score  of  1  for  its 
effectiveness regarding specific objective 3. 

(b) 

Policy option 2 

A  harmonised  strict  liability  and  possible  mandatory  insurance  regime,  as  the  distinguishing 
features  of  Option  2,  could  prevent  a  lack  of  compensation  even  more  effectively  than  the 
alleviations  of  the  burden  of  proof  common  to  Options  1  and  2.  The  expected  effects  on  societal 
trust follow a similar pattern as under Option 1, as it represents simply another  – potentially even 
more effective – way of ensuring an effective compensation of victims. As with the alleviations of 

365 Behavioural Economics Study, op. cit., pp. 24, 25 and 33. 
366 Ibid., p. 47. 
367 Economic Study, p. 164. 
368 Cf. in this respect Economic Study, p. 139, pointing out that an approach assigning (strict) liability to a specific 
actor has the downside that the role of “distant” parties in the chain may be overlooked despite their ability to affect 
the risk and likelihood of damage, which may cause such parties to care less about safety consideration. 
369 Behavioural Economics Study, op. cit., executive summary, p. iii. 

192 

 
 
                                                           
the  burden  of  proof,  this  is  likely  to  have  a  positive  effect  on  consumers’  perception  of  the 
appropriateness of liability rules, which in turn is likely to increase their willingness to take up AI 
applications. However, the assessment must also account for the fact that the specific risk profile of 
relevant AI-enabled products and services is not yet fully known, as these products and services are 
still in a pre-market development phase.  

Indirect  social  impacts:  The  mechanisms  by  which  the  strict  liability  element  of  PO2  would 
contribute to incentivising users of AI-enabled technologies with a special risk profile to minimise 
harm are similar to the ones discussed under PO1. The ability of strict liability rules to incentivise 
safety efforts depends to a large extent on whether the strictly liable person has cost-efficient means 
to prevent damages370. The control criterion envisaged for assigning strict liability to professional 
users/operators  is  therefore  conducive  to  the  desired  incentive  effects.  Moreover,  behavioural 
research  has  shown  that  a  strict  liability  framework  for  AI  is  more  likely  to  be  perceived  as 
predictable and transparent than fault-based liability.371 The strict liability element is thus likely to 
increase societal trust in the justice system. 

Taking into consideration all of these elements, Option 2 is given a score of 4 for its effectiveness 
regarding  specific  objective  3.  The  sub-option  assuming  a  non-binding  instrument  is  scored 
consistently with Option 1, as the same limitations apply. 

(c) 

Policy option 3 

During  the  first  stage,  Option  3  would  be  equally  effective  as  Option  1  in  achieving  specific 
objective 3. While a potentially even more far reaching protection of victims through strict liability 
and possibly mandatory insurance for the use of certain AI-technologies will not be realised during 
this first stage, the targeted review mechanism allows to systematically re-assess the need for more 
these more far-reaching measures. This mechanism lays the groundwork for ensuring that specific 
objective  3  can  be  fully  achieved  even  if  the  need  for  a  harmonised  strict  liability  regime  is 
confirmed in light of technological and market developments as well as empirical evidence on civil 
liability cases involving AI.  

Option 3 is therefore given a score of 4 for its effectiveness regarding specific objective 3. The sub-
option assuming a non-binding instrument is scored consistently with Options 1 and 2, as the same 
limitations apply. 

1.3. 

Comparative overview of effectiveness scores 

Success criteria for specific objectives 

Specific objective 1 (legal certainty) 

- Increased level of legal certainty, leading to improved conditions for (in 
particular cross-border) business activities / investments (e.g. reduced costs, 
more investment and financing security) 
- Increased level of legal certainty, leading to improved conditions for offering 
insurance coverage, in particular for cross-border activities; emergence of new 
market opportunities for insurance companies. 

Score 
(impact net of the baseline (-5 to +5) 
Policy Option 1  Policy option 2  Policy Option 3 

4 (binding) 

3 (binding) 

4 (binding) 

1 (non-bind.) 

1 (non-bind.) 

1 (non-bind.) 

370 Economic Study, p. 142. 
371 Behavioural Economics Study, op. cit., executive summary, p. iii. 

193 

 
 
 
 
 
 
 
 
 
 
 
                                                           
Success criteria for specific objectives 

Specific objective 2 (prevent legal fragmentation) 

- no emergence of fragmented liability rules for AI at MS level  improved 
conditions for cross-border business activities / investments (reduced costs, 
more investment and financing security,...) 
- Improvement of the conditions for offering insurance coverage, in particular 
for cross-border activities; emergence of new market opportunities for 
insurance companies. 

Specific objective 3 (compensation of victims / trust in AI) 
- The effectiveness of liability claims is not diminished by the specific 
characteristics of AI (opacity, autonomy, complexity, lack of predictability, 
etc.) 
- Liability rules provide an effective incentive to potentially liable persons (in 
particular users and providers of AI systems) to prevent harm 
- Increase in the level of societal trust and consumers’ willingness to take up 
AI-technologies. 

Score 
(impact net of the baseline (-5 to +5) 
Policy Option 1  Policy option 2  Policy Option 3 

4 (binding) 

4 (binding) 

4 (binding) 

1 (non-bind) 

1 (non-bind.) 

1 (non-bind.) 

4 (binding) 

4 (binding 

4 (binding) 

1 (non-bind) 

1 (non-bind.) 

1 (non-bind.) 

Overall score 

12 (binding) 
3 (non-bind.) 

11 (binding) 
3 (non-bind.) 

12 (binding) 
3 (non-bind.) 

2. 

Efficiency 

2.1. 

Policy option 1 

(a) 

Impacts on potentially liable parties (businesses and natural persons)  

Due to  the  envisaged harmonised adaptations of  the burden of proof, potentially liable parties  – 
such as companies using AI-enabled products to provide services – would have a more robust and 
consistent basis for assessing their liability risk outside the scope of the PLD. As explained in the 
context  of  effectiveness  above,  it  cannot  be  ruled  out  that  certain  MS  might  come  to  the 
conclusion,  in  light  of  the  technological,  regulatory  and  market  developments  over  the  coming 
years, that there is a need for AI-specific strict liability rules. If they enact such rules at national 
level,  the  legal  fragmentation  expected  under  the  baseline  scenario  might  materialise  to  some 
extent  despite  PO1.  Nevertheless,  the  reduction  of  legal  uncertainty  and  fragmentation  through 
harmonised  measures  delivered  by  PO1  would  benefit  developers  and  users  of  AI-enabled 
technologies  (e.g.  companies  providing  AI-enabled  services)  by  generating  direct  regulatory 
benefits,  namely  through  the  reduction  of  legal  information/representation,  internal  risk 
management, and other compliance-related costs, as well as additional cross-border revenue.372 By 
clarifying  the  kind  of  information  and  evidence  potentially  liable  parties  may  be  required  to 
submit  in  civil  proceedings,  PO1  would  also  help  them  to  choose  more  efficiently  between 
different  technological  options373,  namely  by  favouring  more  transparent  and  explainable 
solutions.  

As  start-ups  and  other  SMEs  are  significantly  more  affected  by  the  internal  market  barriers 
created  by  legal  uncertainty  and  fragmentation  (see  2.6.),  this  stakeholder  group  would  also 
benefit  to  a  higher  degree.374  The  expected  positive  impacts  of  PO1  on  societal  trust  in  AI  and 
consumers’  willingness  to  take  up  AI-enabled  products  and  services,  as  well  as  the  improved 

372 Cf. Economic Study, pp. 113 et seq., 161 and 163. 
373  For  explanations  on  how  liability  rules  may  influence  how  products  and  services  are  designed,  see  e.g. 
Galasso/Luo, Punishing Robots  - Issues in the Economics of Tort Liability and Innovation in Artiﬁcial Intelligence, 
in: Agrawal/Gans/Goldfarb (eds.), The economy of artificial intelligence: an agenda, Chicago 2020, p. 493(501). 
374 Cf. Economic Study, pp. 122 and 161. 

194 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
competitiveness of the European AI sector would directly or indirectly benefit all companies in the 
AI value chain.375  

The findings of the supporting economic study refer to a preliminary set of policy options, which 
was  not  identical  to  the  policy  options  retained  for  this  impact  assessment.  In  particular,  the 
economic study assumed two different combinations of alleviations of the burden of proof under 
fault-based liability rules with a harmonised strict liability regime. One of these options reflected a 
targeted  and  AI-specific  approach  similar  to  the  one  described  above  under  PO2,  the  other  the 
European Parliament’s  resolution  on a  civil  liability regime for AI. The  economic study did  not 
explicitly assess the economic impacts of alleviations of the burden of proof, as per PO1, taken in 
isolation.  The  policy  options  retained  for  detailed  assessment  have  evolved  precisely  due  to  the 
conclusions  of  the  economic  study,  as  well  as  due  to  the  results  of  the  public  consultation  and 
following  discussions  with  stakeholders.  Some  assumptions  made  in  the  framework  of  the 
economic study had to be reconsidered, in particular as regards the feasibility, at the current point 
in time, to define the scope of a harmonised strict liability regime for AI with a sufficient degree 
of precision and certainty. It therefore proved important to consider alleviations of the burden of 
proof  on  their  own  (PO1),  as  an  alternative  to  introducing  these  alleviations  together  with  strict 
liability  (PO2).  It  is  acknowledged  that  this  entails  a  degree  of  uncertainty  as  to  the  extent  to 
which  the  conclusions  of  the  economic  study  apply  to  PO1.  For  the  following  reasons,  the 
assessments made by the economic study are nevertheless still largely relevant: 

- According to  the study, the economic benefits  of an EU initiative on AI liability are primarily 
attached to the expected gains of legal certainty and reduced legal fragmentation. These effects are 
expected  to  materialise  also  with  respect  to  the  alleviations  of  the  burden  of  proof  taken  in 
isolation  (PO1),  which  will  clarify  in  a  harmonised  manner  how  the  burden  of  proof  is  to  be 
handled in cases involving AI. 

-  The  measures  PO1  shares  with  two  of  the  policy  options  assumed  for  the  purposes  of  the 
economic  study  are  relevant  for  the  major  share  of  AI-enabled  products  and  services,  and  thus 
decisive for the economic impacts on most stakeholders. This is because only a small set of AI-
enabled technologies would have a risk profile warranting the application of strict liability.   

These benefits are likely to outweigh the following adaptation (substantive compliance) costs and 
redistribution  effects  linked  to  PO1:  The  business-as-usual  costs  under  the  baseline  scenario 
related to the uncertainty to assess what liability rule would apply to AI and what burden-of-proof 
rule a court would apply in a concrete case are higher than any potential adjustment costs borne by 
potentially liable parties. It will be easier for companies to estimate liability risks and related costs. 
While under the baseline scenario courts might apply on an ad-hoc basis alleviations of the burden 
of proof to remedy what they consider an unequitable result, clear and harmonised alleviations of 
the  burden  of  proof  will  help  liable  parties  to  know  what  to  expect  in  case  AI  is  involved  both 
domestically  and  cross-border.  Companies  operating  cross-border  would  benefit  from  reduced 
compliance costs compared to the very fragmented baseline scenario. Such clarity might also help 
companies get appropriately priced liability insurance coverage. 

In cases where the specific characteristics of AI would not have allowed the victim to prove the 
necessary facts under the baseline scenario, PO1 would shift the cost of compensating the relevant 
damage  from  the  victim  to  the liable  person,  increasing  the  latter’s  liability  exposure.  Likewise, 
victims would be relieved of some of the cost linked to meeting the burden of proof (e.g. costs of 
expert analysis). This cost may partly shift to the potentially liable party (who is however much 
more  likely  to  have  the  necessary  knowledge  of  the  relevant  AI  systems  in-house,  without  the 

375 Ibid., p. 165. 

195 

 
 
                                                           
need  to  procure  external  technical  expertise).  These  effects  are  inherent  in  the  Commission’s 
objective of avoiding that victims are less protected due to the use of AI. They are not regarded as 
undesirable  impacts  or  undue  burden.  They  are  in  line  with  the  policy  objective  to  ensure  that 
victims of damage caused with the involvement of AI systems have the same level of protection as 
victims of damage caused by other technologies and in general with the purpose of liability law. 
They also achieve a macro-economically more efficient cost-allocation to the person best placed to 
prevent damage from occurring. For the impact analysis, these effects are taken into account as a 
re-distribution  effect.  This  effect  may  entail  an  incremental  increase  of  insurance  premiums 
covering AI liability risks. As a major share of liable parties is likely to hold liability insurance376 
covering (also) risks linked to activities involving AI377, the impact of the burden re-distribution is 
approximated through a quantified estimate of its indirect effect on insurance premiums. For this 
purpose, it is assumed that the measures to ease the burden of proof would cause an increase by 
15%  of  the  share  of  general  liability  insurance  premiums  attributable  to  AI  liability  risks.  This 
assumption is based on the following considerations: 

-  PO1  does  not  involve  a  general  reversal  of  the  burden  of  proof,  but  only  targeted 
adjustments  to  counter-balance  the  specific  challenges  of  AI.  The  negative  economic 
impacts  for  potentially  liable  parties  are  likely  to  be  very  marginal,  as  indicated  by  the 
quantified estimates set out below. 

-  PO1 is likely to achieve an overall more efficient allocation of the burden of proof, as the 
difficulty to establish how or why an AI system arrived at a harmful output is typically less 
burdensome for potentially liable parties having influenced the operation of that AI-system 
(e.g. developers, users) than for victims.  

-  As PO1 does not introduce new grounds of liability and keeps the basic allocation of the 
burden of proof intact, it is not expected to lead to a major increase in the number of civil 
actions – or an associated increase of insurance premiums – compared to the baseline, nor 
to significant familiarisation and implementation costs for businesses.  
In many cases, national courts already have similar tools (disclosure orders, presumptions) 
at  their  disposal  under  the  baseline  scenario,  although  it  is  highly  uncertain  whether  and 
how these tools would be used in practice. 

- 

-  Under  the  baseline,  some  MS  might  take  partly  similar  measures  in  their  national  legal 
systems to address the specific challenges of AI. However, it is uncertain how many would 
do so and what precise shape these measures would take. National initiatives would in all 
likelihood not be aligned and thus entail further legal fragmentation. 

-  The  increased  legal  certainty  and  reduced  fragmentation  delivered  by  PO1  will  have  a 
premium-lowering effect on insurance, which will partly offset the premium-driving effect 
of preventing AI-induced compensation gaps. 

Based  on  the  methodology  described  under  point  1.2.(a)(iii)(last  indent)  above,  an  increase  by 
15% of the insurance premiums attributable to AI liability risks would represent an overall cost of 
EUR 5.35mln (based on the lower estimate of the AI market size) to EUR 16.1mln (based on the 
higher estimate of the AI market size) for potentially liable parties. 

The  instrumental  role  of  insurance  in  distributing  and  managing  the  impacts  of  the  envisaged 
policy  measures  needs  to  be  underlined:  ultimately,  insurance  coverage  will  allow  potentially 

376  Ibid.,  pp. 141,  142,  163,  164.  The  study  supporting  the  evaluation  of  the  Product  Liability  Directive  found  that 
around  80%  of  manufacturers  hold  liability  insurance  (cf.  Ernest  &  Young,  Evaluation  of  Council  Directive 
85/374/EEC, 2018, p. 19). While insurance uptake may not be identical amongst operators / users of AI technologies 
subject  to  strict  liability,  it  can  nevertheless  be  deduced  that  voluntary  insurance  coverage  is  widespread,  at  least 
amongst businesses whose activities involve the risk of causing substantial damage. 
377 This  was confirmed by input received from the  insurance  industry during the consultation activities feeding into 
this impact assessment. 

196 

 
 
                                                           
liable  businesses  to  spread  liability  costs  across  the  community  of  all  insured  and  thereby 
communitarise  costs.  This  mechanism  limits  the  economic  burden  on  each  individual  insurance 
holder to the premium, preventing a possible deterring effect of liability risks and keeping market 
entry barriers low, which facilitates the roll-out of AI in particular by start-ups and other SMEs.378 
A  large  portion  of  businesses  concerned  will  likely  procure  insurance  coverage  voluntarily  to 
benefit  from  this  cost-limiting  effect.379  The  expected  development  of  a  competitive  insurance 
market for AI-related liability risks will provide the necessary conditions for effective coverage at 
moderate  prices.380  This  expectation  is  supported  by  the  fact  that  the  insurance  industry  is 
forcefully pursuing the development of innovative AI-specific insurance products, to explore the 
substantial new opportunities linked to this growth market381. First insurance policies designed to 
cover specifically AI-enabled technologies have already been rolled out382. AI liability insurance 
may also be incorporated as an additional feature into existing general policies.383 While an initial 
lack of actuarial data is likely to influence for a transitional period to some extent the premiums of 
AI-specific insurance products, and make these premiums more volatile, this effect is expected to 
dissipate quickly as the data generated during the operation of these technologies will allow risk 
estimations to converge faster towards the optimum than in the case of ‘traditional’ technologies. 
Moreover, even if during an initial phase AI-specific insurance policies may be priced higher than 
warranted by the actual liability risk covered, this does not mean that premiums would be higher 
than  for  competing  technologies  not  equipped  with  AI.  The  expected  safety  gains  achieved 
through AI  are likely to  have a mitigating effect  on insurance premiums  in  many cases384 and a 
sufficient number of insurance companies is likely to be active in the AI-specific insurance market 
from the outset, as they can rely on various tools and approaches to overcome the initial lack of 
actuarial data.385 The regulatory framework established by the AI Act for the development and use 
of high-risk AI systems is likely to improve the conditions for AI risk assessment by insurers over 

378 Economic Study, p. 163. 
379 Ibid., pp. 163 and 164. 
380 Ibid., pp. 148 et seq., 164. 
381 Cf. e.g. Harvard Business Review, The Case for AI Insurance, April 2020. 
382 E.g. Ensure AI‘ by MunichRe, enabling AI providers to guarantee the performance of their AI systems, including 
for AI-systems controlling physical products (e.g. robots). Novel technologies are being developed through statistical 
research (e.g. ‘distribution-free uncertainty quantification’ including ‘conformal inference’), which will enable a more 
standardised failure rate assessment.  
Cf. also  La Playa, offering brokerage of  specialist  AI insurance. Experts  working on  AI  insurance  for a leading re-
insurance company confirmed in bilateral contacts with the Commission services that the insurance industry expects 
an increasing demand for specific AI liability coverages and are developing capabilities to assess AI risks with a view 
to developing new products, including for the coverage of third-party losses. 
383 See e.g. Insurance Europe’s reply to the public consultation: “…insurance can lessen the negative consequences of 
accidents  involving  AI  by  ensuring  that  the  victim  receives  compensation.  There  are  already  many  such  insurance 
solutions available in the European insurance market. Protection against material damage incurred by AI generally 
falls within the remit of general liability insurance policies, which are sold on an all-risks basis.” A major insurance 
company  explained  in  a  bilateral  exchange  with  the  Commission  services  that  AI  liability  will  be  likely  be 
incorporated as additional feature into existing insurance policies, and that the increasing integration of AI in business 
activities is not expected to lead to an increase of premiums on a general level.  
384 Economic Study, p. 146.  
385  Such  as  risk  testing,  advanced  analytics,  cooperation  with  developers  and  risk  modelling,  cf.  Economic  Study, 
pp. 145 and 146. Experts working on AI insurance for a major re-insurance company confirmed in bilateral contacts 
with  the  Commission  services  that  the  insurance  industry  expects  an  increasing  demand  for  specific  AI  liability 
coverages and are developing capabilities to assess AI risks with a view to developing new products, including for the 
coverage of third-party losses. The White Paper ‘Artificial Intelligence and Algorithmic Liability – a technology and 
risk engineering perspective from Zurich Insurance Group and Microsoft Corp’ (July 2021) points for instance to ‘AI 
model  monitoring’  based  on  application  and  model  telemetry,  ‘acceptable  use  policies’  specifying  rules  for 
appropriate  conduct  by  insured  AI  users,  and  the  use  of  ‘algorithmic  design  history  files’  to  document  the  input, 
output,  review,  verification,  validation,  transfer  and  changes  to  the  model.  The  same  paper  also  underlines  that 
technology companies and insurers can pair their strengths to mitigate algorithmic risks across industries. 

197 

 
 
                                                           
the  coming  years.  In  addition,  the  Data  Act  will  promote  access  to  data  generated  by  a  user’s 
product  and thus  facilitate the provision  of services that depend on or  can be improved by such 
data, including insurance and data analytics.386 Moreover, the Commission services will respond 
to the Parliament’s call to work closely with the insurance market to develop innovative insurance 
products387. The Commission will notably facilitate a dialogue between the insurance industry and 
companies active in the AI market (in particular SMEs). 

The  results  of  the  public  consultation  confirmed  that  insurance  solutions  could  ensure  that  the 
victim receives compensation (63,7 % agreement v. only 5 % disagreement) and limit the costs of 
potential  damage  for  the  liable  person  to  the  insurance  premiums  (49,3 %  agreement  v.  only 
18,8 %  disagreement).  Even  the  share  of  business  stakeholders  (business  associations  + 
companies/business organisations) who confirmed the latter effect (37,8 %) was more than three 
times as large as the share of those not agreeing it (12,2 %). 

Insurance  Europe  submitted  that  “liability  insurance  plays  a  vital  role  by  transferring  liability 
risks from companies and consumers to insurers and thereby, protecting the insureds’ economic 
position as well as ensuring that injured persons are compensated for loss or damage.” 

With  respect  to  high-risk  AI-systems,  PO1  could  indirectly  entail  some  minor  administrative 
burden, namely to the extent that it prompts the disclosure of information documented pursuant to 
the  AI  Act.  However,  PO1  relates  only  to  information  that  had  to  be  logged,  documented,  and 
stored  for  possible  disclosure  to  supervisory  authorities  pursuant  to  the  AI  Act  –  activating  the 
same  information  also  in  the  context  of  civil  proceedings  is  not  expected  to  entail  a  significant 
added  burden.  It  would  only  apply  in  the  context  of  pending  civil  proceedings  before  national 
courts. Moreover, in accordance with relevant procedural law, the competent national court could 
order that such disclosure would be subject to stringent safeguards to ensure proportionality and 
protect  the  legitimate  interests  of  all  parties  concerned,  for  instance  confidential  information, 
intellectual  property  rights  and  trade  secrets.  Potentially  liable  parties’  interests  would  thus  be 
effectively safeguarded by PO1. 

(b) 

Impacts on victims of damage caused by AI (natural persons and businesses) 

In line with the political objective to ensure that victims of harm caused with the involvement of 
AI  enjoy  the  same  level  of  protection  as  persons  having  suffered  harm  caused  by  other 
technologies,  PO1  would  relieve  the  victims  of  the  burden  of  bearing  the  damage,  to  the  extent 
that  their  claims  for  compensation  would  have  failed  under  the  baseline  due  to  the  specific 
challenges  of  AI.  This  burden  would  be  re-distributed  to  the  person  responsible  for  causing  the 
damage. This applies not only in respect of material damage, but also pure economic loss and non-
material  harm  (such  as  psychological  harm  and  damage  caused  by  discrimination)  to  the  extent 
that  these  types  of  harm  are  compensable  under  existing  rules.  PO1  would  also  reduce  victims’ 
costs  linked  to  the  burden  of  proof  (e.g.  for  expert  analysis),  by  ensuring  access  to  relevant 
information and alleviating the victim’s burden of establishing how or why an AI-system arrived 
at  a  certain  output.  Based  on  a  conservative  estimate,  it  is  assumed  that  these  measures  would 
reduce the costs to be advanced by victims due to the AI-specific difficulty of meeting the burden 
of proof by at least 50 %. This assumption is based on the following considerations: 

386 See the policy measures envisaged in the Data Strategy Communication (COM/2020/55/final); cf. also Economic 
Study, p. 201. 
387 EP, Resolution 2020/2014(INL) on a civil liability regime for artificial intelligence, recital 22. 

198 

 
 
 
                                                           
-  PO1 does not provide for a general reversal of the burden of proof. In principle, the victim 
would  still  bear  the  burden  of  proof  in  accordance  with  general  rules.  However,  the 
targeted  alleviation  of  the  burden  of  proof  regarding  the  question  how  or  why  an  AI-
system reached a certain (harmful) output would relieve victims of the need to ‘look inside 
the black box’ to demonstrate the inner workings of the AI system. 

-  The  measures  linked  to  the  AI  Act  (rules  on  the  disclosure  of  information  to  be 
documented/logged pursuant to the AI Act + presumption of causality in the case of non-
compliance  with  relevant  requirements  of  the  AI  Act)  apply  only  with  respect  to  AI 
systems  qualified as ‘high risk’ by the AI  Act.  While this is in  line with the principle of 
proportionality and the Commission’s risk-based approach to AI regulation, it means that 
the measures do not alleviate the victim’s burden of proof in cases where other types of AI 
systems  cause  damage.  In  the  latter  cases,  victims  can  nevertheless  invoke  the  targeted 
alleviation of the burden of proof. 

Based on this assumption, it is estimated that PO1 would reduce costs to be advanced by victims 
to  meet  the  burden  of  proof  by  ca.  EUR 2 000  on  average,  per  case  in  which  the  harmonised 
provisions apply. This estimate should not be misconstrued as a comprehensive quantification of 
the  AI-specific  difficulty  of  meeting  the  burden  of  proof,  because  it  does  not  take  into  account 
cases  in  which  liability  claims  would  not  be  pursued  in  the  first  place  based  on  current  liability 
rules,  because  the  victim  either  cannot  identify  the  liable  party  or  considers  the  prospect  of  a 
successful claim insufficient to justify legal action. The preferred policy option will help victims 
also  in  the  latter  cases,  by  overcoming  the  compensation  gaps  induced  by  the  specific 
characteristics of AI.  

The burden of proof will be distributed more efficiently overall, as potentially liable parties must 
by  definition  be  capable  of  influencing,  to  some  extent,  the  operation  of  AI-systems.  They  are 
therefore typically in a position to more easily discharge the burden of proof with respect to how 
or why such systems arrived at a certain harmful output. This has a cost-cutting effect on overall 
litigation costs. 

(c) 

Consumers 

A faster roll-out of AI-technologies under PO1 would benefit consumers, e.g. in the form of faster 
and  more  personalised  services,  innovative  and  performant  products  as  well  as  advances  in  the 
fields of health, safety, security, mobility, sustainability, circular economy, media, etc. Given the 
overall  positive  economic  impacts  also  on  businesses,  it  is  not  expected  that  the  envisaged 
measures would lead to costs being passed on through increased consumer prices.  

(d) 

Insurance companies 

PO1 may marginally increase the take-up of insurance by potentially liable parties – provided the 
insurance  coverage  is  not  already  included  in  existing  all-risks-policies388,  as  increased  legal 
certainty  and  reduced  fragmentation  create  more  favourable  conditions  for  offering  insurance 
coverage,  and  awareness  of  liability  risks  may  slightly  rise  due  to  this  initiative.  An  increased 
coverage  rate  would  benefit  victims  of  damage  as  insurance  claims  provide  an  easier  path  to 
compensation and relieve victims of the liable party’s insolvency risk. 

(e) 

Indirect economic impacts and impacts on the competitiveness of the internal market 

388 AI liability risks are likely to be covered by existing all-risk policies in many cases, cf. fn. 161. 

199 

 
 
 
                                                           
By  the  same  token  (avoidance  of  liability  gaps),  PO1  would  contribute  to  an  efficient  cost 
allocation. Its combined impacts are expected to have a positive effect on cross-border trade in AI-
enabled products and services and the development of the European AI-sector as a whole.389 The 
economic  study  commissioned  for  this  impact  assessment  estimated  that  a  combination  of 
alleviations  of  the  burden  of  proof  (as  per  PO1)  and  measures  to  harmonise  strict  liability  for 
certain AI-enabled products and services (cf. PO2 and 3) would increase the cross-border trade in 
the AI-enabled goods and services falling under the six use-cases analysed in depth for that study 
by about 5 %. While PO1 does not include all of the assumptions made for that estimation, it is 
nevertheless relevant because the decisive drivers of the expected economic benefits  – increased 
legal  certainty,  reduced  fragmentation  and  increased  consumer  trust  –  are  likely  to  materialise 
under PO1.390 As explained under point A.2.1.3.(f) above, PO1 is expected to deliver an increase 
of the AI market value in the EU of between ca. EUR 500mln and ca. EUR 1.1bln. 

The attempt of the European Parliament to quantify the benefits of a clear and coherent EU civil 
liability  regime  for  AI  remained  inconclusive391.  Nevertheless,  its  preliminary  analysis  suggests 
that the added value of EU action on liability could generate EUR 54.8 billion by 2030 for the EU 
economy, in terms of acceleration of the level of research and development in AI, and in the range 
of EUR 498.8 billion if other impacts, including reductions of accidents, health and environmental 
impacts and user impacts are also taken into consideration.392 As these numbers were not linked to 
a  clearly  defined  set  of  PO,  they  cannot  be  readily  applied  to  the  PO  described  in  this  impact 
assessment.  However,  they  provide  a  reasoned  view  on  the  order  of  magnitude  of  potential 
economic benefits linked to a clear and consistent civil liability framework for AI. 

(f) 

Enforcement, adjudication and litigation costs 

Only  small  incremental  impacts  on  enforcement,  adjudication  and  litigation  costs,  borne  by  MS 
and parties to the proceedings respectively, are expected under PO1. The envisaged targeted and 
limited  adaptations  of  the  burden  of  proof  are  not  likely  to  entail  a  substantial  increase  in  the 
number of civil actions, as they are designed to apply only in the confined cases where the specific 
characteristics  of  an  AI  system  make  it  unduly  difficult  to  meet  the  default  burden  of  proof. 
Moreover,  the  burden  of  proof  will  be  distributed  more  efficiently  overall,  as  potentially  liable 
parties  must  by  definition  be  capable  of  influencing  the  operation  of  AI-systems.  They  are 
therefore typically in a position to more easily discharge the burden of proof with respect to how 
or  why  such  systems  arrived  at  a  certain  harmful  output.  This  has  a  cost-cutting  effect  on  the 
overall  enforcement,  adjudication  and  litigation  costs.  More  particularly,  it  is  assumed  that  a 
fraction ranging from 10 % to 80 % of the amount saved by victims due to the alleviations of the 
burden of proof under PO1 will have to be advanced by potentially liable businesses393. This broad 
assumed range is based on the following considerations: 

389 Economic Study, p. 196 and 197. 
390 Ibid., pp. 196-198. 
391 EPRS, European added value assessment, September 2020, op. cit., p. 63. 
392 Ibid.  
393  The  policy  options  are  not  expected  to  entail  additional  litigation  costs  for  private  persons  as  potentially  liable 
parties. These stakeholders are likely to defend themselves against liability claims using the same type of arguments 
and  evidence  as  under  the  existing  burden  of  proof  rules.  For  example,  they  might  seek  to  avoid  liability  by 
demonstrating that they acted diligently and in accordance with the instructions of use accompanying an AI-enabled 
product.  Contrary  to  potentially  liable  businesses,  which  may  have  special  knowledge  and  be  subject  to  certain 
requirements regarding the functioning and ‘inner workings’ of an AI system (in particular under the AI Act), private 
persons  would  not  have  to  base  their  defense  on  an  analysis  of  the  functioning  of  such  a  system.  The  envisaged 
alleviation  of  victims’  burden  of  proof  regarding  the  ‘inner  workings’  of  AI  systems  is  therefore  not  expected  to 
prompt potentially liable private persons to commission technical expertise. 

200 

 
 
                                                           
- 

In certain cases, for instance where the defendant is a provider of a high-risk AI systems 
falling under the AI Act, they will have optimal information on and understanding of the 
functioning  of  the  relevant  AI  system.  They  will  thus  not  need  to  procure  any  external 
technical expertise to discharge the burden of proof. A small fraction (e.g. 10%) of the cost 
may nevertheless be shifted to this type of liable party as PO1 may cause them to devote 
some additional internal resources to: 

o  establishing  why  or  how  the  relevant  AI  system  arrived  at  a  certain  (harmful) 
output (e.g. through reverse engineering of the output, testing of the AI system and 
digital forensics) and  

o  providing information on the AI system to the victim. 

-  On  the  other  end  of  the  spectrum  of  conceivable  cases,  the  defendant  may  not  have  any 
advanced  understanding  of  the  functioning  of  the  relevant  AI  system,  nor  easy  access  to 
detailed information on that system. This may for instance be the case where the defendant 
is  an  SME  using  an  AI  system  not  falling  under  the  transparency  and  documentation 
requirements of the AI Act. As this type of defendant is nevertheless in a better position 
than the victim for establishing the trigger of the damage, it is assumed that even  in such 
cases  the  amount  saved  by  victims  would  not  be  re-distributed  to  the  defendant  in  its 
entirety (but only to a large extent, e.g. by 80%). 

Based on these assumptions, PO1 would entail an increased cost for the liable party linked to the 
burden  of  proof  of  ca.  EUR 200  to  ca.  EUR 1 600  per  case  in  which  the  harmonised  provisions 
apply. 

(g) 

Efficiency score of PO1 assuming a binding legal instrument (Directive) 

In light of these considerations, PO1 is given an efficiency score of 4, assuming that it would be 
implemented by a binding legal instrument (most likely a Directive). 

(h) 

Efficiency score of PO1 assuming a non-binding instrument (recommendation) 

By contrast, if the measures under PO1 would be merely recommended to MS in a non-binding 
instrument,  the  described  benefits  would  likely  materialise  to  a  much  lesser  extent.  The 
implementation  rate  of  non-binding  instruments  is  difficult  to  predict  and  there  is  no  sufficient 
indication  that  the  persuasive  effect  of  a  recommendation  would  be  strong  enough  to  produce 
consistent  adaptations  of  national  laws.  Therefore,  the  desired  harmonisation  effect  and  legal 
certainty  is  unlikely  to  be  delivered  by  a  recommendation.  This  applies  to  a  special  degree  to 
measures concerning the private law of obligations, of which extra-contractual liability rules form 
part.  This  area  is  characterised  by  long-standing  legal  traditions,  which  traditionally  makes  MS 
reluctant  to  pursue  harmonised  reform  unless  driven  by  the  clear  prospect  of  internal  market 
benefits under a binding EU instrument.394 Moreover, the significant existing divergences between 
MS’ civil liability frameworks (see 2.4.), are another reason why a recommendation is unlikely to 
be implemented in a consistent manner. While the expected slight increase in insurance premiums 
might  be even lower if PO1 were implemented by a non-binding instrument395, divergences and 
reduced  legal  certainty  in  cross-border  cases  would  persist  (even  to  the  –  likely  very  limited  – 
extent that MS choose to implement a recommendation). Likewise, compensation gaps are likely 

394  Cf.  e.g.  Caruso,  The  Missing  View  of  the  Cathedral:  The  Private  Law  Paradigm  of  European  Legal  Integration, 
European Law Journal, Vol. 3, No. 1 March 1997, p. 3. 
395 Given the high degree of uncertainty regarding the take-up of a non-binding recommendation by MS, there is no 
sufficiently robust basis for a quantified estimate in this respect. In order to estimate the added insurance costs, one 
would have to account  not only  for the likelihood that a  number of MS  would not  follow the recommendation, but 
also  for  the  fact  that  the  premium-lowering  effect  of  increased  legal  certainty  and  reduced  fragmentation  would 
materialise only to a much smaller extent.   

201 

 
 
                                                           
to persist to a large extent and the expected cost savings for victims linked to the alleviations of 
the burden of proof would  mostly not  materialise.396 The economic study confirmed that a non-
binding  initiative  would  not  address  the  identified  internal  market  obstacles  effectively,  as  the 
underlying problems will likely be perpetuated to a substantial extent.397 It concluded that a non-
binding  instrument  would  not  achieve  any  increase  in  cross-border  trade.398  Therefore,  this  sub-
option is given an efficiency score of 1. 

2.2. 

Policy option 2 

PO2 differs from PO1 as regards the strict liability regime applicable to users of AI technologies 
with  a  special  risk-profile,  possibly  coupled  with  a  mandatory  insurance  regime.  The  following 
efficiency assessment therefore focuses on these elements. 

(a) 

Impacts on businesses (in particular as potentially liable parties) 

Provided  that  the  AI-enabled  technologies  covered  by  the  harmonised  strict  liability  can  be 
determined with a sufficient degree of precision, this element of PO2 would be suitable to increase 
legal certainty and reduce fragmentation by providing users of AI-enabled technologies having a 
special risk profile with  a broader basis for  assessing their liability risk outside the scope of the 
PLD.  This  could  generate  direct  regulatory  benefits, 
legal 
information/representation,  internal  risk  management,  insurance  and  other  compliance-related 
costs,  as  well  as  additional  cross-border  revenue.399  Moreover,  the  expected  positive  impacts  of 
PO2  on  consumers’  willingness  to  take  up  AI-enabled  products  and  services  (see  below)  would 
directly or indirectly benefit all companies in the AI value chain. However, the fact that the time 
horizon for the roll-out of the technologies that might fall under the scope of the envisaged strict 
liability  regime,  as  well  as  the  modalities  defining  their  risk  profile  (e.g.  their  regulatory  and 
operating environment, safeguards, supervision), are not entirely clear could reduce the expected 
benefits  of  PO2  in  terms  of  ensuring  legal  certainty.400  For  the  reasons  set  out  in  the  following 
paragraphs, the overall economic benefits are nevertheless expected to outweigh the cost factors 
under PO2.  

the  form  of  reduced 

in 

In  certain  cases,  beyond  merely  harmonising  existing  strict  liability  rules,  PO2  would  entail  the 
application  of  strict  liability  and  possibly  an  insurance  obligation  to  operators/users  of  AI 
technologies  that  would  otherwise  be  subject  only  to  fault-based  liability  under  national  law.  In 
such  cases,  PO2  could  be  a  disincentivising  factor  for  businesses  choosing  between  AI-enabled 
technologies  and  functionally  equivalent  alternatives  (e.g.  human-driven  sweepers  or  traffic 
management systems relying on merely automated, deterministic software). However, this effect 
is  likely  offset  by  the  cost  reduction  and  internal  market  opportunities  generated  through 
harmonised  liability  rules.  More  specifically,  the  economic  study  found  that  the  moderate 
compliance  costs  linked  to  PO2  would  be  outweighed  by  cost  savings  thanks  to  higher  legal 
certainty,  saved  resources  on  compliance,  and  higher  revenue  enabled  by  a  clearer  and  less 
fragmented  legal  framework.401  Moreover,  the  role  of  insurance  solutions,  as  described  under 

396 Given the high degree of uncertainty regarding the take-up of a non-binding recommendation by MS, there is no 
sufficiently robust basis for a quantified estimate of either victims’ cost savings linked to the burden of proof or the 
additional cost that the potentially liable party might have to advance. 
397 Economic Study, pp. 131, 132, 158-160 and 195. 
398 Ibid., p. 196. 
399 Cf. ibid., pp. 113 et seq., 161 and 163. 
400 Ibid., p. 164. 
401 Ibid, p. 164. 

202 

 
 
 
                                                           
PO1,  is  instrumental,  as  it  limits  potentially  liable  parties’  costs  to  the  insurance  premiums, 
keeping market entry barriers low402.  

In principle, this mechanism holds true whether the relevant risk is covered by voluntary (market-
driven)  insurance  or  a  mandatory  insurance  regime  (either  harmonised  or  regulated  at  national 
level).403 In response to the public consultation, insurance stakeholders have pointed to a potential 
problem with a harmonised insurance obligation for AI-enabled products and services. This was 
linked  to  the  lack  of  statistical  data  on  accidents  and  damages,  which  could  initially  drive  risk 
margins and thus insurance premiums.404 Some SME stakeholders have also raised concerns about 
high  insurance  premiums  due  to  the  difficulties  in  assessing  the  covered  liability  risks.  On  the 
other  hand,  the  fact  the  insurance  industry  is  already  proactively  developing  new  insurance 
products for AI risks405 and that this field is considered by some as the next big growth market406 
support the expectation that coverage will be offered at competitive prices. The parallel initiative 
adapting the PLD to  the digital  age is  key to  enable insurers to  take recourse against producers, 
based on claim subrogation, in particular where defective AI software contributed to the insured 
damage. This can also contribute to keeping premiums low.407  

In any event, the Commission services will respond to the Parliament’s call to work closely with 
the insurance market  to  develop  innovative insurance products408. The Commission  will notably 
facilitate  a  dialogue  between  the  insurance  industry  and  companies  active  in  the  AI  market  (in 
particular SMEs). 

As  start-ups  and  other  SMEs  are  significantly  more  affected  by  the  internal  market  barriers 
created  by  legal  uncertainty  and  fragmentation  (see  2.6.),  this  stakeholder  group  would  also 
benefit to a higher degree from the overall positive economic impacts of PO2.409 

Regarding the effect of PO2 on insurance premiums, it is assumed that: 

- 

- 

a combination of PO1 with a limited strict liability regime applicable to damage caused by 
the  operation/use  of  certain  AI-enabled  products  or  the  provision  of  certain  AI-enabled 
services  with  a  specific  risk  profile  would  cause  an  incremental  increase  by  25%  of 
liability insurance premiums attributable to AI liability risks; 
if these measures were combined, in addition, with an obligation to cover the harmonised 
strict liability regime by insurance, an incremental increase by 35 % of the relevant shares 
of insurance premiums is assumed.  

In addition to the considerations specified under point (a) above (regarding the measures to ease 
the burden of proof under fault-based liability rules), these assumptions are based on the following 
considerations: 

402 Ibid., p. 163.  
403 Ibid., 141. 
404 For instance, Insurance Europe cautioned that “introducing new mandatory insurance requirements would result in 
eliminating the existing cover in voluntary insurance policies and creating new dedicated insurance products tailored 
to  the  specific  requirements  of  the  mandatory  insurance  in  question”.  Further,  Insurance  Europe  submitted  that  the 
insurability of AI-enabled technologies “requires individual risk appraisal and the ability of insurers and insureds to 
be  free to agree insurance terms and conditions suited to the insured’s individual risks. […V]oluntary insurance is 
usually the best solution as it enables insurers and insureds to agree cover that is tailored to individual needs (risk 
profile).” 
405 E.g. ‚Ensure AI‘ by MunichRe, enabling AI providers to guarantee the performance of their AI systems. Cf. also 
La Playa, offering brokerage of specialist AI insurance. 
406 Cf. e.g. Harvard Business Review, The Case for AI Insurance, April 2020. 
407 Cf. Economic Study, pp. 153. 
408 EP, Resolution 2020/2014(INL) on a civil liability regime for artificial intelligence, recital 22. 
409 Cf. Economic Study, pp. 122 and 161. 

203 

 
 
                                                           
-  Where strict liability applies, the effect of re-distributing the burden of bearing the damage 
is  significantly  stronger,  as  the  liable  party  cannot  avoid  liability  even  if  not  at  fault. 
However,  this  regime  would  apply  only  with  respect  to  a  small  number  of  AI-enabled 
products  and  AI-enabled  services,  and  would  therefore  change  the  estimated  impact  on 
insurance premiums only to a limited extent.  

-  Coupling strict liability with an insurance obligation would entail an incrementally bigger 
increase of insurance premiums, as this sub-option of PO2 would preclude to some extent 
the possibility for insurers to manage risks through contractual exclusions and limitations 
of coverage. As confirmed by feedback received during the public consultation, this effect 
of mandatory insurance can have a premium-driving effect in particular if insurers do not 
have sufficient statistical accident data to price premiums with a high degree of accuracy. 
Regarding novel AI-enabled products and services, this issue would primarily be relevant 
during  an  initial  stage  marked  by  scarce  actuarial  data,  but  is  expected  to  dissipate 
gradually  as  data becomes available. Even during the initial  stage, the harmonised safety 
framework  provided  by  the  AI  Act  and  sectoral  safety  legislation  at  EU  level  will  help 
insurers to assess the risks linked to the operation of relevant AI systems.  

Based on the methodology described under point 1.2.(a)(iii)(last indent) above, PO2 would entail 
the following costs through an increase of the insurance premiums attributable to AI liability risks: 

- 

- 

if  the  alleviations  of  the  burden  of  proof  under  fault-based  liability  rules  would  be 
combined  with  a  limited  strict  liability  regime  (only):  EUR 8.89mln  (based  on  the  lower 
estimate of the AI market size) to EUR 26.84mln (based on the higher estimate of the AI 
market size); 
if these were combined, in addition, with an insurance obligation covering strict liability: 
EUR 12.44mln  (based  on  the  lower  estimate  of  the  AI  market  size)  to  EUR 37.57mln 
(based on the higher estimate of the AI market size). 

(b) Victims of damage caused by AI applications with a special risk profile (natural persons 

and businesses) 

A harmonised strict liability  regime would facilitate victims’ access  to  compensation to  an even 
greater  extent  than  alleviations  of  the  burden  of  proof.  Injured  persons  would  have  to  establish 
only  that  the  covered  risk  materialised.410  Within  the  scope  of  strict  liability,  PO2  would  thus 
substantially  ease  the  victim’s  burden  of  proof  and  reduce  associated  costs  (e.g.  for  expert 
analysis). Based on a conservative estimate, it is assumed that PO2 would reduce the costs to be 
advanced by victims  due to  the AI-specific difficulty of meeting the burden of proof by  at  least 
60 %. This assumption is based on the following considerations: 

-  Regarding AI systems not falling under the scope of the harmonised strict liability regime, 
that is to say the vast majority of AI systems, the same considerations as under PO1 apply. 
-  Where  victims  can  invoke  the  harmonised  strict  liability  regime,  they  would  not  bear  a 
significant  cost  linked  to  the  burden  of  proof.  However,  this  would  represent  a  benefit 
compared to the baseline only to the extent that strict liability does not apply also under the 
baseline. Most MS already have strict liability regimes in place for the use of AI-enabled 
products such as autonomous vehicles and autonomous drones. 

Based on this assumption, it is estimated that PO2 would reduce costs to be advanced by victims 
to meet the burden of proof by ca. EUR 2 500 on average. 

410 Economic Study, pp. 127, 128 and 133. 

204 

 
 
 
 
                                                           
In  addition,  a  possible  mandatory  insurance  regime  would  relieve  victims’  of  the  liable  parties’ 
insolvency risk and provide them with an even cheaper, faster and easier path to compensation.411  

(c) 

Consumers 

The  expected  benefits  for  consumers  in  terms  of  a  faster  roll-out  of  AI-enabled  products  and 
services  are  likely  to  be  similar  under  PO2  as  under  PO1,  because  their  common  element  – 
alleviations of the burden of proof under fault-based liability rules – applies to the major part of 
AI-enabled  products  and  services  affected  by  the  initiative.  The  strict  liability  and  mandatory 
insurance  elements  of  PO2  are  much  more  limited  in  scope.  In  respect  of  these  elements,  the 
difficulty  linked  to  specifying,  already  at  the  present  point  in  time,  the  AI-enabled  technologies 
with  a  ‘strict  liability  profile’,  could  lead  to  a  certain  degree  of  legal  uncertainty  which  could 
potentially disincentivise AI rollout in certain cases. Taking into account the cost-mitigating effect 
of  insurance,  it  is  not  expected  that  the  shift,  due  to  strict  liability,  of  compensation  costs  from 
victims to the entities controlling the relevant risk would lead to increased consumer prices. 

(d) 

Insurance companies 

The  harmonisation  of  strict  liability  under  PO2  is  likely  to  entail  some  additional  demand  for 
insurance, in particular when coupled with a compulsory insurance regime, which  generates new 
market opportunities for insurance companies.  

(e) 

Indirect economic impacts and impacts on the competitiveness of the internal market  

While the envisaged strict liability regime may be the most certain and easiest way to ensure that 
the victim does not bear the cost of the damage, it may not in all cases lead to the cost allocation 
on  the  party  which  was  at  the  origin  of  the  damage.  However  if  it  is  coupled  with  mandatory 
insurance, such a cost-allocation will be achieved through subrogated recourse claims.412 In case 
the insurance company of the strictly liable person compensates the victim, a damage claim of the 
victim based on fault or product liability would be re-assigned to the insurance company. On the 
basis of this claim, the insurance company could have recourse against the person at the origin of 
the  damage,  for  instance  against  the  producer  if  the  product  was  defective.  Thereby  the  most 
efficient  cost  allocation  would  be  achieved.413  At  the  same  time,  insurance  coverage  of  liable 
persons  would  limit  the  economic  costs  to  the  annual  insurance  premium  and  keep  the  market 
entries for AI  producers and operators low, while it would be ensured at the same time that the 
victim’s harm would be compensated in a smooth way. The combined impacts of PO1 and 2 are 
expected to have a positive effect on cross-border trade in AI-enabled products and services and 
the development of the European AI-sector as a whole.414  

(f) 

Enforcement, adjudication and litigation costs 

For  similar  reasons  as  under  PO1,  only  small  incremental  impacts  on  enforcement,  adjudication 
and litigation costs are expected under PO2. By dispensing with the need to establish fault and a 
causal link between fault and damage, strict liability considerably reduces the overall evidentiary 
complexity and need for costly technical expertise. More particularly, it is assumed that a fraction 
ranging from 5 % to 60 % of the amount saved by victims due to the eased burden of proof under 

411 This liability risk can be substantial, in particular in the  case of robotics start-ups. Cf.  e.g. Schmelzer,  Why  Are 
Robotics Companies Dying?, 2018; Fresh Consulting, Why Robotics Companies Fail, 2021. 
412 Economic Study, pp. 142, 154 and 156. 
413 This scenario could be extended along the value chain. For example, if the producer has a liability claim against the 
developer, the insurance company of the producer would have a recourse possibility on the basis of this contractually 
subrogated claim against the developer. In the end, the insurance of the developer would pay. 
414 See Economic Study, p. 196 and 197. 

205 

 
 
                                                           
PO2 will have to be advanced by potentially liable businesses415. This assumed range is based on 
the following considerations: 

- 

-  Regarding AI systems not falling under the scope of the harmonised strict liability regime, 
that is to say the vast majority of AI systems, the same considerations as under PO1 apply. 
In cases where PO2 leads to the application of strict liability, the cost savings for victims 
due to the fact that they do not have to prove fault would not be shifted to the defendant 
because  the  latter  cannot  avoid  liability  by  establishing,  possibly  with  the  help  of  costly 
technical expertise, that the damage was not caused by their fault.  

Based on these assumptions, PO2 could entail an increased cost  for potentially liable businesses 
linked to the burden of proof of ca. EUR 100 to ca. EUR 1 500 per case in which the harmonised 
provisions apply. 

To  the  extent  that  risks  within  the  scope  of  the  strict  liability  element  of  PO2  is  not  already 
covered  by  a  national  strict  liability  regime  under  the  baseline,  the  easier  and  more  predictable 
path  to  compensation  afforded  by  this  measure  may  lead  to  a  slight  increase  in  the  number  of 
claims made. Any such possible increase is likely to be marginal. As strict liability would apply 
only in cases involving significant risks to important legal interests (life, health, property), victims 
would in such cases be likely to seek compensation also under the baseline, despite the challenges 
and costs linked to fault-based claims.416 

(g) 

Efficiency score of PO2 assuming a binding legal instrument (Directive) 

For all of the above considerations, PO2 is given an efficiency score of 3, assuming that it will be 
implemented through a binding legal instrument (Directive). Compared to the assessment of PO1, 
this score takes into account, in particular, that the time horizon and modalities of the roll-out of 
technologies with a risk profile relevant for strict liability are not yet known. It would therefore, at 
the present point in time, be somewhat challenging to assess the risk profile of those technologies 
and to specify them in a legislative instrument in a way that ensures a maximum of legal certainty 
regarding the scope of the harmonised strict liability regime. This consideration would be all the 
more relevant with respect to a possible mandatory insurance regime covering strict liability. The 
obligation to ensure insurance coverage is a market entry requirement. Having regard to the policy 
objective  of  promoting  the  roll-out  of  AI-enabled  products  and  services,  it  is  crucial  to  enable 
businesses to assess with a high degree of certainty whether they fall under such a requirement or 
not. 

(h) 

Efficiency score of PO2 assuming a non-binding instrument (recommendation) 

If  the  measures  under  PO2  would  be  merely  recommended  to  MS  in  a  non-binding  way,  the 
expected  benefits  are  expected  to  materialise,  if  at  all,  to  a  much  lesser  extent.  For  the  reasons 
explained  above  in  the  context  of  PO1  (and  in  the  section  on  effectiveness),  this  sub-option 
receives an efficiency score of 1.  

2.3. 

Policy option 3 

415  As  explained  above  in  the  assessment  of  PO1,  the  policy  options  are  not  expected  to  entail  additional  litigation 
costs  for  private  persons  as  potentially  liable  parties.  The  additional  elements  of  PO2  (strict  liability  and  possibly 
mandatory insurance) would in any case apply only vis-à-vis businesses as potentially liable parties. 
416 Ibid., p. 61. 

206 

 
 
                                                           
During the 1st stage, PO3 would have the same impacts as PO1. As MS anyway have to report on 
the implementation of the initiative in line with better regulation requirements on evaluation and 
monitoring, targeted reporting requirements supporting the review mechanism under PO3 would 
not entail a significant burden for them. While the potential benefits of a harmonised strict liability 
regime would materialise later than under PO2, the following factors will improve the efficiency 
of the more far-reaching measures (strictly liability, possibly coupled with mandatory insurance) 
potentially taken at the second stage:  

-  By ensuring that the assessment of a possible strict liability regime for the use of AI-enabled 
technologies can rely on a more developed factual basis, the staged approach minimises the risk 
of creating an uneven playing field for AI-enabled technologies. 

-  The  staged  approach  allows  the  development  of  tailored  market-driven  insurance  solutions, 
which can be taken into account at the second stage when assessing the need for and effects of 
a  mandatory  insurance  regime.  Moreover,  as  AI-technologies  potentially  subject  to  such  a 
regime are rolled out over the coming years and statistical accident data is being accumulated, 
the  lack  of  actuarial  data  as  main  potential  cost-driver  of  AI-specific  liability  insurance  will 
have become considerably less relevant by the time of the targeted review under PO3.  

As relevant safety standards for the AI-enabled technologies will be available by the time of the 
targeted  review,  the  conditions  for  assessing  liability  risks  will  have  improved  for  both  insurers 
and liable users/operators. Given that the possible harmonisation of strict liability and mandatory 
insurance for certain AI-enabled products and services with a specific risk profile would be taken 
only at the stage of the targeted review, the same incremental increase of insurance premiums is 
assumed as for PO1 (EUR 5.35mln to EUR 16.1mln). Likewise the same cost savings for victims 
linked  to  the  alleviated  burden  of  proof  (ca.  EUR 2 000  per  case  on  average)  and  the  same 
incremental cost increase linked to the burden of for liable businesses (ca. EUR 200 to EUR 1 600 
per case) are expected. 

These considerations lead to an  efficiency score of 4 for PO3, assuming it will be implemented 
through a binding legal instrument (Directive). The non-binding sub-option is scored consistently 
with the other two policy options, for the reasons explained above in the context of PO1 (and in 
the section on effectiveness). 

2.4.  Comparative overview of efficiency scores 

The  following  table  shows  the  policy  options’  respective  efficiency  scores,  reflecting  the  ratio 
between all relevant costs and benefits for the affected stakeholder groups, as summarised in the 
mapping tables at the beginning of this Annex. 

Policy Option  

Efficiency scores 
(impacts net of the baseline (-5 to +5) 

Policy Option 1 

Policy Option 2 

Policy Option 3 

4 (binding) 
1 (non-binding) 
3 (binding) 
1 (non-binding) 
4 (binding) 
1 (non-binding) 

3. 

Coherence 

3.1. 

Policy option 1 

207 

 
 
 
Option 1 would be coherent with the – not AI-specific – measures envisaged in the framework of 
the  PLD  review.  These  instruments  are  complementary  as  they  address  challenges  posed  by 
emerging digital technologies with respect to claims based on different grounds, directed against 
different  liable persons  and covering compensation for different  victims  and types of harm. The 
respective  instruments  use  a  consistent  approach  and  similar  tools  (access  to  information, 
adaptations of the burden of proof) to ensure in their combination that products or services using 
AI or other digital technologies do not make it more difficult under any of the existing pillars of 
liability  to  get  compensation  compared  to  traditional  products.  The  envisaged  measures  will 
together  contribute  to  creating  a  more  consistent  and  adequate  civil  liability  framework  for  the 
digital economy, without upsetting the balance established by the existing rules. 

Option 1 would also be coherent with the proposals already adopted as part of the follow-up to the 
AI White Paper, in particular the AI Act.  It would namely take over definitions of key concepts 
from those acts, such as ‘AI-system’, ‘provider’ and ‘user’, although additional criteria would be 
necessary  to  ensure  that  the  envisaged  alleviations  of  the  burden  of  proof  apply  only  where  the 
specific  characteristics  of  AI  effectively  challenge  existing  liability  rules.  The  provisions  on 
disclosure  of  information  and  presumptions  of  causality  would  build  specifically  on  the 
requirements  of  the  AI  Act.  Option  1  would  thus  complement  this  act  providing  an  additional 
incentive  for  ensuring  the  safety  of  AI-enabled  products  and  services  as  well  as  the  respect  of 
fundamental rights. 

Finally, MS could fit the alleviations of the burden of proof envisaged under Option 1 into their 
national  liability  regimes  without  disrupting  their  respective  legal  traditions,  because  this  policy 
option allows for sufficient  flexibility and is  based on tools that are already well-known in  MS’ 
civil liability systems. 

For  these  reasons,  policy  option  1  is  given  a  coherence  score  of  4,  assuming  that  it  would  be 
implemented by means of a binding legal instrument. 

While  a  non-binding  instrument  would  be  even  less  intrusive  and  would  allow  MS  maximum 
flexibility in assessing whether and how to adjust their national rules, this approach would not be 
coherent  with the Commission’s overall AI policy, which aims at  creating an ecosystem of trust 
and  promoting  the  roll-out  of  AI  in  the  internal  market  through  reliable  and  harmonised  legal 
framework. Therefore, this sub-option is given a coherence score of 3. 

3.2. 

Policy option 2 

Policy option 2 would be coherent with the measures envisaged in the PLD review. In particular, 
the  criteria  defining  the  operator/user  for  the  purposes  of  the  harmonised  strict  liability  regime 
would  be  different  from  those  defining  the  producer  under  the  revised  PLD.  In  cases  where  the 
producer is nevertheless at the same time also the user/operator, the fact that they would be subject 
to the revised PLD and the harmonised strict liability regime for AI would not lead to inconsistent 
results  because  these  liabilities  would  intervene  in  different  capacities.  In  any  event,  such 
scenarios  exist  already  in  the  national  laws  where  strict  liability  and  product  liability  apply  in 
parallel. 

Strict liability of the user/owner of technologies with a specific risk profile is a widely known and 
established  approach  in  national  laws.  Option 2  would  thus  be  coherent  with  the  approaches  of 
existing liability systems. However, in cases where the use of AI-enabled technologies would fall 
under a different liability regime than the use of functionally equivalent non-AI technologies, the 
coherence of Option 2 with the existing legal framework could be questioned. 

Regarding the other strands of the Commission’s AI policy, in particular the AI Act, it needs to be 
considered  that  the  already  proposed  measures,  aimed  at  ensuring  safety  and  an  effective 

208 

 
 
protection  of  fundamental  rights,  do  not  apply  directly  to  some  of  the  AI-enabled  products  that 
could  fall  under  a  strict  liability  regime  (e.g.  autonomous  vehicles  and  drones).  Safety  rules 
specify the duties of care to be fulfilled by various actors with a view to preventing harm. These 
rules  thus  provide  an  important  framework  for  assigning  liability  based  on  the  control  over  the 
risks that safety rules aim to minimise. Conversely, liability rules provide an additional incentive 
to  comply  with  safety  rules  and  fundamental  rights.  Given  these  complementary  functions  of 
safety  and  liability  rules,  it  would  be  more  conducive  to  the  coherence  of  the  overall  EU 
regulatory framework for AI if the relevant safety rules for AI-enabled technologies with a ‘strict 
liability  profile’  were  already  in  place  at  the  time  the  harmonised  strict  liability  regime  for  the 
use/operation of these technologies is laid down. 

These considerations lead to a coherence score of 3, assuming that it would be implemented by 
means  of  a  binding  legal  instrument.  The  non-binding  sub-option  is  given  the  same  coherence 
score  because,  on  the  one  hand,  it  ensures  more  flexibility  and  allows  MS  to  avoid  any 
inconsistencies  in  their  national  legal  systems,  but  on  the  other  hand,  it  would  not  be  entirely 
coherent with the Commission’s overall AI policy, as explained in the context of Option 1 above. 

3.3. 

Policy option 3 

Option 3 would insert  itself without friction into the  existing liability system (PLD  and national 
rules),  and  be  consistent  also  with  the  other  strands  of  the  Commission’s  AI  policy.  The  staged 
approach  would  allow  the  Commission  to  take  stock  of  the  practical  effect  of  the  planned 
adaptations to the PLD, and in particular the extension of product liability to providers of safety-
relevant AI-systems, before deciding on the need to  harmonise strict liability of users/operators. 
AI-specific  requirements  applicable  to  AI-systems  used  in  technologies  with  a  typical  ‘strict 
liability profile’ (in particular transport vehicles and aircraft) are likely to be gradually integrated 
into the ‘old approach’ safety legislation in step with technological developments. Requirements 
of  the  AI  Act  (and  also  the  Machinery  Products  Regulation)  do  not  currently  apply  to  such 
technologies.  The targeted review mechanism  for strict liability  and mandatory  insurance would 
allow the Commission to take into account regulatory developments at national and EU level over 
the  following  years,  and  thus  to  ensure  coherence  also  with  respect  to  future  AI-related  policy 
measures beyond the proposed AI Act. This may for instance concern future safety rules tailored 
to  autonomous  vehicles  and  highly  autonomous  AI-enabled  drones,  in  line  with  the  EU’s 
complementary  approach  to  safety  and  liability  rules.  These  considerations  lead  to  a  coherence 
score of 4 for policy option 3, assuming that it would be implemented by means of a binding legal 
instrument.  The  non-binding  sub-option  receives  a  coherence  score  of  3,  for  the  same 
consideration as set out in the context of PO1 above. 

3.4. 

Indirect environmental impacts 

As regards indirect environmental impacts, all policy options are expected to contribute – albeit to 
a non-quantifiable extent – to the uptake of AI applications that are beneficial for the environment. 
For  instance,  AI  systems  used  in  process  optimisation  make  processes  less  wasteful  (e.g.  by 
reducing  the  amount  of  fertilizers  and  pesticides  needed,  decreasing  the  water  consumption  at 
equal  output, etc.).  AI systems supporting improved  vehicle automation  and traffic management 
contribute to the shift towards cooperative, connected and automated mobility, which in turn can 
support more efficient and multi-modal transport, lowering energy use and related emissions. 

3.5.  Comparative overview of coherence scores 

Policy Option  

Coherence scores 

209 

 
 
 
Policy Option  

Coherence scores 

Policy Option 1 

Policy Option 2 

Policy Option 3 

4 (binding) 
3 (non-binding) 
3 (binding) 
3 (non-binding) 
4 (binding) 
3 (non-binding) 

4. 

Proportionality 

4.1. 

Policy option 1: 

Option  1  is  limited  to  the  measures  strictly  necessary  to  address  the  AI-specific  problems 
identified. In particular, it would not touch upon the substantive conditions of liability like fault or 
causality, but focus on targeted proof-related measures ensuring that victims have the same level 
of  protection  as  in  cases  not  involving  AI.  In  this  way,  Option  1  is  strictly  aligned  with  the 
Commission’s targeted approach of ensuring that victims are not less, but also not more protected 
due  to  the  involvement  of  AI.  Any  shifts  in  the  risk  and  cost  distribution  between  affected 
stakeholders that would go beyond counter-balancing the specific proof-related challenges of AI 
would  thus  be  avoided,  and  MS’  well-established  liability  systems  would  be  respected  to  the 
maximum  extent  possible.  However,  while  proportionality  requires  that  the  mildest  option 
suitable  to  achieve  the  relevant  objectives  is  chosen,  there  is  presently  still  a  degree  of 
uncertainty as to whether the envisaged measures to ease the burden of proof will in all cases be 
sufficient to prevent compensation gaps. These considerations lead to an overall proportionality 
score  of  4,  assuming  that  PO1  would  be  implemented  by  means  of  a  binding  legal  instrument. 
While a non-binding approach would be even less interfering into the existing legal framework, it 
receives a slightly lower proportionality score (2) because it is much less suitable to achieve the 
policy objectives. 

4.2. 

Policy option 2 

Option 2 would in principle be suitable and effective to achieve the policy objectives. However, as 
there is still a certain level of uncertainty about whether, despite the alleviations of the burden of 
proof envisaged under Options 1, the absence of a harmonised strict liability regime for AI users 
would indeed  entail  liability  gaps over the baseline period, the strict proportionality of Option 2 
cannot  be  affirmed  with  certainty  at  this  point  in  time.  In  particular,  it  can  currently  not  be 
confirmed  with  certainty  that  policy  option  2  does  not  go  beyond  the  minimum  intervention 
necessary to address the relevant issues. Given the more far-reaching nature of a harmonised strict 
liability  regime  compared  to  measures  easing  the  burden  of  proof,  policy  option  2  therefore 
receives  a  proportionality  score  of  3.  The  non-binding  sub-option  is  scored  consistently  with 
PO1, as the same limitations apply. 

4.3. 

Policy option 3 

Option 3 would keep immediate measures to the strict minimum necessary to address the proof-
related  challenges  posed  by  AI  under  national  –  primarily  fault-based  –  liability  rules,  leaving 
other aspects to national law and the technology-neutral provisions of the PLD. At the same time, 
this  option  would  ensure  that  future  technological,  regulatory  and  jurisprudential  developments 
will be systematically taken into account to verify the need to harmonise strict liability for certain 
uses of AI. The staged approached is thus aligned with the principle of proportionality to a very 
large  extent,  although  it  cannot  be  ruled  out  that  the  measures  enacted  at  the  first  stage 
(alleviations of the burden of proof) are not in all cases sufficient to achieve the policy objectives 

210 

 
 
 
 
(proportionality  score  of  4).  The  non-binding  sub-option  is  scored  consistently  with  the  other 
two policy options, as the same limitations apply. 

4.4.  Comparative overview of proportionality scores 

Policy Option  

Proportionality scores 

Policy Option 1 

Policy Option 2 

Policy Option 3 

4 (binding) 
2 (non-binding) 
3 (binding) 
2 (non-binding) 
4 (binding) 
2 (non-binding) 

5. 

Overall results of the multi-criteria analysis and comparison of options 

The following tables summarise the results of the impact analysis, and show how the options were 
compared following a multi-criteria analysis methodology. As the sub-option of implementing the 
policy  measures  through  a  non-binding  instrument  (recommendation)  consistently  scored  lower 
across  the  IA  criteria,  it  can  be  discarded  for  the  purposes  of  this  comparison.  A  binding 
legislative approach (most likely a Directive) is assumed for the comparison. 

5.1.   Simple aggregation of scores 

Firstly, the following impact matrix presents the scores using simple aggregation and assuming an 
equal weight of each individual criterion. As three out of six individual criteria would come under 
the umbrella of effectiveness,  effectiveness  is  de facto  given more importance than the other  IA 
criteria. 

The assessment has not lead to a preference between PO1 and PO3, which is consistent with the 
fact  that  they  involve  the  same  harmonising  provisions  at  the  present  stage.  The  feature 
distinguishing these options, the targeted review mechanism, does not lead to different impacts of 
the implemented measures to be implemented at this first stage. PO2 scores slightly lower in terms 
of its effectiveness to achieve specific objective 1 (increase legal certainty regarding liability for 
AI),  coherence  and  proportionality,  leading  to  a  marginally  lower  overall  score.  This  ranking  is 
linked primarily to the fact that PO2 entails the need, at the present stage, to assess the risk profile 
and  operational  characteristics  of  specific  types  of  AI-enabled  products  and  services  in  order  to 
determine and specify with a high degree of precision the products and services falling under the 
harmonised strict liability regime. As the relevant products and services have not reached market 
maturity  yet  and  the  conditions  for  their  operation,  e.g.  the  stakeholders  in  charge  of  ensuring 
human  supervision,  operational  environments,  safety  constraints,  etc.,  are  not  yet  known,  this 
situation  potentially  engenders  some  uncertainty.  This  potential  lack  of  certainty  would  be 
particularly  pertinent  if  the  harmonised  strict  liability  regime  were  to  be  coupled  with  the 
obligation to ensure insurance coverage. Such an obligation acts as a market entrance requirement. 
With regard to the policy objective to promote the roll-out and take-up of AI-enabled products and 
services, it is therefore especially important that businesses can know with certainty whether they 
fall under such a regime or not. 

MCA (simply aggregation) 

Criterion 

211 

Score net of the baseline (scale of -5 to +5) 
Option 2 

Option 3 

Option 1 

 
 
 
 
Effectiveness 
Specific objective 1 
Specific objective 2 
Specific objective 3 
Efficiency 
Coherence 
Proportionality 

 Simple sum 
 Ranking based on simple aggregation 

4 
4 
4 
4 
4 
4 

24 
1 

3 
4 
4 
4 
3 
3 

21 
2 

4 
4 
4 
4 
4 
4 

24 
1 

5.2. 

Sensitivity analysis based on neutral weighting of impact assessment criteria 

Secondly,  a  weighted  sum  method  was  applied  to  analyse  the  sensitivity  of  the  MCA.  This 
sensitivity  analysis  neutralises  the  number  of  sub-criteria,  giving  equal  weight  to  overall 
effectiveness, efficiency, coherence and proportionality. 

As shown in the following table, PO1 and PO3 are confirmed as the highest ranking options. As 
the  sensitivity  analysis  shifts  the  weighting  from  effectiveness  to  the  other  IA  criteria,  the 
difference compared to PO2 is more distinct based on this method. This is due to the fact that PO2 
scored slightly lower in terms of proportionality and coherence. 

MCA ((based on an equal weight of the IA criteria)) 

Criterion 

Effectiveness 
Specific objective 1 
Specific objective 2 
Specific objective 3 
  Overall effectiveness 

Efficiency 
Coherence 
Proportionality 

 Simple sum 
 Weighted sum of scores (=effectiveness/3 + efficiency + coherence 
+ proportionality)/4 
 Ranking based on the weighted sum of scores 

5.3. 

Sensitivity analysis based on weighted scores 

Score net of the baseline (scale of -5 to +5) 
Option 2 

Option 3 

Option 1 

4 
4 
4 
12 
4 
4 
4 

24 
4 

1 

3 
4 
4 
11 
4 
3 
3 

21 
3,42 

3 

4 
4 
4 
12 
4 
4 
4 

24 
4 

1 

Thirdly, further sensitivity analysis was done by attaching different weights to individual criteria. 
Given the political priorities of the Commission, as well as Member States’ tendency to minimise 
interference into their traditional civil law systems, a higher weight was attached to effectiveness, 
coherence and proportionality. 

As  shown  in  the  following  table,  the  ranking  based  on  the  previous  methods  of  comparison  is 
confirmed.  Options  1  and  3  are  most  suitable  to  achieve  the  Commission’s  political  objectives, 
while  taking  into  account  Member  States’  emphasis  on  considerations  of  proportionality  and 
coherence. 

MCA (based on an weighting taking into account political priorities) 

Criterion 

Weight 

Score net of the baseline (scale of -5 to +5) 
multiplied by the weight 

212 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Effectiveness  
Specific objective 1 
Specific objective 2 
Specific objective 3 
Efficiency 
Coherence 
Proportionality 

0,1 
0,1 
0,1 
0,1 
0,3 
0,3 

 Sum of weighted scores 
 Ranking based on the weighted scores 

Option 1 

Option 2 

Option 3 

0,4 
0,4 
0,4 
0,4 
1,2 
1,2 

4 
1 

0,3 
0,4 
0,4 
0,4 
0,9 
0,9 

3,3 
3 

0,4 
0,4 
0,4 
0,4 
1,2 
1,2 

4 
1 

5.4.  Overall conclusions on the ranking of options 

The multi-criteria analysis provides a clear ranking between PO1/PO3 and PO2, confirming that it 
is preferable not to lay down a harmonised strict liability regime for certain types of AI-enabled 
products and services at the present time, whether or not coupled with strict liability. This result is 
also corroborated by the consideration that a clear majority of business stakeholders opposed strict 
liability in the public consultation. These stakeholders’ views are of particular relevance given the 
Commission’s policy objective to promote the rollout of AI-enabled products and services.  

In  order  to  inform  the  necessary  political  decision  between  PO1  and  PO3,  it  is  important  to 
elaborate on other factors of relevance for that decision, and notably to consider how well these 
options  take  into  account  the  suggestions  of  the  European  Parliament  as  well  as  stakeholder 
opinions. In this respect, firstly, only the staged approach (PO3) incorporates both main elements 
suggested by the Parliament – a facilitated burden of proof under fault-based liability rules and a 
limited  strict  liability  regime  for  certain  AI-enabled  technologies  –  explicitly  into  the  proposed 
legislative  instrument.  While  the  strict  liability  element  is  not  yet  implemented  at  the  present 
stage,  the  targeted  review  mechanism  provides  a  dedicated  framework  preparing  the  ground  for 
the  future  policy  decision  on  this  element.  Secondly,  by  explicitly  acknowledging  the  possible 
need  for  a  harmonised  strict  liability  regime  in  the  proposed  legislative  provisions,  PO3  also 
satisfies to a greater extent the opinions expressed by non-business stakeholders, a large majority 
of whom supported the harmonisation of strict liability.  

If, at the stage of the targeted review, a political decision is taken to propose a harmonised strict 
liability regime, this measure could be designed in a way to meet concerns expressed by business 
stakeholders, which are at present sceptical towards it. 

In  light  of  these  considerations,  the  staged  approach  (PO3)  is  the  most  balanced,  politically 
feasible, proportionate and yet effective option. It is most suitable to deliver the desired economic 
benefits  in  terms  of  roll-out  of  AI-enabled  products  and  services  in  the  internal  market,  and  to 
increase  citizens’  trust  in  AI  by  ensuring  that  victims  who  suffered  harm  caused  with  the 
involvement  of  AI  systems  enjoy  the  same  level  of  protection  as  victims  who  suffered  harm 
caused  by  other  technologies.  It  is  also  most  adapted  to  the  political  context  of  the  AI  liability 
initiative,  including  the  Parliament’s  legislative  own-initiative  resolution,  and  stakeholder 
feedback. 

213 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 11  

The economic impact of adapting civil liability rules to the specific 
challenges of AI 

 The example of the European market for vacuum cleaners 

Bruno Carballa-Smichowski and Nestor Duch-Brown 

February 2022 

JRC Digital Economy Working Paper 2022-01 

1. 

INTRODUCTION 

In its White Paper on artificial intelligence (AI), the Commission set the objectives of promoting 
the uptake of AI and addressing the risks associated with certain of its uses. As part of the latter, 
one  of  the  specific  objectives  of  the  White  Paper  is  to  ensure  the  same  level  of  protection  for 
persons having suffered  harm caused by AI systems  and persons having suffered harm by other 
technologies.  In  the  report  on  AI  liability  accompanying  the  White  Paper,  the  Commission 
recognised  specific  challenges  posed  by  AI  to  current  liability  rules.  The  Commission  Work 
Programme  2020  foresaw  a  follow-up  to  the  White  Paper  on  AI  in  the  form  of  new  legislative 
initiatives, including on liability.  

Against  this  backdrop,  DG  JUST  is  proposing  an  initiative  to  adapt  civil  liability  rules  to  the 
specific challenges posed by artificial intelligence (“the initiative” hereafter). The initiative details 
the problem stemming from a lack of intervention in the baseline scenario as well as three policy 
options and their respective expected effects.  

The objective of this report is to contribute to the impact assessment of the initiative by providing 
empirical  evidence  of  the  economic  impacts  of  the  main  intervention  common  to  all  policy 
options: an alleviation of victims’ burden of proof (“the intervention” hereafter). In line with this 
purpose,  the  report  focuses  on  the  application  of  national  fault-based  liability  rules  to  damage 
caused  with  the  involvement  of  AI-systems.  This  scope  is  consistent  with  the  other  studies 
supporting the impact assessment of the initiative (Deloitte, 2021; Kantar, 2020). The report does 
in particular not cover the review of the Product Liability Directive, which is subject to a separate 
impact  assessment addressing more  general  (i.e.  not  AI-specific)  issues in relation  to  producers’ 
liability for damage caused by defective products. 

Focusing  on  the  vacuum  cleaners  market,  the  report  estimates  first  the  baseline  scenario,  which 
captures the economic impact of two problems: legal uncertainty regarding the liability exposure 
of the operator and lack of consumer trust on AI-enabled robot vacuum cleaners (RVC). Then, it 
simulates the impact of three expected effects of the intervention: an increase in demand for due to 
increased  legal  certainty  regarding  liability  exposure,  increased  consumer  trust  in  AI-enabled 

214 

 
 
products  and  a  re-distribution  of  compensation  costs  due  to  the  prevention  of  AI-induced 
compensation gaps. 

The remainder of the report is structured as follows. Section 2 describes the data used to evaluate 
the impact of the intervention on the vacuum cleaners market. Section 3 explains the methodology 
used to the estimate demand for vacuum cleaners and, on those basis, simulate the impact of the 
intervention on that market. Section 4 presents the results. 

2. 

THE IMPACTS TO BE EVALUATED 

The  initiative  considers  a  series  of  problems  that  take  place  in  the  absence  of  intervention 
(baseline scenario) and proposes three policy options and describes the expected impacts they will 
have. In this section, we briefly describe only the economic effects of those problems and policy 
measures that, given the time and data availability constraints, we evaluate in this report.  

Problem 1: legal (un)certainty 

AI poses two types of difficulties to the interpretation of liability rules. First, in the absence of AI-
specific  legislation  on  the  matter,  national  courts  might  decide  to  either  employ  the  traditional 
allocation of the burden of proof (which means that the burden to establish the liable party’s fault 
and the causal link between that fault and the damage is usually fully borne by the victim), or to 
adapt it to the specificities of AI.  Second, it is difficult to establish the fault of a person when AI-
enabled products are involved. This is because, in these cases, it is very difficult to determine the 
standard  of  care.  Even  if  it  is  established,  the  specific  characteristics  of  certain  AI  systems 
(autonomous behaviour, opacity/lack of transparency, complexity, limited predictability) make it 
highly  uncertain  whether  and  how  the  victim  can  identify  and  prove  a  behaviour  that  failed  the 
objective  standard.  This  generates  legal  uncertainty  as  to  how  existing  liability  regimes  will  be 
applied in the case of damage caused by AI-enabled products and services. 

Legal uncertainty is expected to have various negative economic impacts. For the purpose of this 
report,  we  focus  on  one:  lower  willingness  to  take-up  AI-enabled  products  and  services.  In  the 
presence  of  legal  uncertainty,  it  is  expected  that  businesses  and  consumers  demand  less  AI-
enabled products and services than in absence of it.  

Problem 2: lack of consumer trust 

The  initiative  presents  evidence  of  a  current  lack  of  consumer  trust  on AI-enabled  products  and 
services.  This  is  rooted  in  two  major  concerns  consumers  have  regarding  these  products  and 
services: an expected low likelihood of receiving compensation if AI applications cause damage 
and concerns regarding the allocation of responsibility and liability if something goes wrong. As a 
result, consumers’ demand for AI-enabled products and services is restrained.  

Intervention: easing victims’ burden of proof for AI-related claims 

All  the  policy  options  include  targeted  and  risk-based  measures  to  ease  the  victim’s  burden  of 
proof.  The  objective  is  to  do  a  harmonised  adaptation  of  member  state’s  national  rules  that 

215 

 
 
 
determine  the  possibility  for  injured  parties  to  meet  the  burden  of  proof.  This  alleviation  will 
consist of three measures: 

(i) 

(ii) 

Harmonised  rules  on  the  possibility  for  national  courts  to  order  the  disclosure  of 
relevant  information  to  be  recorded  or  documented  pursuant  to  the  AI  Act.  National 
courts would ensure confidentiality, proportionality and the protection of both parties’ 
legitimate  interests  (e.g.  intellectual  property  rights,  trade  secrets).  In  the  case  of  the 
refusal  or  inability  to  comply  with  an  order  to  disclose  the  information,  a  rebuttable 
to 
presumption 
prove based on that information would apply. 

sought 

victim 

facts 

that 

the 

the 

of 

A rebuttable presumption, for the purposes of fault-based claims under national law, of 
the  causal  link  between  the  non-compliance  with  the  applicable  standard  of  care 
(=fault) and the damage for which compensation is sought, if the claimant shows that 
the  defendant  did  not  comply  with  AI  Act  requirements  designed  to  prevent  such 
damage. 

(iii)  An  alleviation  of  the  victim’s  burden  of  proof,  for  the  purposes  of  national  civil 
liability  claims,  to  ensure  that  the  victim  does  not  bear  the  burden  of  demonstrating 
how or why an AI-system reached a certain output. This could for instance be achieved 
through the legislative technique of a rebuttable presumption. 

These measures should provide legal certainty to businesses and consumers by clarifying how the 
proof-related challenges of AI are to be handled by national courts and the conditions for meeting 
the  burden  of  proof.    As  a  result,  the  valuation  of  AI-enabled  products  by  firms  and  consumers 
should increase, which should boost their demand.  

The  measures  should  also  allow  injured  parties  to  use  their  existing  civil  liability  claims 
effectively in cases involving AI. In that manner, they would tackle one of the roots of the lack of 
consumer trust: the low likelihood of compensation in case of AI-generated damage. This should 
result  in  an  increase  in  demand  for  AI-enabled  products.  Note  that  a  corollary  effect  of  the 
intervention  is  that  compensation  for  AI-caused  damage  born  by  the  defendant  should  be  more 
likely.  We  will  come  back  to  this  point  in  Section  4.4.3.  It  is  however  appropriate  to  specify 
already  at  this  stage  that  this  result  materialises  in  cases  where  the  specific  characteristics  of 
certain  AI  systems  would  not  have  allowed  the  victim  to  prove  the  necessary  facts  under  the 
baseline scenario. Only in these cases, the intervention would shift the cost of compensating the 
relevant  damage  from  the  victim  to  the  liable  person,  increasing  the  latter’s  liability  exposure. 
Likewise, victims  would be relieved of some of  the costs linked to  meeting the burden of proof 
(e.g.  costs  of  expert  analysis).  These  re-distribution  effects  are  inherent  in  the  Commission’s 
policy objective to ensure that victims of damage caused with the involvement of AI systems have 
the  same  level  of  protection  as  victims  of  damage  caused  by  other  technologies,  and  in  general 
with  the  purpose  of  liability  law.  These  impacts  lead  to  a  more  efficient  cost-allocation  to  the 
person  best  placed  to  prevent  damage  from  occurring.  Moreover,  the  potentially  liable  party  is 
much more likely to have the necessary knowledge of the relevant AI systems in-house, and thus 
to  discharge  the  burden  of  proof  more  efficiently  without  the  need  to  procure  external  technical 

216 

 
 
 
 
 
expertise.  These  re-distribution  effects  are  therefore  not  regarded  as  an  undesirable  impacts  or 
undue burden in the impact assessment.  

3. 

DATA 

We use panel data on the sales of household vacuum cleaners provided by GfK, one of the major 
providers  of  data  and  analytics  on  consumer  goods.  The  data  covers  the  years  2015-2019  for  6 
European Union countries: Belgium, Germany, Greece, Ireland, Poland and Slovakia and includes 
all the brick-and-mortar and online sales channels. Each vacuum cleaner is described by its brand 
and model. The unit of observation is a vacuum cleaner (brand-model), country and year. 

For each observation, we observe quantity sold, total sales and several observable characteristics: 
type of vacuum cleaner (e.g. handstick, robot, etc.), maximal wattage, battery type, whether it has 
a control handle, whether it has an EPA filter, the motor position, the noise level, whether it has a 
power  brush,  the  type  of  power  supply,  whether  it  has  smart  connect,  whether  it  has  special 
provisions for animals, the type of dust collector and the type of EPA filter, if any. 

Around 12% of the observations of the original dataset correspond to unidentified models. These 
are  private  labels  sold  by  big  retailers  that  were  eliminated  during  the  data  cleaning  process. 
During  the  latter,  vacuum  cleaners  were  classified  into  5  categories  (robot,  cylinder,  upright 
handstick  and  handheld)  and  9  subcategories  based  on  their  type.  In  order  to  reduce  the 
computational burden of the estimation, only brands and models summing up to 99% of the sales 
were  retained.  Finally,  we  computed  average  prices  from  all  sales  channel  and  eliminated 
observations with outlier values, namely average prices below 25€ and above 1 000€. The cleaned 
dataset used for the estimation contains 28 373 observations. 

Tables 1 shows the share of robot vacuum cleaners in total quantities by country and year. 

Table 1: Percentage of robot vacuum cleaners over total quantities of vacuum cleaners sold 

Year 

Country 
Belgium  Germay  Greece  Ireland  Poland  Slovakia 

2015 
2016 
2017 
2018 
2019 

8.9% 
8.4% 
9.1% 
9.9% 
10.8% 

6.9% 
7.6% 
8.5% 
10.4% 
10.9% 

1.0% 
0.8% 
1.1% 
1.9% 
3.5% 

1.3% 
1.0% 
1.3% 
1.8% 
1.1% 

6.9% 
7.3% 
8.4% 
9.6% 
12.0% 

7.3% 
8.5% 
8.6% 
9.4% 
11.8% 

As Tables 1 shows, the penetration of robot vacuum cleaners shows an increasing tendency in the 
2015-2019 period for all the countries studied. This confirms the increasing relevance of artificial 
intelligence  in  the  vacuum  cleaners  market  and  hence  of  potential  third-party  liability  issues 
addressed by the initiative. 

Table  2  below  provides  summary  statistics  of  the  variables  retained  for  the  demand  estimation 
described in Section 4. 

217 

 
 
 
 
 
Table 2: Summary statistics of the variables retained in the nested logit model 

Variable 

aprice 
q_total 
control 
motor 
powerbrush 
rech 
smart 
animal 
bagless 
epa 
robot 

Mean 
173 
2.04 
0.05 
0.11 
0.11 
0.31 
0.02 
0.12 
0.44 
0.40 
0.08 

Std. Dev. 
144 
7.68 
0.21 
0.32 
0.31 
0.46 
0.15 
0.32 
0.50 
0.49 
0.27 

Min 
25 
0.001 
0 
0 
0 
0 
0 
0 
0 
0 
0 

Max 
1000 
292.164 
1 
1 
1 
1 
1 
1 
1 
1 
1 

NB:  “aprice”  stands  for  “average  price”  and  “q_total”  for  “total  quantities”.  Variables  “control”,  “motor”, 
“powerbrush”,  “rech”,  “animal”,  “bagless”,  “epa”  and  “robot”  are  dummy  variables  describing  technical 
characteristics  

The  main  two  limitations  of  the  dataset  stem  from  its  nature.  First,  none  of  the  policy  options 
involves  strict  liability  for  household  robot  vacuum  cleaners.  Hence,  we  can  only  analyse  the 
impact  of the measures aimed to  the ease of victims’ burden of proof under fault-based liability 
rules  (which  is  common  to  all  policy  options),  but  not  that  of  the  application  of  strict  liability, 
which  is  one  of  the  measures  forming  part  of  option  2.  Hence,  the  dataset  does  not  allow  us  to 
analyse the different impact of each policy option, only that of any policy option in comparison to 
the  baseline  scenario.  Second,  given  that  the  dataset  refers  to  household  vacuum  cleaners  (as 
opposed  to  industrial  vacuum  cleaners  used  by  professional  service  providers),  we  can  only 
estimate  households’  demand.  This  circumscribes  the  analysis  to  cases  implying  lower  risks  of 
third  party  liability  than  those  faced  by  professional  service  providers.  However,  these  risks  are 
not inexistent, as illustrated in Section 4.1.  

The richness of the database in terms of its temporal and country coverage, its exhaustivity and the 
technical  characteristics  of  the  vacuum  cleaners  covered,  allow  to  circumscribe  the  baseline 
scenario with high precision, which is the cornerstone of any impact analysis of an intervention.  

4. 

METHODOLOGY 

This  section presents  the methodology used to  estimate demand for the baseline scenario and to 
build the counterfactuals used to assess the impact of the intervention. 

Note  that,  regarding  the  relevant  liability  regimes,  the  analysis  for  which  the  methodology  is 
described  in  this  section  focuses  on  issues  and  policy  solutions  concerning  the  application  of 
national  fault-based  liability  rules  to  damage  caused  with  the  involvement  of  AI-systems.  It  is 
designed  to  complement  the  studies  supporting  the  impact  assessment  on  AI  Liability  (Deloitte, 
2021;  Kantar,  2020),  which  had  the  same  scope.  Therefore,  it  does  in  particular  not  cover  the 
Product  Liability  Directive,  which  harmonises  producers’  liability  based  on  defects  in  their 
products. 

218 

 
 
 
 
4.1. 

Setting 

We consider a three-agent setting summarized in the figure below. 

Figure 1: The setting analyzed 

In this setting, final consumers can expose third parties to three types of damages by operating AI-
enabled robot vacuum cleaners: 

  Harm to persons (e.g. stuck hair, harming a toddler, electrocution, stumbling, etc.)417   
  Harm  to  property  (e.g.,  damaging  pets,  damaging  jewellery,  damaging  electronics, 

catching fire, etc.) 

  Cybersecurity threats (exposure of personal data collected by the RVC)  

Our dataset allows us to identify two of these parties: robotic vacuum cleaner producers (which we 
assume  to  be  the  same  as  sellers  in  Figure  1  for  the  sake  of  simplicity)  and  final  consumers 
(households buying robotic vacuum cleaners). Given that that the dataset analyses risks posed to 
third-parties by household RVC, the scope of the risks to which the intervention would apply are 
circumscribed  to  situations  in  which  a  non-member  of  the  household  is  exposed  to  any  of  the 
above-mentioned risks while present in the household  

4.2.  The choice of a nested logit model 

In order to simulate different scenarios corresponding to the policy options, we first estimate the 
demand  for  vacuum  cleaners  using  a  structural  demand  estimation  model.  Structural  Demand 
estimation models allow to estimate the demand of a product in a market and, using the results of 
that  estimation,  to  obtain  the  marginal  costs  (i.e.  the  cost  of  producing  an  additional  unit  of  the 
good)  of  the  firms.  Using  a  structural  demand  estimation  model,  we  can  therefore  produce  an 
empirical description of how consumers and producers behave in a market (in this case, the market 
for vacuum cleaners) and use it to simulate scenarios in which consumer preferences or producers’ 
cost  change  (i.e.  “counterfactuals”),  obtaining  so  new  (hypothetical)  quantities  sold  and  prices. 
Hence, after estimating demand and marginal costs that we observe in the vacuum cleaners market 
today, we apply counterfactuals to simulate the impacts of the baseline scenario and of each policy 
option in the vacuum cleaners market. 

Within the family of demand estimation models, discrete choice models are particularly suited to 
analyse  the  vacuum  cleaners  market.  In  these  models,  a  product  is  considered  as  a  bundle  of 

417 For anecdotical evidence of a robot vacuum cleaner harming persons see 
https://www.theguardian.com/world/2015/feb/09/south-korean-womans-hair-eaten-by-robot-vacuum-
cleaner-as-she-slept 

219 

 
 
 
 
 
                                                           
characteristics.  For  example,  a  specific  robot  vacuum  cleaner  is  a  bundle  of  the  characteristics 
“robot”,  “EPA  filter”,  “with  power  brush”,  etc.  These  models  take  into  account  heterogeneous 
consumers (i.e., consumers with different tastes for the same product or bundle of characteristics 
and,  hence,  different  price  sensitivities  to  each  product)  that  buy  one  out  of  many  differentiated 
products (i.e. products that vary in their characteristics but fulfil the same need) they can choose 
from within a period that defines the market (typically a year). In this respect, this model is suited 
to  estimate  the  consumers’  demand  for  vacuum  cleaners.  The  latter  are  differentiated  products 
(different types of vacuum cleaners, different noise levels, with or without smart connect, etc.) and 
durable  goods  that  consumers  buy  at  least  on  yearly  basis.  Moreover,  given  the  existence  of 
multiple types of vacuum  cleaners in  the market and the different  needs and budgets  consumers 
have, a model accounting for consumer heterogeneity is fit for the analysis of the vacuum cleaners 
market.  In  other  words,  it  is  realistic  to  think  that  different  consumers  value  differently  a  given 
characteristic  (e.g.  the  vacuum  cleaner  being  small),  and  hence  are  willing  to  pay  different 
amounts of money for that characteristic. 

Discrete choice models consider a utility-maximizing consumer and calculate the probability of a 
consumer choosing a particular good (e.g., a robot vacuum cleaner) over other alternative goods in 
the market (e.g., a traditional vacuum cleaner), including an “all other goods” choice. Then, they 
equate the observed market shares to the calculated probability. This means that what we observe 
in  the  market  as  the  market  share  of  a  product  is  the  result  of  a  discrete  choice  (i.e.,  choosing 
either  one  good  or  another,  but  not  more  than  one,  within  typically  a  year)  of  consuming  a 
particular good (e.g. a robot vacuum cleaner with certain characteristics) over other goods he/she 
could have consumed with the same income. On the basis of that identity between the calculated 
probability of buying a good and the observed market share, these models use data on the latter to 
estimate several metrics, and in particular: 

  Own price elasticity of a given product (i.e., by what percentage would the quantities sold 

of a given vacuum cleaner model decrease if its price increased by 1%) 

  Cross-price elasticity of a given product (i.e., by what percentage would the quantities sold 

of a given vacuum cleaner vary if the price of another model price increased by 1%) 

  The  marginal  cost  of  a  given  product  (i.e.,  the  cost  of  producing  an  additional  unit  of  a 

given vacuum cleaner model) 

These  metrics  will  be  used  to  produce  the  above-mentioned  counterfactuals  that  we  generate  to 
estimate the impact of the different policy options and the baseline scenario. 

The  particularity  of  the  nested  logit  model  within  the  discrete  choice  models  family  is  that  it 
assumes that consumers consider some products to be substitutes to each other to a greater extent 
than  other  products.  For  example,  it  might  be  the  case  that,  when  buying  a  vacuum  cleaner, 
consumers  decide  first  between  buying  a  robot  vacuum  cleaner  or  a  traditional  one,  and  then 
decide on which power level to choose. This is the referred to  as the “nesting structure”. In this 
example, there would be 2 nests: the “robot vs non-robot” nest and the “power” nest. Given that 
the  observed  vacuum  cleaner  models  vary  considerably  in  their  characteristics,  a  nesting  logit 
model seems the most appropriate one. 

220 

 
 
 
 
4.3.  Model specification, estimation and instruments 

In this section, we present the specification of the nested logit model estimated. 

4.3.1.  The model 

We use a nested logit model as described by Berry (1994). We specify the model as a one-level 
nested  logit  model  (Barry,  1994)  in  which  consumers  choose  first  which  category  of  vacuum 
cleaner to buy, and then the specific product (brand-model). We build the following 5 categories 
on the bases of the variable type: robot, cylinder, upright handstick and handheld. 

Consumers choose among J differentiated products (vacuum cleaners), j = 1, …, J, and an outside 
good j = 0 representing the decision of not buying  any. The indirect utility of an individual  i for 
product j is given by: 

𝑢𝑖,𝑗 = δ𝑗 + Ϛ𝑖,𝑔 + (1 − 𝜎𝑔)ε𝑖,𝑗 

Where 𝑢𝑖,𝑗 designates the utility obtained by consumer i from consuming good j. Variable ε𝑖,𝑗 is  
an individual-specific valuation  of product  j. Variable  Ϛ𝑖,𝑔  is  a  group-g-specific valuation  of the 
goods that are part of group g, where each group g corresponds to one of the 5 above-mentioned 
categories of vacuum cleaners. Parameter 𝜎𝑔, with 0 ≤ 𝜎𝑔 ≤ 1, represents the degree of preference 
correlation  for  products  of  the  same  category.  The  first  part  of  the  equation,  δ𝑗,  represents  the 
mean utility of consuming good j that is common to all consumers, which is defined as: 

δ𝑗 ≡ 𝑥𝑗𝛽 −  𝛼𝑝𝑗 + ξ𝑗 

The mean utility of consuming product j depends on three variables. First, variable 𝑥𝑗 , which is a 
vector of the product characteristics affecting consumers’ utility, namely: whether it has a control 
handle, the motor position, whether it has a power brush, whether it is rechargeable, whether it has 
smart connect, whether it has special provisions for animals, whether it is bagless, whether it has 
an EPA filter and whether it is a robot vacuum cleaner. Second, the observed price  𝑝𝑗. Third, an 
unobserved  quality  of  the  product,  ξ𝑗.  Parameters  𝛽  and  𝛼  are  unobserved  and  represent  how 
sensitive  the  mean  utility  is  to  each  of  the  product  characteristics  and  the  product’s  price, 
respectively.  

Following Berry (1994), the estimation equation is 

                                                   ln (

𝑠𝑗
𝑠0

) = 𝑥𝑗𝛽 −  𝛼𝑝𝑗 + 𝜎ln (𝑠𝑗|g) + ξ𝑗                                   () 

Where 

𝑠𝑗
𝑠0

 is the market share of product j, which is equal to the quantities sold of good j as divided 

by the quantities corresponding to the outside good (i.e., market size minus total quantities sold of 
all vacuum cleaners). Each market is defined as a combination of country and year. This gives us a 
total of 30 markets. In order to calculate the market size, we considered that, in each market, 20% 
of the population of the country decides whether to buy a certain vacuum cleaner or the outside 
good. The variable 𝑠𝑗|g represents the market share of product j within group g. It is calculated as 
the quantities sold of product j (a vacuum cleaner model-brand) divided by the quantities sold for 
group g (the category of vacuum cleaner). 

221 

 
 
 
 
4.3.2.  Estimation and instruments 

Using the dataset described in Section 3, we estimate equation (1) using the generalized method of 
moments. In order to improve the quality of the estimation, we include a series of fixed effects and 
instrument variables 𝑝𝑗 and 𝑠𝑗|g.  

We  create  vectors  of  dummy  variables  to  control  for  market  (i.e.,  country-year  combinations), 
vacuum cleaner model (variable model) and type of vacuum cleaner (variable type) fixed effects, 
which  we  label  𝑀𝑎𝑟𝑗, 𝑀𝑜𝑑𝑗  and  𝑇𝑦𝑝𝑒𝑗.  Given  that  prices  𝑝𝑗  and  group  market  share  𝑠𝑗|g  are 
endogenous  and  correlated  with  the  unobserved  quality  ξ𝑗,  we  use  BLP  instruments  (Berry, 
Levinsohn  &  Pakes  1995).  For  each  of  the  9  characteristics  affecting  the  indirect  utility  for 
product  j and represented by the vector  𝑥𝑗, we create 7 instrumental variables by computing the 
counts of product characteristics and the sums of product characteristics’ values of each firm, first, 
and of its competitors, second, in each case overall, by brand and by nest. This results in a total of 
63 instrumental variables. 

Hence, the estimation equation becomes: 

                         ln (

𝑠𝑗
𝑠0

) = 𝑥𝑗𝛽 − 𝛼𝑝𝑗̂ +𝜎ln(𝑠𝑗|ĝ ) +  𝑀𝑎𝑟𝑗 + 𝑀𝑜𝑑𝑗 +   𝑇𝑦𝑝𝑒𝑗 +  ξ𝑗                 (2) 

Where 𝑝𝑗̂  is the instrumented price variable and 𝑠𝑗|ĝ  is the instrumented group market share 
variable. 

After estimating the model using equation (2), we compute the resulting total quantities of vacuum 
cleaners sold, consumer surplus and firms’ revenues and profits. This is our baseline scenario. We 
then re-run the estimation three times for three counterfactual scenarios. For each counterfactual 
scenario, we compute the resulting total quantities of vacuum cleaners sold, consumer surplus and 
firms’ revenues and profits and compare them to the first estimation’s results. In the next section, 
we  detail  how  we  design  and  calibrate  the  counterfactuals.  Section  5  shows  the  results  of  the 
comparison between the counterfactual scenarios and the baseline scenario. 

4.4.  Design and calibration of the counterfactuals 

The  results  of  the  estimation  described  in  Section  4.3  correspond  to  the  baseline  scenario. 
Currently, in  absence of policy intervention, we expect legal uncertainty regarding liability risks 
and  a  related  lack  of  consumer  trust  to  exist  with  respect  to  the  use  of  AI-enabled  products  in 
particular,  which  affects  negatively  consumers’  valuation  of  robot  vacuum  cleaners  relative  to 
non-robot vacuum cleaners. In order to assess the economic impact of the intervention, we design 
counterfactuals  translating  the  economic  impacts  of  an  alleviation  of  victims’  burden  of  proof, 
including the impacts of the consequent increases in legal certainty and consumer trust.  Figure 2 
below provides a summary of how the intervention (i.e. an alleviation of victims’ burden of proof) 
generates effects that are translated into the economic impacts simulated in the counterfactuals (in 

222 

 
 
 
 
 
 
orange):  higher  use  demand  for  RVC  and  higher  expected  cost  of  compensation  paid  by  RVC 
users. 

Figure 2: The estimated economic impacts of the intervention 

NB:  “RVC”  stands  for  “robot  vacuum  cleaners”.  Orange-filled  rectangles  correspond  to  the 
economic effects estimated through counterfactuals. 

The  remaining  of  the  section  details  how  each  of  these  counterfactuals  were  designed  and 
calibrated. Given the uncertainty over the magnitude of the impacts, we considered a “high” and a 
“low” scenario for each of them. 

4.4.1.  Increased legal certainty 

If consumers face an increase in legal certainty regarding liability exposure in case of third-party 
damage caused by the RVC they own, their valuation of these products relative to other non-robot 
vacuum  cleaners should  increase. With legal  certainty,  consumers  know that  in  case of accident 
generated by a RVC the legal framework will be clear regarding which party should be held liable 
and  what  they  might  be  required  to  prove  in  civil  proceedings,  and  hence  they  have  more 
incentives  to  buy  one.  In  terms  of  the  model  described  in  Section  4.3,  this  translates  into  an 
increase  in  the  δ  parameter  of  RVC,  which  expresses  consumers’  mean  utility  or  willingness  to 
pay for those products. We therefore simulate the “increased legal certainty effect” as an increase 
in the δ parameters corresponding to each market.  

In order to provide a magnitude to this increase, we use data from the behavioural study produced 
to support the initiative (Kantar, 2020). The study methodology includes an online survey and a 
survey-based online experiment. The survey was carried out on representative samples of the adult 
population from eight countries (Denmark, Netherlands, Ireland, France, Germany, Italy, Poland, 
Romania), with a sample size of minimum 1 000 respondents per country, amounting to a total of 
8 079 respondents across the eight countries. Following a between and within subject design, the 
study  tested  the  effect  of  alternative  liability  regimes  on  consumer  behaviour  towards  AI 
applications.  The  latter  is  focused  on  three  AI-enabled  products:  smart  lawnmowers,  smart 
irrigation systems and grocery-carrying robots. Using the results of the survey and the experiment, 
the study finds that consumers’ willingness  to  pay  for a smart  lawnmower increases by 6% if it 
contains third-party civil liability insurance. 

This product is the more similar in the study to robot vacuum cleaners both in terms of its use and, 
more importantly, the type of damage it can cause. We therefore use results on this product as a 

223 

 
 
 
 
 
proxy to approximate the results corresponding to RVC. Moreover, we use the willingness to pay 
attributable  to  third-party  civil  liability  insurance  (+6%)  as  a  proxy  of  consumers’  valuation  of 
legal  certainty.  By stating how much they value  this (currently inexistent) insurance,  consumers 
are putting a value on the legal certainty that the existence of such insurance would give them. In 
case  of  accident,  they  would  be  certain  that  the  legal  framework  would  be  clear  regarding  the 
distribution of liability in  case of accident. Given that lawnmowers contain blades that could  do 
harm  to  property  and  persons  that  a  RVC  cannot,  we  take  this  average  6%  increase  in  the 
valuation as the high scenario of the impact of increased legal certainty on consumers’ willingness 
to pay for RVC. In absence of additional data, we take a 100% spread between the high and low 
scenario, so that the increase in consumers’ willingness to pay due to an increase in legal certainty 
in the low scenario is of 3%. Hence, our counterfactual δ parameters are the following: 

𝐻 = 1.06 ∗   𝛿𝑅𝑉𝐶 
δ𝐼𝐿𝐶

𝐿 = 1.03 ∗ 𝛿𝑅𝑉𝐶 
δ𝐼𝐿𝐶

Where 𝛿𝑅𝑉𝐶 is the vector containing the δ parameters (i.e. consumers’ valuation) corresponding to 
𝐻   is  the  vector  containing  the  δ  parameters  corresponding  to  RVC  in  an  a  high 
RVC,    δ𝐼𝐿𝐶
𝐻   is  the  vector  containing  the  δ  parameters 
“increased  legal  certainty”  scenario  and  δ𝐼𝐿𝐶
corresponding to RVC in an a low “increased legal certainty” scenario. 

4.4.2.  Increased consumer trust 

This  effect  is  simulated  in  a similar manner to  the effect  of increased legal  certainty. Consumer 
trust refers to “consumers’ trust in services, products, or, latently, providers and manufacturers to 
deliver  against  quality  expectations”  (Kantar,  2020,  p.13).  The  above-mentioned  study  built  a 
consumer trust index on the basis of consumers’ answers to a question linking the level of trust to 
the need for regulation.  However, when analysing the impact  of consumers’ trust  index on their 
willingness to pay for AI-enabled products, it finds that, counterintuitively, the higher the trust, the 
lower  the  willingness  to  pay.  According  to  the  study,  this  is  because  consumers  associate  more 
trustable AI-enabled products  with  more expensive products  they  cannot  afford.  In other words, 
the results contain the mix of two effects that cannot be dissociated: higher consumer trust (which 
should lead to a higher valuation of the product) and price elasticity of demand (consumers expect 
this  products  to  be  more  expensive).  Given  that  the  latter  is  already  taken  into  account  in  the 
nested logit model used, we cannot employ the results using the consumer trust index. 

We  therefore  use  the  results  of  another  related  concept  that  is  correlated  to  consumer  trust: 
likelihood of compensation418. The study shows that consumer trust more AI-enabled products if, 
in  case  of  an  accident,  the  likelihood  of  compensation  is  high.  This  is  coherent  with  the  very 
definition  of  consumer  trust,  as  providing  a  compensation  in  case  of  accident  is  a  way  of 
maintaining a level of quality. Moreover, the intervention analysed (alleviation of victims’ burden 
of proof) would tackle the problem of the low likelihood of compensation in particular in order to 
generate  more  consumer  trust.  Hence,  this  variable  is  well-suited  to  simulate  the  impact  of  an 
increase in consumer trust as a result of the intervention.  

418 The other variable positively correlated to consumer trust, “utility of the application” yielded similar results. 

224 

 
 
 
                                                           
The behavioural study runs two models to test for the impact of an increase in the likelihood of 
compensation on  the willingness to pay for a smart lawnmower: a generalized linear model and a 
linear mixed effects model. In each of these models, the coefficient for the variable “likelihood of 
compensation”  translates  by  how  much  the  willingness  to  pay  increases  when  the  likelihood  of 
compensation  rises.  Given  that  lawnmowers  generate  higher  risks  than  RVC,  we  take  the  lower 
bound in the interval of confidence of the estimated coefficients and compute the percentage over 
the baseline valuation given to lawnmowers that the constant translates. For the generalized linear 
model, this  percentage is  equal  to  4% and for the linear mixed effects  model  to  6%.  Hence, we 
used the former as the low scenario and the latter as the high scenario to estimate the variation in 
consumers’ willingness to pay for a RVC as a result of an increase in consumer trust.  

As in  the case of increased legal  certainty, we use the above-mentioned  percentages to  generate 
two counterfactual δ parameters: 

𝐻 = 1.06 ∗   𝛿𝑅𝑉𝐶 
δ𝐼𝐶𝑇

𝐿 = 1.04 ∗   𝛿𝑅𝑉𝐶 
δ𝐼𝐶𝑇

Where 𝛿𝑅𝑉𝐶 is the vector containing the δ parameters (i.e. consumers’ valuation) corresponding to 
𝐻   is  the  vector  containing  the  δ  parameters  corresponding  to  RVC  in  an  a  high 
RVC,    δ𝐼𝐶𝑇
𝐻   is  the  vector  containing  the  δ  parameters 
“increased  consumer  trust”  scenario  and  δ𝐼𝐶𝑇
corresponding to RVC in an a low “increased consumer trust” scenario. 

4.4.3.  Re-distribution of compensation costs due to the prevention of AI-induced liability gaps 

All policy options consider an alleviation of victims’ burden of proof. As explained in Section 2, 
the alleviation of victims’ burden of proof is the reason why there should be an increase in legal 
certainty and consumer trust.  Moreover, the alleviation of victims’ burden of proof should make 
getting compensation under national liability rules for damage caused by AI-enabled products or 
services  more  likely  for  victims.  Conversely,  users/operators,  which  in  our  setting  are  the 
consumer  buying  RVC,  would  be  more  likely  to  face  the  cost  of  the  compensation.  Then,  the 
alleviation  of  victims’  burden  of  proof  constitutes  an  additional  expected  cost  for  rational 
consumers who take into account both the product of the product (the RVC) and related costs of 
ownership,  in  this  case  the  cost  of  a  possible  compensation.  In  terms  of  the  nested  logit  model 
estimated, this  can be simulated as a  counterfactual  in  which the price of RVC increases by the 
expected  compensation  cost,  the  latter  being  equal  to  the  probability  of  an  accident  happening 
multiplied by the average compensation. 

Note that,  as stated by insurance industry representatives during exchanges with  DG JUST, it is 
likely that the damage to third parties generated by RVC owned by an individual will be covered 
by existing general liability insurance policies, provided that the individual is insured. However, 
when estimating the effects of a re-distribution of compensation costs, we will consider that RVC 
consumers  are  not  insured.  Although  it  is  likely  that  existing  general  liability  insurance  policies 
will  cover  such  damage,  we  cannot  be  sure  this  will  be  the  case  in  absence  of  an  intervention. 
Hence,  given  that  the  re-distribution  of  compensation  costs  has  a  negative  effect  on  (robot) 
vacuum  cleaners’  demand  (although  a  positive  effect  for  the  victims),  we  opt  for  a  prudent 

225 

 
 
methodological  choice  that  will  likely  underestimate  the  positive  impact  of  the  initiative  in  the 
vacuum cleaners market.  

In order to calculate the magnitude of the “re-distribution of compensation costs” effect, we use 
data  from  the  United  States419  generated  by  the  Insurance  Information  Institute  on  the  bases  of 
Insurance Services Office. The data refers to average observed homeowners insurance losses for 
the  period  2015-2019,  which  matches  the  timespan  of  the  vacuum  cleaners  sales  dataset.  In 
particular,  we  focus  on  the  liability  subcategory  of  losses  defined  as  “bodily  and  property 
damage”, which corresponds to the type of damage that RVC could cause. For this category and 
period,  the  claim  frequency  is  of  0.07/100  and  the  average  claim  of  29,752  USD  or  26  219  €. 
Then, the expected loss is equivalent to 18€ per year.  

Given that these figures consider all claims related to bodily injury and property damage to third 
parties,  they  are  not  circumscribed  to  RVC,  although  we  can  expect  claims  related  to  these  and 
other  AI-enabled  products  used  domestically  to  increase  with  their  (increasing)  use.  Hence,  we 
consider that, for the high scenario, the percentage of RVC-related claims over total claims is of 
10%, and hence the claim frequency becomes 0.1 x 0.07/100 and the expected loss 1.84€ per year. 
For the low scenario, we consider that the percentage of RVC-related claims over total claims is of 
5%, and hence the expected loss of 0.92€ per year. Therefore, we define the counterfactuals of the 
effect  of  an  alleviation  of  victims’  burden  of  proof  on  expected  compensation  costs  as  the 
following hypothetical RVC price increases: 

𝐻 = 𝑝𝑅𝑉𝐶 + 1.84€ 
p𝐴𝐵𝑃

𝐿 = 𝑝𝑅𝑉𝐶 + 0.92€ 
p𝐴𝐵𝑃

5. 

RESULTS 

In this section, we present the results of the three effects studied in terms of changes in consumer 
surplus, revenues, profits, revenues and quantities in the vacuum cleaners market. The figures for 
the 6 countries studied result from the model described in Section 4.3. Values for the EU-27 are 
computed as an extrapolation of the results for  the 6 countries studied obtained from the model. 
Hence, EU-27 values should be taken as rough approximations.420  

419 We  were unable to find similar data for European countries. However, it is likely that for the category of losses 
study the frequency and average loss in the United States is similar to that of the European countries studied in this 
report. 
420 The extrapolation made has two limitations. First, demand is influenced by country-specific attributes (e.g. culture, 
income, etc.). By extrapolating results combining demand functions for markets in 6 countries, we implicitly assume 
that those country-specific attributes are representative of the EU-27, which introduces a bias. Second, given that we 
had to use an alternative data source to do the extrapolation, we cannot be sure of how accurate the share of these 6 
countries’ sales over total EU-27 sales is. In order to do the extrapolation, we used data on total vacuum cleaner units 
sold between 2015 and 2019 in EU-27 and for each of the 6 countries studied provided by Statista. The figures for the 
6  countries  studied  provided  by  Statista  and  GfK  (the  data  source  used  to  estimate  the  model)  differ  considerably 
across each of the 30 markets (country-year) studied. However, despite these discrepancies, if the proportion of units 
sold in these countries over the EU-27 total reported by Statista (36%)  is accurate  (this proportion cannot retrieved 
from GfK), then the extrapolation is reliable. 

226 

 
 
 
                                                           
Table 3 below presents the variation in consumer surplus, profits, revenues and quantities sold for 
all  robot  vacuum  cleaners  stemming  from  the  impact  of  the  combined  effect  of  the  three 
counterfactuals. The table translates the sum of all the effects studied that stem from an alleviation 
of  victims’  burden  of  proof:  increased  legal  certainty,  increased  consumer  trust  and  a  re-
distribution of compensation costs due to the prevention of AI-induced liability gaps. 

Table 3: Impact of the intervention on consumer surplus, profits, revenues and quantities sold (in 
thousands of euros/units) 

*Profits and revenues correspond to vacuum cleaner sellers and the third party parties benefiting from compensations. 

victims 

receiving 

Table 3 shows that, for the six countries studied, the overall impact of a policy intervention that 
alleviates victims’ burden of proof is an increase of consumer welfare that ranges from 4 185 000 
€ to 6 959 000 € depending on the scenario considered. For the EU-27, consumer welfare would 
increase by 11 500 000 € and 19 124 000 €, depending on the scenario considered. Total welfare 
(i.e.,  the  sum  of  vacuum  cleaners  consumers  surplus,  vacuum  cleaners  sellers’  profits  and  third 
between 
party 
10 957 000 € and 19 556 00 € for the 6 countries studied and between 30 111 000 € and 53 742 
000  €  for  the  EU-27  depending  on  the  scenario  considered.  These  results  indicate  that  the 
intervention  would  have  a  market  expansion  effect:  the  increase  in  demand  explained  by  ‘new’ 
demand  for  RVC  would  be  greater  than  the  decrease  in  demand  for  non-robot  vacuum  cleaners 
coming from consumers substituting the latter for RVC. As a result, we observe both an expansion 
in consumer welfare and firms’ profit. Then, the intervention should benefit both consumers and 
sellers of vacuum cleaners.  

compensation), 

turn,  would 

increase 

in 

a 

Tables  4,  5  and  6  below  disaggregate  the  contribution  of  the  three  effects  that  are  conflated  in 
Table 3: increased legal certainty, increased consumer trust and a re-distribution of compensation 
costs due to the prevention of AI-induced liability gaps, respectively. 

227 

Low scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioBelgium383 €637 €653 €1 211 €1 353 €2 374 €2.13.5Germany3 322 €5 525 €5 095 €9 532 €11 174 €19 639 €22.136.7Greece21 €35 €38 €70 €38 €70 €0.20.3Ireland6 €10 €14 €25 €8 €16 €0.10.1Poland253 €420 €586 €1 053 €1 664 €2 846 €2.74.4Slovakia199 €332 €386 €706 €863 €1 499 €1.72.8Total4 185 €6 959 €6 772 €12 597 €15 100 €26 444 €28.747.8EU-27 extrapolation11 500 €19 124 €18 611 €34 618 €41 495 €72 670 €79.0131.4CountryConsumer surplusProfits*Revenues*Quantities sold 
 
 
 
Table  4:  Impact  of  an  increase  in  legal  certainty  on  consumer  surplus,  profits,  revenues  and 
quantities sold (in thousands of euros/units) 

Table 4 shows that an increase in legal certainty would make consumer welfare rise between 2 149 
000 
and 
4  311  000  €  for  the  6  countries  studied  and  between  5  905  000  €  and  11  847  €  for  the  EU-27, 
depending on the scenario considered. Profits, in turn, would increase between 1 414 000 € and 2 
837 000 € for the 6 countries studied and between 3 887 000 € and 7 797 000 € for the EU-27, 
depending on the scenario considered. Given the market expansion effect observed, an increase in 
legal certainty should benefit both consumers and sellers of vacuum cleaners.  

Table  5:  Impact  of  an  increase  in  consumer  trust  on  consumer  surplus,  profits,  revenues  and 
quantities sold (in thousands of euros/units) 

The  impact  of  increased  consumer  trust  is  similar  to  that  of  an  increase  in  legal  certainty.  The 
impact on consumer welfare would be between 2 868 000 € and 4 311 000 € for the 6 countries 
studied  and  between  7  882  000  €  and  11  847  €  for  the  EU-27,  depending  on  the  scenario 
considered.  Profits,  in  turn,  would  increase  between  1  888  000  €  and  2  837  000  €  for  the  6 
countries  studied  and  between  5  188  000  €  and  7  797  000  €  for  the  EU-27,  depending  on  the 
scenario  considered.  As  for  the  case  of  an  increase  in  legal  certainty,  we  observe  a  market 
expansion effect. Hence, an increase in consumer trust should benefit both consumers and robot 
vacuum cleaners sellers. 

228 

Low scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioBelgium197 €395 €142 €285 €501 €1 006 €1.12.2Germany1 706 €3 422 €980 €1 966 €4 101 €8 227 €11.322.7Greece11 €22 €10 €20 €10 €20 €0.10.2Ireland3 €6 €4 €8 €1 €3 €0.00.1Poland130 €260 €180 €361 €733 €1 472 €1.42.8Slovakia102 €205 €98 €197 €343 €689 €0.91.7Total2 149 €4 311 €1 414 €2 837 €5 691 €11 415 €14.829.6EU-27 extrapolation5 905 €11 847 €3 887 €7 797 €15 639 €31 371 €40.681.4CountryConsumer surplusProfitsRevenuesQuantities soldLow scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioBelgium263 €395 €190 €285 €669 €1 006 €1.42.2Germany2 277 €3 422 €1 308 €1 966 €5 474 €8 227 €15.122.7Greece15 €22 €13 €20 €13 €20 €0.10.2Ireland4 €6 €6 €8 €2 €3 €0.00.1Poland173 €260 €240 €361 €979 €1 472 €1.82.8Slovakia137 €205 €131 €197 €458 €689 €1.11.7Total2 868 €4 311 €1 888 €2 837 €7 595 €11 415 €19.729.6EU-27 extrapolation7 882 €11 847 €5 188 €7 797 €20 873 €31 371 €54.281.4CountryConsumer surplusProfitsRevenuesQuantities sold 
 
 
 
 
Table 6: Impact of a redistribution of compensation costs on consumer surplus, profits, revenues 
and quantities sold (in thousands of euros/units) 

*Profits and revenues correspond to vacuum cleaner sellers and the third party victims benefiting 
from compensations. 

Table 6 shows that a redistribution of compensation costs from third-party victims to consumers of 
robot vacuum cleaners make consumer welfare in the vacuum cleaners market decrease between 
832 000 € and 1 663 000 € for the 6 countries studied and between 2 287 000 € and 4 570 000 € 
for the EU-27, depending on the scenario considered. 

As  expected,  a  re-distribution  of  compensation  costs  from  third-party  victims  to  consumers  of 
robot vacuum  cleaners, resulting from  an alleviation  of victims’ burden of proof, has a negative 
impact  on  consumer  welfare  in  the  vacuum  cleaners  market  and  a  positive  one  on  third  party 
victims, as explained below. This results from the combination of two effects. On the one hand, 
the additional compensation costs contract demand for RVC. On the other hand, some consumers 
replace  the  (now  more  expensive)  RVC  by  other  non-robot  vacuum  cleaners,  which  boosts 
demand for non-robot vacuum cleaners. However, the former effect prevails, so the overall impact 
on all robot vacuum cleaners (robot and non-robot) is negative in terms of consumer welfare in the 
vacuum cleaners market. Hence, we see a market contraction effect. 

Note  that  here  profits  increase  because  the  additional  compensation  costs  are  modelled  as  an 
increase in RVC prices (cf. Section 4.4.3). Hence, these profits should be interpreted as belonging 
to  vacuum  cleaners  sellers  and  the  third  party  victims  that  benefit  from  the  compensations. 
Vacuum  cleaners  sellers’  profits,  in  turn,  should  decrease  as  a  result  of  re-distributing 
compensation  costs  to  the  parties  responsible  for  the  risks  created  by  the  use  of  these  products: 
consumers would buy less robot vacuum cleaners and only partially replace some of them by non-
robot vacuum cleaners. However, as shown in Table 3, the positive impact of an increase in legal 
certainty and consumer trust would countervail the negative impact of the intended re-distribution 
of compensation costs. 

REFERENCES 

Berry,  S.  T.  (1994).  Estimating  discrete-choice  models  of  product  differentiation.  The  RAND 
Journal of Economics, 242-262. 

Berry,  S.,  Levinsohn,  J.,  &  Pakes,  A.  (1995).  Automobile  prices  in  market  equilibrium. 
Econometrica: Journal of the Econometric Society, 841-890. 

229 

Low scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioLow scenarioHigh scenarioBelgium-76 €-152 €321 €641 €182 €363 €-0.4-0.8Germany-661 €-1 320 €2 807 €5 601 €1 599 €3 186 €-4.4-8.8Greece-4 €-8 €15 €30 €15 €30 €0.0-0.1Ireland-1 €-2 €4 €8 €5 €10 €0.00.0Poland-50 €-100 €166 €331 €-48 €-97 €-0.5-1.1Slovakia-40 €-79 €156 €312 €61 €121 €-0.3-0.7Total-832 €-1 663 €3 470 €6 923 €1 813 €3 613 €-5.7-11.4EU-27 extrapolation-2 287 €-4 570 €9 536 €19 024 €4 984 €9 929 €-15.7-31.4CountryConsumer surplusProfits*Revenues*Quantities sold 
 
 
 
 
Deloitte (2021). Study to Support the Commission’s Impact Assessment on Liability for Artificial 
Intelligence. Forthcoming. 

Insurance  Information  Institute  (2020).  Facts  +  Statistics:  Homeowners  and  renters  insurance. 
Available at: https://www.iii.org/fact-statistic/facts-statistics-homeowners-and-renters-insurance 

Kantar  (2020).  Behavioural  Study  on  the  link  between  challenges  of  Artificial  Intelligence  for 
Member  States’  civil  liability  rules  and  consumer  attitudes  towards  AI-enabled  products  and 
services. JUST/2020/RCON/FW/CIVI/0065 

230 

 
 
 
 
 
 
Annex 12 

Monitoring and evaluation 

1. 

In order to ensure that the targeted review under the preferred policy option (staged approach) can 
rely on a sufficient evidentiary basis, this mechanism would: 
- 

provide for reporting and information sharing by Member States regarding the application of 
the measures under Option 1 in national judicial or out-of-court settlement procedures; 
use information collected by the Commission or market surveillance authorities under the AI 
Act (in particular Article 62) or other relevant instruments; 
use information  and analyses supporting  the  evaluation of the AI  Act  and the reports to  be 
prepared by the Commission on the implementation of that act; 
take into account any information and analyses supporting the assessment of relevant future 
policy measures under the ‘old approach’ safety legislation; 
rely on the information and analyses supporting the Commission’s report on the application 
of  the  Motor  Insurance  Directive  with  regard  to  technological  developments  (in  particular 
autonomous and semi-autonomous vehicles) pursuant to Article 28c(2)(a) MID. 

- 

- 

- 

- 

2.  For  the  purposes  of  evaluating  the  effectiveness  of  the  preferred  policy  option,  success  criteria 
have been defined, on a provisional basis, for each of the specific objectives. Possible sources of 
data  informing  this  evaluation  have  also  been  identified,  as  shown  in  the  subsequent  overview 
table. 

Specific objective 

1. Ensuring legal 
certainty 

2. Preventing legal 
fragmentation 

3. Ensuring an equal 
level of protection of 
victims, and  
increasing trust in AI 

Success criteria for monitoring and evaluating effectiveness 

Success criteria / indicators 

Data sources 

- positive: significant improvement of the level of legal certainty 
as perceived by business stakeholders (benchmark: public 
consultation results) 
- positive/negative: legal experts’ opinion on the level of legal 
certainty achieved 
- positive: correct transposition by MS 
- negative: adoption of diverging measures on AI liability at  
national level 
- positive: reduced difficulties for victims claiming compensation 
(benchmark: cost/time estimates by legal experts for economic 
study) 
- positive impact on consumers’ perception of liability rules and 
attitudes vis-à-vis AI-enabled products and services; increased 
likelihood to buy / use such products and services and increased 
willingness to pay (benchmark: results of behavioural economics 
study 

- Survey of business 
stakeholders with focus  
on SMEs 
- Commission services / 
external legal experts 
- MS reporting 

- MS reporting 
- Commission services 
- analysis by legal experts 
- behavioural analysis 

3. 

In  order  to  benchmark  the  efficiency  of  the  envisaged  measures,  operational  objectives  and 
preliminary indicators have been defined regarding the relevant stakeholder groups, together with 
data sources enabling an effective monitoring. 

Operational objectives and indicators for monitoring and evaluating efficiency (costs / benefits) 

Stakeholders 

Operational objectives / indicators 

Data sources 

1. Companies (as 
potentially liable  

- extent to which the AI liability initiative has shifted  
costs of compensation from victims to liable companies 

- Survey of business stakeholders 
with focus on SMEs 

231 

 
 
 
 
 
 
 
parties) 

2. Victims 

3. Insurers 

4. Public authorities 

- cost reductions (e.g. legal information, insurance, risk 
management costs) due to increased legal certainty and 
reduced legal fragmentation 
- higher demand due to increased consumer trust 
- reduced difficulties for victims claiming  
compensation (benchmark: cost/time estimates by legal 
experts for economic study) 
- victims of harm caused with the involvement of AI  
have the same level of protection as victims of harm  
caused by other technologies 
- improved conditions for offering insurance coverage  
due to increased legal certainty and reduced  
fragmentation; 
- increased demand for insurance solutions due to  
higher awareness of liability risks and increased uptake  
of AI-enabled products and services 
- no significant added burden due to changes in  
national courts’ caseload compared to the baseline  
scenario 
- no significant administrative burden on MS linked to 
reporting requirements 

- MS reporting on legal cases 
- Economic data on AI market 
- behavioural analysis 

- MS reporting on legal cases 
- Commission services 
- analysis by legal experts 

- Survey / workshops with insurance 
stakeholders 

- MS 

4. 

In  order  to  evaluate  the  proportionality,  coherence  and  continued  relevance  as  well  as  the  EU 
added value of the policy measures, the following criteria are envisaged on a provisional basis: 

Criteria 

Data Source 

Proportionality 

- Availability of more effective or efficient means  
to achieve the policy objectives? 

Coherence 

- Synergetic interplay between the AI liability  
initiative and the revised PLD / the AI Act 
- frictionless integration of AI liability provisions 
into MS’ existing civil liability systems 

EU added value 

Continued relevance - Need for harmonised adaptations of general  
liability rules given the specific characteristics  
of AI systems on the market and practical  
experience with liability cases 
- Likely consequences of stopping or withdrawing  
the harmonised provisions on AI liability, in  
particular regarding 
(i) the level of legal certainty,  
(ii) the level of legal fragmentation; 
(iii) the protection of victims of harm caused by AI 

- Qualitative assessment by Commission 
services taking into account MS reporting  
on legal cases and stakeholder feedback 
- Qualitative assessment by Commission 
services 
- MS reporting on legal cases 
- Feedback from MS 
- MS reporting on legal cases 
- Stakeholder survey 
- Analysis of the characteristics and risk  
profile of AI systems on the market 
- Legal economic assessment in the  
framework based on stakeholder feedback  
and data reported by MS 

232 

 
 
 
 
 
 
 
 
Annex 13 

Illustration of AI-specific difficulties in claiming compensation based 
on case scenarios 

This Annex presents a number of case scenarios, which have been developed in close collaboration 
with  the  Joint  Research  Center  (JRC).  The  technical  description  of  the  technologies  involved  has 
been provided by AI experts of the JRC. 

The  purpose  of  this  Annex  is  to  illustrate  concretely  the  AI-specific  challenges  this  initiative  is 
designed  to  address.  It  only  provides  examples.  Due  to  the  general  nature  of  the  national  civil 
liability rules to be adapted by this initiative, the envisaged measures can apply in a large variety of 
cases involving various AI-enabled technologies (either self-standing software systems or physical 
products enabled by AI), potentially liable parties and types of harm (material harm and immaterial 
harm to the extent it is compensable under national law). 

1. Case scenario 1: autonomous cleaning robots 

(a) Characteristics of the relevant AI systems and operational environment 

An  autonomous  fleet  of  cleaning  robots  operates  in  pedestrianised  public  areas.  The  robots  are 
equipped with multiple sensors (cameras, LiDAR, radar, ultrasound, GPS, etc.), digital information 
(digital maps), and connectivity features including communication between the robots and between 
the robots and the infrastructure. The robots include multiple AI systems, each one responsible for a 
particular  task  (e.g.,  detection  and  location  of  litter  and  dirt,  robot  localization  and  mapping, 
detection of obstacles, trajectory planning or lateral and longitudinal control).  

Each cleaning robot belongs to  a fleet deployed throughout the city. An employee is  in  charge of 
defining  the  operation  areas  to  be  cleaned  (the  missions)  and  monitoring  multiple  robots  in 
simultaneous operation from a remote-control centre. The fleet can coordinate the safe cleaning of 
the  selected  region,  the  interaction  with  pedestrians,  and  the  avoidance  of  obstacles,  with  a  high 
degree of automation. The role of the human operator is of a supervisory nature. 

(b) Description of the events leading to damage  

A  colourful  baby  stroller  is  parked  in  front  of  a  similarly  patterned  advertising  banner  while  the 
baby’s  guardian  looks  at  a  nearby  shop  window.  One  of  the  cleaning  robots  seems  to  fail  to 
recognise  the  stroller  as  an  obstacle  and  collides  with  it.  The  stroller  is  damaged  and  the  baby 
slightly injured. 

(c) Potential reasons of the accident 

The accident might have been caused by any of the following issues:  

-  An original flaw in  the AI vision component, an AI-based perception system which is  not 
capable of detecting the trolley since it was somehow camouflaged with the background (an 
advertising banner of a similar colour and pattern than the trolley). This leads to an image 
segmentation error (i.e., a false negative) that considers the trolley as part of the background 
of the banner.   

233 

 
 
 
-  Failure by the provider of the AI vision component to distribute an available software update 
fixing an identified safety  issue or due to  the operator for not  having updated to  the latest 
version. The fixing update was based on an enhanced segmentation system by using a sensor 
fusion approach including data from range-based sensors, which would have allowed the 3D 
volume of the stroller to be detected as an obstacle, and being avoided by the path planning 
system.  

-  Failure by the human remote operator to appropriately monitor the operation of the fleet of 
robots. The failure may be either due to inadequate supervision by the human operator (i.e. 
incorrect compliance with the human oversight mechanisms defined by the provider), or to 
defects in the human-robot interfaces (i.e. deficiencies of the human oversight mechanisms 
defined by the cleaning robot producer).   

-  A specific vulnerability of the perception system based on some patterns, designed through 
adversarial  machine  learning,  which  can  be  printed  on  stickers  that,  when  pasted  on  the 
billboard  or  any  other  surface,  produce  critical  problems  in  machine  learning-based 
segmentation algorithms leading to unexpected perception results.     

-  A deliberate attack on the robot's sensors such as blinding, jamming or spoofing. If there are 
no  mechanisms  to  detect  and  counteract  this  type  of  attacks,  the  perception  systems  are 
completely compromised, leading to failures in the interpretation of the environment, such 
as, for example, the non-detection of an object such as a baby stroller.   

-  Failure by a cybersecurity breach through unauthorised access and subsequent manipulation 
of  the  internal  electronic  control  units  by  an  attacker,  using  the  wireless  interface  as  the 
entry point. In this case, the attacker would have forced the system to take an unsafe action 
making the robot to collide with the stroller.   

-  Failure due to an updated version of the perception system devised to reduce the number of 
false positives and negatives of previous versions (which leads to many regions not properly 
cleaned). The confidence threshold to consider a detection as a true obstacle was increased 
to  reduce  the  number  of  false  positives.  Unfortunately,  the  similarity  between  the  texture 
and  colour  of  the  baby  stroller  with  the  background  of  the  advertising  banner  from  the 
perspective  of  the  camera  resulted  in  a  potential  obstacle  detected  with  not  very  high 
confidence and was discarded by the new updated version of the segmentation system.    

(d) Potentially liable parties 

The final producer of the cleaning robot can be strictly liable under the Product Liability Directive 
for  damage  caused  by  a  defect  in  its  product.  However,  the  autonomous  cleaning  robots  are  very 
complex  systems  with  many  different  components  based  on  AI,  affecting  each  other,  and  usually 
developed  and  integrated  by  different  parties  or  subcontractors.  The  defect  (e.g.  a  safety  or  a 
cybersecurity vulnerability) can be in one of the components, in several components, or in a faulty 
integration of these components. Therefore, other potentially liable parties can be found within the 
complex  supply  chain  involved  in  the  development  of  autonomous  cleaning  robots  (e.g.,  AI-
software developer, company providing the perception systems of the robots). The existing Product 
Liability Directive may not adequately cover these parties as component manufacturers, because 
of doubts as to the extent to which software falls within the definition of ‘product’. 

The producer establishes a set of operational requirements for the AI systems involved (in particular 
for human oversight). The professional user (or the user’s employee in the case of legal persons) 
have  to  follow  these  requirements  to  ensure  safe  operation  of  the  autonomous  cleaning  robots 
vehicles. If a user (or their employee in charge) does not comply with their obligations, the user can 
be (vicariously) liable for damage caused by a negligent use of the robotic fleet. Potentially liable 
parties include also companies providing the datasets for training perception and decision-making 
systems. 

234 

 
 
 
Other possible liable parties are  third parties (adversaries) that perform a cyber attack on the 
autonomous  robotic  fleet  such  as  attacking  the  sensors  through  jamming  or  spoofing,  placing 
adversarial artefacts in the scene (e.g. sticker), unauthorised access to the electronic control units of 
the robots thorough wireless connectivity vulnerabilities, etc. 

In summary, potentially liable parties include: 

-  Producer of the cleaning robots. This stakeholder would likely be considered as the provider 

of the robots’ AI-systems for the purposes of the AI Act.  

-  Provider  of  individual  AI  components  integrated  in  the  cleaning  robots  (e.g.  navigation, 
perception  systems  such  as  vision  component,  path  planning,  low-level  controllers, 
operational  interfaces).  These  stakeholders  would  likely  be  considered  as  providers  under 
the AI Act.  

-  Professional  user  /  operator:  the  municipality,  or  a  company  providing  the  service  to  the 
municipality, deploying the cleaning robot services in the city. This stakeholder would likely 
be considered as the user of the robots’ AI systems for the purposes of the AI Act.  

-  Adversaries (e.g. cybercriminals) that attack the system exploiting vulnerabilities in the AI 
components  (e.g.  adversarial  ML)  or  on  the  broader  software  and  hardware  surface  (e.g. 
buffer overflows). 

(e) Fault-based compensation claims under national law  

Under national tort law, the injured party or claimants in principle have to prove that the defendant 
–  or  their  employees  in  the  case  of  vicarious  liability  –  caused  the  damage  intentionally  or 
negligently (i.e. by not complying with the applicable standard of care). As mentioned, there may 
be multiple alternative or cumulative reasons for the damage, including low confidence detection, 
internal cybersecurity breach, external adversarial attack with some specific patterns, etc.  

Due to the high degree of complexity and lack of explainability of the AI systems with which the 
cleaning  robots  are  endowed,  proving  the  fault  of  the  defendant  and  the  causal  link  between  that 
fault and the damage in this case will often require a (possibly costly) expert opinion. Provided that 
the  claimant  can  obtain  access  to  the  relevant  data,  the  expert  can  examine  and  interpret  the  raw 
inputs  read  by  the  sensors,  the  internal  variables  of  some  of  the  subsystems,  and  the  outputs  or 
actions taken by the robot. The necessary analysis would require in particular:  

- 

- 

access  to  documentation  related  to  operational  and  human  oversight  requirements  of  the 
system;  
access  to  system  logs  (inputs,  outputs,  and  internal  states  of  any  of  the  subsystems) 
corresponding to the last few minutes before the accident.   

The initiative on civil liability for AI will include provisions on the disclosure of information to be 
documented/logged  pursuant  to  the  AI  Act.  In  accordance  with  relevant  procedural  law,  the 
competent national court could order that such disclosure would be subject to stringent safeguards 
to  ensure  proportionality  and  protect  the  legitimate  interests  of  all  parties  concerned,  for  instance 
confidential information, intellectual property rights and trade secrets. On the basis of the disclosed 
information, an expert could for instance determine that the result of the perception system seems to 
be wrong at the time of the collision, since the stroller does not appear in the list of detected objects. 
The expert may thus be able to prove that the stroller was not properly detected (without indicating 
the cause). She may also be able to discard that the sensors were jammed or spoofed since the raw 
data seems  correct.  The  expert could  further suppose a correlation  between such detection failure 
and  the  control  decision  of  the  robot  to  move  forward  until  colliding  with  the  stroller.  This  may 
allow the claimant to establish prima facie evidence.  

235 

 
 
 
 
However,  the  high  degree  of  autonomy,  opacity  and  complexity  of  the  AI  systems  involved  may 
make it impossible even for an expert to infer a clear causal link between a specific input and the 
harmful  output,  given  in  particular  that  not  only  the  perception  module  but  also  the  trajectory 
planning system and the low-level controllers are based on complex AI models. Depending on the 
standard  of  proof  national  courts  will  require  of  the  claimant,  the  inability  to  discard  alternative 
causes  of  the  damage  (e.g.,  cybersecurity  vulnerability,  unpredictable  behaviour  due  to 
reinforcement learning or continuous adaptation of the trajectory planning system, etc.) may lead to 
all fault-based claims being dismissed, leaving the victims to bear the damage. 

The initiative on civil liability for AI will address these issues by: 

-  providing  for  a  presumption  of  causality  if  the  defendant  did  not  comply  with  obligations 
under the AI Act designed to prevent damage of the type for which compensation is sought; 
-  providing  for  a  targeted  alleviation  of  the  burden  of  proof  regarding  the  question  how  or 

why an AI system reached the relevant (harmful) output.  

These measures  are designed specifically to  avoid that the peculiar characteristics of certain  AI-
systems lead to compensation gaps compared to cases not involving AI. 

(f) Obtaining compensation under the Product Liability Directive  

- From the manufacturer of the final product: 

The injured party has to prove that the robot was defective, i.e. it failed to provide the safety that the 
public  at  large  is  entitled  to  expect,  and  that  this  lack  of  safety  caused  the  damage.  Whether  the 
source of the defect was a mechanical flaw, a software flaw or a data flaw is irrelevant. It should not 
be  difficult  to  prove  that  the  robot  failed  to  provide  the  safety  the  public  at  large  was  entitled  to 
expect when it crashed into the stationary stroller.   

However, the Directive exempts producers from liability in some cases:   

- 

- 

- 

if they prove that the defect probably did not exist when the product was put on the market 
(Article 7(b) PLD). The robot manufacturer might argue the defect emerged while the robot 
was  in  operation.  Under  plans  to  revise  the  PLD,  this  exemption  would  be  adapted  to  the 
reality of digital products, which change and interact with other products/systems after being 
placed on the market.  
if they prove that even though the defect existed when the product was put on the market, it 
was  undiscoverable  according  to  state-of-the-art  knowledge  (Article 7(e)  PLD).  This 
exemption is intended to encourage producers to put innovative products on the market. In 
this  case,  however,  the  risk  of  a  robot  that  operates  in  public  colliding  with  people  and 
property  is  a  clearly  foreseeable  risk,  so  it  is  unlikely  this  exemption  would  be  available. 
The  availability  of  the  defence  in  such  cases  should  be  clarified  in  the  revision  of  the 
Directive.  
if the damage was caused entirely by a third party, in this case the hacker, and not because 
of  a  defect.  It  is  currently  unclear  if  a  product’s  cybersecurity  vulnerabilities  can  make  a 
product  defective  –  should  a  product  be  able  to  withstand  the  most  sophisticated 
cyberattack? The assessment of defectiveness would need to take into account, among other 
elements, the intended usage of the product, the threats identified in the context of use, the 
state  of  the  scientific  literature,  and  the  actions  taken  by  the  manufacturer  to  mitigate  the 
cybersecurity risks. If the damage is only partially due to a defect, the robot manufacturer is 
nevertheless fully liable for the damage (Article 8(1) PLD).  

- From component manufacturers: 

236 

 
 
 
In most cases, the injured party will seek compensation from the manufacturer of the final product, 
but  it  is  possible  to  seek  compensation  from  component  manufacturers  instead.  The  injured  party 
has to prove that the perception system, AI-software or datasets were defective and that this lack of 
safety caused the damage.  

The  first  difficulty  is  that  the  AI-software  developer  and  provider  of  datasets  could  argue  that 
software/data is  not  a product  within the meaning of the Directive  and that  they are therefore not 
producers and cannot be sued. Such products and producers should be brought within the scope of 
the Directive through the revision.  

Proving  that  the  components  failed  to  provide  the  safety  that  the  public  at  large  was  entitled  to 
expect would be challenging. What safety expectations are the public entitled to when it comes to 
new technological developments like AI-software or datasets? The court would take into account all 
circumstances,  which  would  include  applicable  legal  requirements,  like  the  AI  Act  and  the 
Machinery Directive/Regulation, which will apply to AI-systems as such an AI-software integrated 
into  machinery.  The  notion  of  defectiveness  in  relation  to  new  technologies  should  be  clarified 
through the revision of the Directive.  

Proving causality raises similar issues to those described above for the fault-based claims: difficult 
and  costly.  The  revision  will  look  at  easing  the  burden  of  proof,  including  an  obligation  on  the 
producer to disclose technical information (e.g. data logs) to the claimant.   

- Limits on claims  

The claimant can claim compensation for physical injury to the baby, but property damage below 
the  value  of  EUR  500  is  not  covered  by  the  Directive.  In  all  likelihood  therefore,  damage  to  the 
stroller would not be recoverable. The EUR 500 threshold may be lowered or removed through the 
revision. 

2. Case scenario 2: socially assistive robots in education 

(a) Characteristics of the relevant AI systems and operational environment 

Socially  assistive  robots  are  defined  as  physical  devices  that  can  autonomously  sense,  process 
sensory information, and perform actions upon the surrounding social environment, in a meaningful 
way. This case scenario refers to the use of socially assistive robots as a tool to teach or demonstrate 
socially desirable behaviours to help children who have difficulties expressing themselves to others 
because of neurodevelopmental conditions, characterized by social communication difficulties as a 
result  of  Autism.  Socially  assistive  robots  can  have  anthropomorphic  features,  or  they  may  have 
minimalistic morphological characteristics. They can be designed across a spectrum of behavioural 
and morphological characteristics, regarding their mimicry of agency, to  be perceived from social 
agents to a smart toy. In any of those cases, the robots might function fully autonomously, and they 
might learn from their environment and through interaction.    

Autism  Europe  highlights  the  need  for  inclusion  and  preparation  of  autistic  children  for  an 
independent life. One of the ways to address this challenge is the connection between special and 
mainstream  schools.  In  the  present  case  scenario,  a  school  unit  therefore  decides  to  increase  the 
number of admissions for high functioning autistic children. However, the staff is not adequate to 
undertake  individual  support  for  the  autistic  children.  For  this  reason,  the  director  of  the  school 
decides  to  introduce  one  Socially  Assistive  Robot  per  class  for  personalized  interaction  with  the 
autistic  children  for  the  improvement  of  their  social  skills.  The  school  signs  a  contract  with  a 
company providing robotic educational services to provide the service at the school.  

In this case scenario, the robot has the following characteristics and AI-based modules:  

237 

 
 
- 

- 
- 
- 

It is 1.30m tall.  
It has arms.  
It  has  multiple  sensors  to  detect  the  environment,  including  cameras,  3D  sensors,  laser, 
sonar infrared, tactile, microphones and inertial measurements units.  
It  is  mobile,  including  AI-based  perception,  navigation,  facial,  speech  and  emotion 
systems, 
localization,  decision-making,  mapping  and  path  planning 
recognition 
manipulation, grasping, expressive communication and other AI-based systems.  
It has a tablet for an alternative means for communication.  
It  can  perceive  and  process  natural  language  using  AI  systems,  including  a  module  for 
verbal communication with expressive voice.  
It is capable of detecting obstacles, people, and facial expressions using AI-based computer 
vision algorithms.   
It is equipped with an AI-based cognitive architecture which combines task-related actions 
and socially adaptive behaviour for effective and sustained human-robot interaction.  
For the robot to be tailored for interaction with autistic children in school environments, it provides 
the following AI-based modules:  

- 
- 

- 

- 

-  Pre-designed interventions for cognitive engagement (task-oriented).  
-  Adaptation  of  the  robot’s  social  behaviour  according  to  the  child’s  personal  abilities  and 

preferences in the context of autism.  

The  robots  are  installed  in  the  classrooms  for  regular  personalized  interventions  with  the  autistic 
children and for voluntary interaction during children’s free time. The robots are mobile, and they 
can navigate dedicated space during the children’s free time if a child requests so. The robots learn 
from  the  interaction  with  the  autistic  children  and  adapt  their  social  behaviour.  While  during  the 
lesson  time  the  robot  is  available  only  for  children  with  autism  to  perform  personalized 
interventions, during the free time, any child of the school can interact with the robot at dedicated 
spaces. 

(b) Description of the events leading to damage 

In this use-case, we focus on injury/damage/harm which might be caused because of the adaptive 
behaviour of the robot. Some property damage may also occur.   

Case 1 – physical harm and property damage towards a child with darker skin: Because of biases in 
the development of robot adaptation (perception system) from the prolonged period of interaction 
with children with certain characteristics, the robot fails to perceive a child with darker skin, and it 
causes physical harm to the child. The blow caused by the robot also resulted in the  breakage of 
the child's glasses, valued at less than €500.  

Case 2 – physical harm and property damage towards a child that behaves in an unexpected way: 
Because  of  biases  in  the  development  of  robot  adaptation  (decision-making  and  path-planning 
system),  from  a  prolonged  period  of  interaction  with  children  with  certain  behavioural 
characteristics, the robot fails to respond in an appropriate way to an autistic child that might have 
unexpected behaviour, it loses control and collides with the child. This incident caused by the robot 
also  resulted  in  the  breakage  of  the  child's  glasses,  valued  at  less  than  €500.  In  addition  to  the 
physical harm, the inappropriate robot response causes emotional distress to the interacting child, 
which might lead to psychological trauma. 

238 

 
 
Case 3 - Long-term psychological harm towards a neurotypical child421 

During childrens’ free time at the school, a neurotypical child interacts with the robot on a regular 
basis.  The  robot  adapts  to  the  child's  needs  and  requests  which  subsequently  leads  the  child  to 
develop the following medically recognised pathological conditions: 

-  The  child  develops  symptoms  of  addiction  to  the  robot.  The  robot  perceives  the  child's 
social interaction styles, the emotional states and the cognitive level and adapts its behaviour 
accordingly. The robot is equipped with the intrinsic motivation for sustained human-robot 
interaction  which  makes  it  develop  behaviours  that  stimulate  the  development  of  an 
emotional bond of the child towards the robot. This,  in  turn, results in  the child's possible 
addictive behaviour towards the robot. The increased preference of the child to interact with 
the robot rather than with humans causes an abnormal socio-emotional development.  

-  Medical condition of depression: Deficiency of emotion regulation skills and anxiety. The 
lack  of  interactions  with  humans  and  social  relationships  (i.e.,  social  isolation)  can  have 
detrimental effects on an individual’s physical and psychological health. Social isolation can 
negatively influence psychological health leading to depressive symptoms.  

-  Abnormal  cognitive  and  socio-emotional  development:  The  robot’s  limited  modalities 
allow  for  a  limited  variety  in  the  robot-initiated  social  interactions  with  the  child  and  for 
poor  and  predictable  loops  of  interaction.  The  children  have  the  tendency  to  adapt  their 
behaviour  to  the  social  agents  they  interact  with.  Consequently,  for  neurotypical  children, 
the  predictable  robot  behaviour  has  a  negative  impact  on  the  child’s  cognitive  and  socio-
emotional development. This might lead to cognitive deficiencies and learning difficulties.  
-  Cognitive  and  socio-emotional  dependencies.  This  might  have  a  negative  impact  on  the 

child’s development of autonomy and agency. 

(c) Potential causes of the damage suffered 

In cases 1 and 2 (primarily physical harm), the damage could have the following possible causes: 

- 
- 
- 

the perception module of the robot fails to perceive the child; 
the decision-making and path planning modules of the robot fail to adapt to the child user; 
the  control  module  fails  to  consider  the  physical  and  behavioural  differences  of  the  child 
user. 

-  Where a person was entrusted with ensuring regular human supervision (e.g. an educator or 
therapist), that person’s possible failure to adequately monitor the robot during operation is 
an additional possible cause of the damage. 

These  potential  causes  could  be  related  to  the  adaptation  on  the  perception  and  decision-making 
(and control) elements of the robot, which evolved continuously via the interaction of the robot with 
children with specific characteristics. As a result, the robot could have developped biases towards 
children  with  darker  skin  or  unexpected  behaviours,  whereby  the  causation  of  harm  is  linked  to 
protected grounds of discrimination. 

In case 3 (psychological harm), the possible reasons are linked primarily to the robot’s adaptation 
module. This module embeds an intrinsic motivation element which contributes to the human-robot 
sustained interaction. The robot's internal  motivation  to  remain in  an optimal level  of comfort  for 
the  child-user  contributes  to  its  adaptation  to  the  specific  child's  characteristics,  needs  and 
behaviours.  This  robot  behaviour  develops  a  closed  loop  of  cognitive  and  socio-emotional 

421 The risk for a robot-related psychological harm to children is not based on existing scientific results since there is 
no longitudinal study in the field of child-robot interaction that has looked at this topic. The JRC is interested to carry 
out such a study in the future. 

239 

 
 
 
                                                           
interaction with the child that might lead to the child's addiction to the specific robot behaviour. In a 
long-term interaction the child might exhibit a preference for interaction with the robot rather than 
human social agents. In that case, the child and the robot develop in a mutual adaptation loop. Here 
again, a possible neglect by the designated human supervisor to adequately monitor the robot-child 
interaction (if such human supervision is prescribed) is an additional possible cause of the harm. 

(d) Overview of stakeholders involved 

Potentially liable parties: 

-  Provider  of  the  robotic  educational  system:  this  stakeholder  owns  the  robots  and  equips 
them  with  tailored  AI-based  educational  capabilities.  They  modify  the  robot  to  add  new 
functions  to  it  after  it  has  been  placed  on  the  market.  This  stakeholder  would  likely  be 
considered as the provider of the educational AI system for the purposes of the AI Act, as it 
provides the system for the particular context. They can also be the users of the system, if 
they themselves use it to provide educational services.  

-  Manufacturer  of  the  robot:  Designs  and  develops  various  AI  and  non-AI  modules  of  the 
robot or collaborates with providers that develop the various modules and is responsible for 
their integration. This stakeholder would likely be considered as the provider of the robot’s 
AI systems for the purposes of the AI Act. 

-  Providers of the various AI-based systems integrated in the robot before it is placed on the 

market, e.g. perception systems, control systems. 

-  User  of  the  robotic  educational  platform:  This  entity  provides  the  robotic  educational 
services. It may be the same as the provider of the robotic educational system or a different 
entity. In the latter case, they may purchase or lease the robotic education platform from the 
provider. 

-  School  unit  A  mainstream  school  which  accommodates  high  functioning,  verbal  children 

that have been diagnosed with ASD. 

Injured parties: 

-  End-user  (neurotypical  and  autistic  children,  children  with  a  certain  skin  colour)  From  a 
psychological  perspective,  children  diagnosed  with  ASD  show  a  notably  lower  number  of 
behaviours aimed at initiating, responding, or maintaining social interaction and an extreme 
difficulty in filtering sensory input. For autistic children, robots represent safe, predictable, 
and  coherent  environments  to  experience  prototypes  of  social  interactions  and  reduce 
emotional dysregulations. 

(e) Compensation claims for the psychological and physical injury based on fault-based liability 
under national law422 

Under national fault-based liability rules, claims can in principle be brought against all potentially 
liable parties listed above. Both physical injury and psychological harm amounting to a recognised 
pathological  condition  are  usually  compensable  under  national  liability  rules,  and  there  is  no 
minimum  threshold  in  respect  of  property  damage  as  in  the  PLD.  However,  the  injured  party  in 
principle  has  to  prove  that  the  respective  defendant  –  or  their  employees  in  the  case  of  vicarious 
liability – caused the damage intentionally or negligently (i.e. by not complying with the relevant 
standard of care).  

422  For  the  Product  Liability  Directive,  see  mutatis  mutandis  the  cleaning  robot  use-case  above.  Under  the  existing 
provisions  of  the  Directive,  damage  means  damage  caused  by  death  or  personal  injuries  and  damage  to  items  of 
property for private use or consumption, other than the defective product itself. The review of the Directive will not 
extend the types of harm giving rise to claims under the PLD to psychological harm. 

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Claims against the manufacturer of the robot: Depending on how fault-based producer liability 
is  approached  under  the  relevant  national  law,  national  courts  may  or  may  not  infer  fault  and 
causality  from  the  fact  that  the  robot  caused  the  relevant  injuries.  The  fact  that  the  AI-systems 
influencing  the  robot’s  behaviour  adapted  during  the  latter’s  autonomous  operation  may  put  into 
doubt such an inference. National courts may namely take into account that the robot’s behaviour 
depends  on  various  circumstances  that  may  be  considered  unforeseeable  for  the  manufacturer 
(namely  the  precise  operating  environment,  human  interaction  and  the  input  data  the  robot  is 
exposed  to).  The  challenges  described  below  with  respect  to  claims  against  the  providers  of  AI 
modules may then apply also vis-à-vis the manufacturer of the end-product.  

If  the  injured  party  can  prove  that  the  manufacturer  did  not  comply  with  certain  obligations 
incumbent upon them as provider pursuant to the AI Act (e.g. the obligation to ensure balanced and 
suitable  training  data),  this  may  facilitate  the  compensation  claim  as  national  courts  may  infer 
liability from such non-compliance. However, in order to establish this, the injured party may need 
access  to  relevant  information  that  providers  are  required  to  record/document  under  the  AI  Act, 
such  as  information  on  the  training  data  used,  the  specifications  defining  suitable  input  data,  the 
provider’s risk-management system, etc.  

The  initiative  on  civil  liability  for  AI  will  address  these  challenges  by,  first,  laying  down 
harmonised  provisions  on  the  disclosure  of  information  (subject  to  appropriate  confidentiality 
safeguards in accordance with the relevant procedural law) to be documented/logged pursuant to the 
AI  Act,  second,  presuming  that  non-compliance  with  requirements  of  the  AI  Act  was  causal  for 
damage  of  the  kind  those  requirements  are  meant  to  prevent,  and  third,  alleviating  the  victim’s 
burden  of  proof  as  regards  the  question  how  or  why  an  AI  system  reached  a  certain  output.  The 
latter measure is particularly relevant with respect to AI systems that self-adapt continuously, as the 
characteristic unpredictability of such systems can make it very difficult to prove the necessary link 
with a specific trigger of a harmful AI output. 

Claims  against  providers  of  individual  AI  modules,  including  the  education  system:  The 
applicability  of  national  fault-based  liability  rules  does  not  depend  on  whether  an  AI-system  has 
been integrated into the robot before or after it was placed on the market by the manufacturer. Fault-
based  liability  thus  extends  in  principle  to  the  provider  of  the  various  individual  AI  modules, 
including  the  educational  system  with  which  the  robot  was  equipped  to  tailor  it  to  its  specific 
intended  purpose.  In  order  to  claim  compensation  from  any  of  the  providers  of  the  various  AI 
modules  enabling  the  autonomous  functioning  of  the  educational  robot,  the  injured  party  would 
have  to  establish  that  the  specific  AI-system  provided  by  the  defendant  malfunctioned,  that  this 
malfunctioning  was  caused  by  a  negligent  or  deliberately  harmful  action  or  omission  of  the 
defendant, and that it caused the damage. Given the high degree of autonomy, complexity and the 
lack  of  explainability  of  the  relevant  AI-systems,  meeting  this  burden  of  proof  will  often  require 
expert analysis of the design and functioning of the relevant AI-systems.   

The  necessary  expert  analysis  could  notably  be  based  on  the  documentation  and  system  logs 
required  by  the  AI  Act  (inputs  read  by  the  sensors,  outputs,  internal  states  of  the  AI  subsystems, 
actions taken by the robot), provided that the injured party is entitled to access that information for 
the  purposes  of  their  claim.  The  initiative  on  civil  liability  for  AI  will  include  provisions  on  the 
disclosure of such information, subject to appropriate confidentiality safeguards in accordance with 
the relevant procedural law. On this basis, an expert may notably be able to determine whether the 
result  of  the  robot’s  perception  system  is  correct  at  the  time  of  the  accident,  for  instance  by 
checking whether the physically injured child appears in the list of detected objects. The expert may 
also review the relevant control decisions of the robot, e.g. the decisions to interact in a certain way 
with  the  affected  children,  or  the  decision  to  actuate  certain  movements.  The  analysis  may  also 
inform  the  supposition  of  a  correlation  between,  for  instance,  a  detection  failure  and  a  relevant 
control decision of the robot.   

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However, the high degree of autonomy, complexity and the lack of explainability of the AI systems 
involved make it impossible – even for an expert – to establish clear causality between a specific 
input  variable  and  the  harmful  AI  output.  Given  the  adaptive  nature  of  the  robot’s  interaction 
capabilities,  which  are  shaped  by  various  parameters  in  its  operational  environment,  and  thus 
outside the providers’ control, national courts may deny the attribution of that output to any specific 
provider.  The  initiative  on  civil  liability  for  AI  will  address  this  challenge  by  providing  for  a 
targeted alleviation of the victim’s burden of proof regarding the question how or why an AI system 
reached the relevant (harmful) output.  

The  initiative  on  civil  liability  will  ensure  that  if  the  defendant  can  prove  that  the  respective 
provider  has  not  complied  with  relevant  preventive  requirements  of  the  AI  Act,  a  liability  claim 
based on national law could be brought on those grounds, without the need to prove a causal link 
between  that  non-compliance  and  the  damage.  In  order  to  provide  the  necessary  proof  of  non-
compliance,  the  injured  party  may  again  rely  on  the  harmonised  conditions  for  access  to 
information that providers are obliged to document or record under the AI Act.  

These  measures  are  designed  specifically  to  avoid  that  the  peculiar  characteristics  of  certain  AI-
systems  –  and  this  case  scenario  in  particular  their  continuously  adapting  nature  –  lead  to 
compensation gaps compared to cases not involving AI. 

Claims  against  the  company  using  the  robot  to  provide  education  services:  The  company 
providing  educational  services  is  using  the  robot’s  AI  systems  under  its  authority,  and  would 
therefore be considered as the ‘user’ for the purposes of the AI Act. In the case at hand, proving that 
the  damage  was  caused  by  a  negligent  or  intentionally  harmful  action  or  omission  of  that 
company’s employees would be challenging because the potential causes of the harmful output are 
linked to autonomous, adaptive output of different AI-systems. In such a context, it is uncertain how 
national courts would assess the professional user’s duty to ensure the safe operation of the robot. 
Access  to  information  such  as  the  human  oversight  requirements  (instructions  of  use)  and  data 
logged during the operation could help the victim to establish a possible failure to comply with the 
user’s standard of care.   

Claims against the school: A failure of the school’s staff to duly monitor children’s activities and 
prevent  harm  can  in  principle  give  rise  to  the  school’s  (vicarious)  liability  under  national  law. 
However, given that a company was in charge of providing the educational services using the robot, 
and  assuming  that  the  robot  was  intended  to  function  without  supervision  by  a  teacher,  national 
courts are unlikely to uphold such a claim. 

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