Policy and investment recommendations for trustworthy AI.
Date: 2022-09-22
Institutions: Directorate-General for Communications Networks, Content and Technology
Subjects: EU strategy, artificial intelligence, competitiveness, digital economy, digital literacy, economic growth, ethics, foresight, fundamental rights, governance, investment, private sector, public sector, regulation, social integration, sustainable development

INDEPENDENT 
HIGH-LEVEL EXPERT GROUP ON 
ARTIFICIAL INTELLIGENCE 
SET UP BY THE EUROPEAN COMMISSION

POLICY AND INVESTMENT RECOMMENDATIONS 
FOR 
 TRUSTWORTHY AI 

POLICY AND INVESTMENT RECOMMENDATIONS 

FOR 

TRUSTWORTHY AI 

High-Level Expert Group on Artificial Intelligence 

This document was written by the High-Level Expert Group on AI (AI HLEG). It is the second deliverable of the 
AI  HLEG  and  follows  the  publication  of  the  group's  first  deliverable,  Ethics  Guidelines  for  Trustworthy  AI, 
published on 8 April 2019. 

The members of the AI HLEG named in this document support the overall framework of recommendations put 
forward in this document, although they do not necessarily agree with every single statement therein. 

The AI HLEG is an independent expert group that was set up by the European Commission in June 2018. 

Contact   Nathalie Smuha - AI HLEG Coordinator 
E-mail   CNECT-HLG-AI@ec.europa.eu  

European Commission 
B-1049 Brussels 

Document made public on 26 June 2019. 

PDF  ISBN 978-92-76-12012-4    doi:10.2759/465913   KK-02-19-849-EN-N

Neither  the  European  Commission  nor  any  person  acting  on  behalf  of  the  Commission  is  responsible  for  the  use  which 
might be made of the following information. The contents of this working document are the sole responsibility of the High-
Level  Expert  Group  on  Artificial  Intelligence  (AI  HLEG).  Although  Commission  staff  facilitated  the  preparation  of 
the Guidelines, the views expressed in this document reflect the opinion of the AI HLEG and may not in any circumstances 
be regarded as reflecting an official position of the European Commission.  

More information on the High-Level Expert Group on Artificial Intelligence is available online (https://ec.europa.eu/digital-
single-market/en/high-level-expert-group-artificial-intelligence).  
The reuse policy of European Commission documents is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p.39). 
For  any  use  or  reproduction  of  photos  or  other  material  that  is  not  under  the  EU  copyright,  permission  must  be 
sought directly from the copyright holders. 

 
 
 
 
 
 
TABLE OF CONTENTS 

INTRODUCTION 

      6 

CHAPTER I: USING TRUSTWORTHY AI TO BUILD A POSITIVE IMPACT IN EUROPE  9 

A. 

B. 

C. 

D. 

Empowering and Protecting Humans and Society 
1.     Empower humans by increasing knowledge and awareness of AI 
2.     Protect the integrity of humans, society and the environment 
3.     Promote a human-centric approach to AI at work 
4.     Leave no one behind 
5.     Measure and monitor the societal impact of AI 

Transforming Europe's Private Sector 
6.     Boost the uptake of AI technology and services across sectors in Europe 
7.     Foster and scale AI solutions by enabling innovation and promoting technology transfer 
8.     Set up public-private partnerships to foster sectoral AI ecosystems 

Europe's Public Sector as a Catalyst of Sustainable Growth and Innovation 
9.      Provide human-centric AI-based services for individuals 
10.    Approach the Government as a Platform, catalysing AI development in Europe 
11.    Make strategic use of public procurement to fund innovation and ensure trustworthy AI 
12.    Safeguard fundamental rights in AI-based public services and protect societal infrastructures 

Ensuring World-Class Research Capabilities 
13.    Develop and maintain European strategic AI research roadmap 
14.    Increase and streamline funding for fundamental and purpose-driven research 
15.    Expand AI research capacity in Europe by developing, retaining and acquiring AI researchers 
16.    Build a world-class European research capacity 

CHAPTER II. LEVERAGING EUROPE'S ENABLERS FOR TRUSTWORTHY AI 

E. 

F. 

Building Data and Infrastructure for AI 
17.    Support AI infrastructures across Member States 
18.    Develop legally compliant and ethical data management and sharing initiatives in Europe 
19.    Support European leadership in the development of an AI infrastructure 
20.    Develop and support AI-specific cybersecurity infrastructures 

Generating appropriate Skills and Education for AI 
21.    Redesign education systems from pre-school to higher education 
22.    Develop and retain talent in European higher education systems 
23.    Increase the proportion of women in science and technology 
24.    Upskill and reskill the current workforce 
25.    Create stakeholder awareness and decision support for skilling policies 

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G. 

Establishing an appropriate governance and regulatory framework 
26.    Ensure appropriate policy-making based on a risk-based and multi-stakeholder approach 
27.    Evaluate and potentially revise EU laws, starting with the most relevant legal domains 
28.    Consider the need for new regulation to ensure adequate protection from adverse impacts 
29.    Consider whether existing institutional structures, competences and capacities need revision to ensure    
          proportionate and effective protection 
30.    Establish governance mechanisms for a Single Market for Trustworthy AI in Europe 

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41 

H. 

Raising Funding and Investment 
31.    Ensure adequate funding for the recommendations put forward in this document 
32.    Address the investment challenges of the market 
33.    Enable an open and lucrative climate of investment that rewards Trustworthy AI 

CONCLUSION: KEY TAKEAWAYS 

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INTRODUCTION 

In its various communications on artificial intelligence (AI)1 the European Commission has set out its vision 
for AI, which is to be trustworthy and human-centric. Three pillars underpin the Commission’s vision: (i) 
increasing  public  and  private  investments  in  AI  to  boost  its  uptake,  (ii)  preparing  for  socio-economic 
changes, and (iii) ensuring an appropriate ethical and legal framework to protect and strengthen European 
values. To support  the  implementation of this vision, the Commission established the High-Level Expert 
Group  on  Artificial  Intelligence  (AI  HLEG),  an  independent  group  mandated  with  the  drafting  of  two 
deliverables: a set of AI Ethics Guidelines and a set of Policy and Investment Recommendations.2  

In  our  first  deliverable,  the  Ethics  Guidelines  for  Trustworthy  AI3  published  on  8  April  2019  (Ethics 
Guidelines),  we  stated that  AI  systems  need  to  be  human-centric,  with  the  goal  of  improving  individual 
and societal well-being, and worthy of our trust. In order to be deemed trustworthy, we put forward that 
AI  systems  –  including  all  actors  and  processes  involved  therein  –  should  be  lawful,  ethical  and  robust. 
Those  Guidelines  therefore  constituted a first  important step  in  identifying the  type of AI that we  want 
and  do  not  want  for  Europe,  but  that  is  not  enough  to  ensure  that  Europe  can  also  materialise  the 
beneficial impact that Trustworthy AI can bring. 

Taking the next step, this document contains our proposed Policy and Investment Recommendations for 
Trustworthy AI, addressed to EU institutions and Member States. Building on our first deliverable, we put 
forward  33  recommendations  that  can  guide  Trustworthy  AI  towards  sustainability,  growth  and 
competitiveness,  as well as inclusion  –  while empowering, benefiting and protecting human beings. We 
believe that EU Institutions and Member States will play a key role in the achievement of these goals, as a 
pivotal  player  in  the  data  economy,  a  procurer  of  Trustworthy  AI  systems  and  as  a  standard-setter  of 
sound governance. 

Our  recommendations  focus  on  four  main  areas  where  we  believe  Trustworthy  AI  can  help  achieving  a 
beneficial  impact,  starting  with  humans  and  society  at  large  (A),  and  continuing  then  to  focus  on  the 
private  sector  (B),  the  public  sector  (C)  and  Europe’s  research  and  academia  (D).  In  addition,  we  also 
address  the  main  enablers  needed  to  facilitate  those  impacts,  focusing  on  availability  of  data  and 
infrastructure (E), skills and education (F), appropriate governance and regulation (G), as well as funding 
and investment (H).  

These recommendations should not be regarded as exhaustive, but attempt  to tackle the most pressing 
areas  for  action  with  the  greatest  potential.  Europe  can  distinguish  itself  from  others  by  developing, 
deploying,  using  and  scaling  Trustworthy  AI,  which  we  believe  should  become  the  only  kind  of  AI  in 
Europe, in a manner that can enhance both individual and societal well-being. 

Europe is well placed to seize AI’s opportunities 

Europe is entering the third wave of digitalisation, but the adoption of AI technologies is still in its infancy. 
The first wave involved primarily connection and networking technology adoption, while the second wave 

1  

2  

3  

European Commission communications published on 25 April 2018 (COM(2018) 237 final), 7 December 2018 (COM(2018) 
795  final)  and  8  April  2019  (COM(2019)  168  final).  For  our  definition  of  Artificial  Intelligence  (AI),  please  refer  to  our 
document  published  on  8  April  2019:  “A  Definition  of  AI:  Main  Capabilities  and  Disciplines”  which  was  prepared  for  the 
purpose of our deliverables and can be found here: 
https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341.  
For more information on the AI HLEG, see  https://ec.europa.eu/digital-single-market/en/high-level-expert-group-artificial-
intelligence. 
These are available at: https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.   

6 

 
 
 
                                                           
was driven by the age of big data. The third wave is characterised by the adoption of AI which, on average, 
could  boost  growth  in  European  economic  activity  by  close  to  20%  by  2030.4  In  turn,  this  will  create  a 
foundation for a higher quality of life, new employment opportunities, better services, as well as new and 
more sustainable business models and opportunities.  

However, as is the case with any technology, AI can be deployed both in a manner that increases human 
well-being and that may lead to potential harm. Therefore, while it creates a great opportunity for Europe, 
it  also  implies  certain  risks  of  which  policy-makers  are  increasingly  aware.  This  may  necessitate  specific 
and targeted governance measures that provide appropriate safeguards to protect individuals and society. 
In this report, we make recommendations to position Europe so that it can maximise the extent to which 
it can benefit from the opportunities presented by AI, while simultaneously ensuring that these benefits 
are felt throughout the entire European society, and that any risks are prevented or minimised. 

When  analysing  the  international  landscape  regarding  key  markets  and  investments  for  AI,  there  are 
significant  differences  in  focus  across  the  largest  economies  in  the  world,  including  the  U.S.,  China  and 
Europe.  The  market  for  AI  is  much  broader  than  business-to-consumer  (B2C).  More  specifically,  it  also 
includes  business-to-business  (B2B)  platforms  and  markets,  in  which  substantial  industrial  value  is 
created.  In  fact,  studies  indicate  that  one  third  of  the  value  created  by  AI  is  contributable  to  the  B2C 
segment, and two-thirds to the B2B, where Europe has a traditionally strong footprint.5 

In addition, a third segment can be identified when considering public-to-citizens (P2C) services that are 
increasingly procuring and deploying digital and AI-enabled solutions and where the trust of individuals is 
an  even  more  crucial  prerequisite.  The  P2C  context  or  Digital  Government  is  emerging  very  rapidly, 
leading  to  a  potential  revolution  in  the  role  and  structure  of  government  and  its  relationship  with 
individuals and businesses.6 

Within  each  of  these  three  segments,  an  additional  level  of  granularity  is  needed  to  understand  their 
underlying logic. Therefore, we should disentangle AI systems that consist of a digital element only, from 
AI  systems  that  combine  both  digital  and  physical  components.  This  leads  to  a  more  comprehensive 
picture,  comprised  of  six  different  yet  connected  segments  with  their  own  specific  logic  for  growth, 
competitiveness and governance. 

This more holistic vision lends itself to the creation of a European Single Market for Trustworthy AI, where 
Europe  is  in  an  exceptional  position  to  put  tailored  policy  and  investment  measures  in  place  that  can 
enable it to seize the benefits and capture the value of AI, while minimising and preventing its risks.  

4  
5  
6  

See Notes from the AI Frontier: Tackling Europe’s Gap in Digital and AI, McKinsey Global Institute, 2019. 
See Notes from the AI Frontier: Modelling the Impact of AI on the World Economy, McKinsey Global Institute, 2018. 
See  Deloitte 
https://www2.deloitte.com/insights/us/en/focus/artificial-intelligence-in-government.html.  

Insights,  How 

government 

intelligence 

transform 

artificial 

(2017), 

could 

available 

at: 

7 

 
 
 
 
 
                                                           
CHAPTER I 

USING TRUSTWORTHY AI TO BUILD A POSITIVE 
IMPACT IN EUROPE 

8 

 
 
 
 
 
 
 
 
 
 
 
CHAPTER I: USING TRUSTWORTHY AI TO BUILD A POSITIVE IMPACT IN EUROPE 

In  this  chapter,  we  focus  on  four  major  areas  of  impact  that  merit  a  specific  focus:  (A)  Humans  and 
Society, (B) the Private Sector, (C) the Public Sector and (D) Research and Academia. 

(A)  As  already  stated  in  our  Ethics  Guidelines,  in  building  a  future  with  AI,  our  point  of  departure  is 
human-centricity. By placing the human at the centre of our thinking, we underscore the fact that AI is 
not an end in itself, but a means to enhance human well-being and freedom. All policy recommendations 
that we put forward in this document have this as their direct or indirect goal. Human-centricity, however, 
not only implies attention to individuals, but also to the well-being of society at large and the environment 
that humans live in. Europe should champion the use of AI towards sustainable development in line with 
the Agenda 2030. 

(B)  For  Europe  to  tackle  the  opportunities  and  challenges  created  by  AI,  it  needs  a  thriving  and  vibrant 
private sector. The private sector is a key player in generating economic growth and creating relevant and 
growing employment opportunities through its success. The uptake and scaling of AI solutions as well as 
the  stimulation  of  innovation  and  technology  transfer  can  help  achieving  this.  At  the  same  time,  a  key 
success factor for the private sector is the ability to understand the specific needs in different businesses 
and sectors. Collaboration with the public sector and civil society, research and academia as well as with 
policy-makers  is  essential  for  gaining  such  understanding  and  generating  AI-enabled  solutions  to  the 
benefit of all. 

(C) Equally, the public sector has a crucial role to play at both individual and societal level in the adoption 
of Trustworthy AI. The current transformation of governments towards more efficient and digitised public 
services  might  represent  a  fundamental  shift  in  the  way  in  which  the  economy  and,  in  particular,  the 
Single Market, is structured and organised. Harnessing the public sector “as a platform” could lead to new 
opportunities for researchers and entrepreneurs to gain access to data and infrastructure for developing 
welfare-enhancing  AI  solutions  through  privacy-preserving  means.  The  public  sector  can  make  strategic 
use of public procurement to foster responsible innovation, as well as steering it towards tackling societal 
challenges and the development of trustworthy AI solutions. Moreover, by delivering higher quality, more 
targeted and thus more effective services to individuals and groups where appropriate, it can also act as a 
catalyst for innovation and growth.  

(D) The  bedrock of any successful transition is the ability to understand the  changes  we are facing. The 
role of research and academia is therefore essential in creating the foundational layer for all actors and 
activities  needed  for  Europe  to  master  these  changes.  Research,  both  academic  and  industry-based, 
enables  us  to  understand  and  address  emerging  challenges  and  can  help  turn  the  development  of  AI 
towards  the  common  good.  It  can  also  provide  the  interdisciplinary  and  multidisciplinary  mix  that  is 
needed to ensure AI’s trustworthiness, and foster our understanding  on the impact of new and existing 
policies.  Research  is  also  an  engine  of  creativity,  beneficial  innovation  and  new  ventures  through 
technology transfer, offering solutions that can be embedded into useful products. Finally,  research and 
academia  are  key  providers  of  education  and  skills,  which  are  essential  in  a  world  where  “intelligent” 
systems perform an increasing number and variety of tasks. 

In what follows, we elaborate on each of those areas, indicating which impact we wish to achieve with AI 
and providing a number of recommendations on how this can be accomplished. 

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A.  Empowering and Protecting Humans and Society 

AI presents a promising means to enhance individual and societal well-being and the 
common  good,  as  well  as  driving  progress  and  responsible  innovation.  Yet  it  also 
carries  risks  for humans and  societies, which  need to be identified and addressed. 
Hence,  we  need  to  foster  AI  solutions  that  can  empower  human  beings,  and 
monitor the impacts they create, ensuring that this happens in a way that protects 
our  rights  and  values.  It  is  therefore  essential  that  individuals  gain  awareness,  knowledge  and 
understanding  of  the  capabilities,  challenges  and  limitations  of  AI  systems,  and  of  their  rights  related 
thereto.   

The  impact  of  AI  systems  on  individuals  and  society  at  large  needs  to  be  better  understood,  so  as  to 
enable actions to guide the technology’s development and use towards the vision we set out in our Ethics 
Guidelines. This requires research, new monitoring and measuring mechanisms, as well as measures that 
can protect individuals and society from potential adverse impact generated by the technology. Below, we 
reflect on the policies that should be put in place to achieve this goal.  

1.

EMPOWER HUMANS BY INCREASING KNOWLEDGE AND AWARENESS OF AI  

In order to build a society supported by AI that we would like to live in, it is imperative that not only AI 
experts,  but  also  society  at  large  is  aware  of  the  possibilities  and  limitations  of  the  application  of  AI 
systems. The following actions are recommended in this respect: 

1.1  Encourage Member States to increase digital literacy through courses (e.g. MOOCs) across Europe 
providing  elementary  AI  training.  This  includes  fostering  the  understanding  of  AI  systems  more 
generally  (including  a  basic  understanding  of  machine  learning  and  reasoning),  but  also  raising 
awareness  of  data  protection  rights,  an  understanding  of  how  (personal)  data  can  be  used,  the 
implications  of  digital  tracking,  and  the  importance  of  issues  such  as  fairness,  explainability, 
transparency, robustness of AI systems, and knowledge of these topics. Efforts need to be made to 
ensure  that  such  courses  are  accessible  to  all,  taking  due  account  of  the  digital  divide  and  paying 
particular attention to the lower skilled and disadvantaged. A universal design approach should be 
inherent in all of these programs. The outreach and popularity of national public broadcasters can 
also  be  leveraged  to  develop  educational  programmes  on  AI,  outlining  its  main  opportunities, 
possible risks and ways to mitigate them.  

1.2  Encourage  Member  States  to  support  and  further  develop  basic  education  on  AI  and  digital 
literacy, particularly in primary, secondary and tertiary education systems, as well as beyond. This 
includes  supporting  young  people  through  EU  youth  policies  and  funds  who  are  leading  projects 
aiming at educating and informing individuals on opportunities and challenges of new technologies. 
However, it also includes fostering AI knowledge  amidst the less young, encompassing employees, 
executives,  civil  servants,  policy-makers  and  all  other  individuals.  The  education  and  nurturing  of 
STEM and more specifically AI experts not only as technology drivers, but also as enablers for society 
on  all  levels,  needs  to  be  emphasised,  supported and extended.  Further  recommendations  on  the 
fostering of skills and education can be found under Section F. 

1.3  Create an AI competence framework for individuals, including a focus on the core skills required. 
This includes directing the Digital Competence Framework for Citizens7 – as a tool to improve digital 

7  

For  more  information,  see  https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/digcomp-
21-digital-competence-framework-citizens-eight-proficiency-levels-and-examples-use. 

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1.4 

1.5 

competences.  All individuals should be  empowered with the  necessary skills to use  AI technology, 
beyond being recipients of information on the technology. In addition, they should be able to make 
use  of  tools  to  challenge  public  governance  through  AI  systems,  as  addressed  under 
Recommendation 12.3 below. 

Institutionalise  a  dialogue  between  policy-makers,  developers  and  users  of  AI  technology,  for 
instance through the European AI Alliance, on the ethical and legal limits of AI and examine how the 
policy and regulatory framework needs to be further developed in order to guarantee legal certainty 
and  foster  beneficial  innovation  while  ensuring  due  respect  for  human  rights,  democracy  and  the 
rule of law. A widespread mechanism of public consultation should be ensured on the most ethically 
sensitive  issues  revolving  around  AI  systems  with  a  significant  impact  on  society  or  individuals. 
Besides enabling a dialogue through the European AI Alliance, such mechanisms are also needed at 
Member State level to respect linguistic and cultural diversity.   

Inform  the  public  at  large  about  free  available  resources  on  AI  that  they  can  use  to  learn  and 
experiment (e.g. algorithms and data), to discuss (e.g. via blogs) and to share best practices. This 
could  be  done  by  creating  a  repository  on  the  European  AI  Alliance  platform.  Moreover,  publicly 
financed  and  subsidised  programmes  in  the  media  and  social  networks  as  well  as  non-formal 
education  organisations  that  provide  training  and  awareness-raising  on  data  protection,  coding, 
robotics and data (including data protection), should be supported. 

1.6  Establish a yearly European AI Awareness Day (for instance, on the birthday of Alan Turing) with 
awareness  material  to  be  used  for  the  event.8  This  should  be  accompanied  by  a  European  AI 
Awareness  website,  which  could  also  provide  answers  to  common  questions  related  to  AI  (in  the 
form of a “FAQ”). 

2.

PROTECT THE INTEGRITY OF HUMANS, SOCIETY AND THE ENVIRONMENT 

AI can provide tools to help safeguarding the integrity of our public institutions and the private sector (e.g. 
by  detecting  and  proving  discrimination),  the  privacy  of  individuals  (e.g.  by  detecting  and  rooting  out 
privacy infringing content) as well as the environment (e.g. by improving the detection of pollutants and 
their sources). However, if not applied in a trustworthy manner, AI systems could cause adverse impacts 
to individuals, society and the  environment,  such as unjust  discrimination or bias, privacy infringement, 
social  or  economic  exclusion  or  environmental  decline.  Adequate  protection  should  be  put  in  place  to 
counter such impacts. 

2.1  Refrain from  disproportionate and  mass surveillance of  individuals.  While there may  be  a strong 
temptation for governments  to “secure  society” by building a pervasive  surveillance  system  based 
on AI systems, this would be extremely dangerous if pushed to extreme levels. Governments should 
commit  not  to  engage  in  mass  surveillance  of  individuals  and  to  deploy  and  procure  only 
Trustworthy AI systems, designed to be respectful of the law and fundamental rights, aligned with 
ethical principles and socio-technically robust.  

2.2  Commercial surveillance of individuals  (particularly consumers) and society should be countered, 
ensuring  that  it  is  strictly  in  line  with  fundamental  rights  such  as  privacy  –  also  when  it  concerns 
“free” services – taking into consideration the effects of alternative business models. 

2.3  Consideration  should  be  given  to  power  asymmetries  between  institutions,  businesses  and 
individuals  arising  from  the  growth  of  digital  devices  and  systems  and  the  rapid  expansion  of 

8  

This could for instance include a “1 hour AI” initiative, similar to 1-hour code initiatives (see e.g. https://hourofcode.com). 

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digital  data  that  they  generate.  Where  appropriate,  for  instance  in  situations  of  individual  or 
collective  harm,  consideration  should  be  given  to  whether  potential  measures  may  be  needed  to 
address this. 

2.4 

Introduce  a  mandatory  self-identification  of  AI  systems.  In  situations  where  an  interaction  takes 
place between a human and an AI system, and whenever there is a reasonable likelihood that end 
users could be led to believe that they are interacting with a human, deployers of AI systems should 
be attributed a general responsibility to disclose that in reality the system is non-human.  This goes 
hand-in-hand with ensuring the transparency of AI systems.  

2.5  Foster  the  development  of  AI  solutions  that  address  sustainability  challenges,  by  launching 
competitions and missions for AI solutions tackling specific environmental problems, strengthening 
this  component  in  the  Horizon  Europe  missions,  and  enact  a  circular  economy  plan  for  digital 
technologies  and  AI  in  particular  to  incentivise  companies  to  reduce  the  carbon  footprint  of  data 
centres and devices (including smartphones). Consideration must be also given to the sustainability 
of  big  data-driven  AI  and  modern  computing  and  AI  architectures  to  ensure  that  the  process  of 
developing AI products and services also does not have an undue sustainability impact.  

3.

PROMOTE A HUMAN-CENTRIC APPROACH TO AI AT WORK 

Over the past years, an important debate has taken place on the impacts that the increased diffusion of AI 
systems and, more generally, the digital transformation of the economy will have on the labour market. 
The European Commission appointed a dedicated High Level Expert Group9 on this issue, which released a 
report10 in April 2019. A few months earlier, the EGE11 likewise dedicated a report12 on this topic. We refer 
to that report for a more detailed analysis of existing trends and possible measures to ensure that digital 
technologies  do  not  negatively  disrupt  the  workplace,  and  that  adequate  social  protection,  collective 
representation and training are offered to European workers. Here below, we will limit ourselves to those 
recommendations  which  we  believe  are  crucial  to  also  take  up  in  this  document,  against  the  broader 
context  of  the  goals  we  set  out  to  achieve,  particularly  focusing  on  the  impact  of  the  use  of  AI  in  the 
workplace. We note that this section also covers the domestic working environment, covering those who 
engage in domestic work, as well as situations where work is carried out on an unpaid basis.   

3.1  Promote  the  research,  development  and  deployment  of  human-centric  AI  systems  in  work 
contexts without  stifling  socially  beneficial  innovation. A  careful  consideration  of  challenges 
associated with socio-technical work systems integration, be it issues of safety and security, human 
oversight  or  deskilling,  will  allow  the  full  exploitation  of  AI-driven  automation. Research  funding, 
technology  transfer  and  deployment,  tax  policy  and  government  demand-side  policies  should  be 
aimed  at creating  collaborative  AI-human  work  systems without  preventing  innovation  and  aiming 
to  avoid  menial  jobs,  especially  in  situations  where  automation  is  a  cost-efficient  alternative, 
enabling governments to allocate funds for the creation of more productive jobs and improving the 
social  well-being  of 
to 
Europe. Moreover, human-empowering AI  applications  could  also  create  new  job  opportunities  in 
particular for those who – because of natural restrictions such as disabilities or living circumstances 

also  drive the 

individuals. This 

reshoring  of 

could 

jobs 

9  

10  
11  

12  

For  more  information  on  this  expert  group,  see:  https://ec.europa.eu/digital-single-market/en/high-level-expert-group-
impact-digital-transformation-eu-labour-markets. 
Available at: https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=58412.  
The European Group on Ethics in Science and New Technologies (EGE) is an independent expert group established by the 
European Commission and focusing on ethical aspects of science and technology relating to EU legislation and policies.  
Available at: https://ec.europa.eu/info/sites/info/files/research_and_innovation/ege/ege_future-of-
work_opinion_122018.pdf. 

12 

 
 
 
                                                           
–  were  initially  bound  to  less  qualified  jobs.  Automation  can  also  serve  humans  with  natural 
restrictions in making their lives more convenient and meaningful. 

3.2  Encourage  automation  of  dangerous  tasks  and  when  humans  are  put  at  risk,  to  ensure  safe 
working  conditions  at  work.  Special  efforts  should  be  made  to  find  ways  of  automating  tasks  in 
which workers are put in danger (e.g. facing accidents, harsh environmental conditions, or long-term 
ailments of exposure to harmful substances) even if these are less cost-efficient than using a human 
workforce.  European  institutions  should  put  efforts  to  increase  awareness  of  the  value  of 
automation in such situations and support organisations through funding of special projects, aiming 
to avoid menial jobs and dangerous working conditions. 

3.3  Apply a  process  of  representation,  consultation and,  where possible,  co-creation, where  workers 
are involved in the discussion around AI production, deployment or procurement process in order to 
ensure that the systems are useable and that the worker still has sufficient autonomy and control, 
fulfilment  and  job  satisfaction.  This  implies  informing  and  consulting  workers  when  developing  or 
deploying  AI,  as  set  out  in  the  existing  texts  adopted  by  the  European  institutions  and  the  social 
partners.13  Workers  (not  only  employees  but  also  independent  contractors)  should  be  involved  in 
discussions  around  the  development,  deployment  or  procurement  of  algorithmic  scheduling  and 
work  distribution  systems,  to  ensure  compliance  with  health  and  safety  legislation,  data  policy, 
working time legislation and work-life balance legislation. Social dialogue plays a key role to enable 
this. 

3.4  Map  value  chains  in  Europe  and  engage in horizon  scanning  to gain an understanding of  which 
skills will become less relevant, and which ones will be more in demand or at risk of shortage. This 
can enable a more targeted policy to help workers transition between jobs or acquire necessary new 
skills, to incentivize organisations to conduct such mapping at organisational level, to anticipate the 
new  skills  that  their  workers  may  need,  and  to  foster  the  development  of  those  skills  in  a  timely 
manner.  

3.5  Establish  a  full-fledged  European  transition  fund  to  help  managing  the  AI  transformation  in  a 
socially  responsible  way.  This  can  be  aligned  with  the  process  of  reinforcing  the  European 
Globalisation Adjustment Fund.  

4.

LEAVE NO ONE BEHIND 

The benefits of AI should be shared with all parts of society, leaving no one behind. This is also in line with 
the Agenda 2030, and should be actively pursued by the EU and Member States.  

4.1 

13  

Introduce a duty of care for developers of consumer-oriented AI systems to ensure that these can 
be  used  by  all  intended  users,  fostering  a  universal  design  approach,  and  do  not  lead  to  the 
exclusion  of  users  with  disabilities,  particularly  when  used  in  public  services.  More  generally,  AI 
developers  should  be  incentivised  to  adopt  a  universal  design  approach  to  AI,  which  entails 
considering  the  benefits  and  concerns  relating to  all  societal  groups,  such  as the  elderly,  children, 

about 

See for instance Articles 151 and following of the Treaty on the Functioning of the EU, dealing with social policy. For more 
information  on  European  social  dialogue,  see  https://ec.europa.eu/social/main.jsp?langId=en&catId=329.  For  more 
information 
see 
action 
https://ec.europa.eu/social/main.jsp?catId=707&langId=en.  In  this  regard,  see  also  the  Declaration  of  European  social 
partners 
at: 
on 
adopted 
https://www.businesseurope.eu/sites/buseur/files/media/position_papers/social/2016-03-16_tss_-
_statement_on_digitalisation.pdf.    

digitalisation, 

involvement, 

concerning 

accessible 

employee 

Directives 

March 

2016, 

and 

EU 

on 

16 

13 

 
 
 
                                                           
people  with  disabilities  and  other  groups  at  risks  of  exclusion  such  as  migrants,  people  living  in 
remote areas and people living in poverty.  

4.2  Encourage  the  development  of  AI  tools  and  applications  that  are  specifically  targeted  to  help 
vulnerable demographics, for instance through the organisation of open challenges or competitions. 
Resources should be devoted to yearly “AI for good” prizes, which reward the efforts to address key 
societal  challenges,  thereby  tackling  inequalities  and  ensuring  that  AI  is  deployed  also  to  tackle 
problems which are experienced by a minority of the users, and are therefore seldom addressed by 
the  market.  AI  should  be  developed  with  due  regard  to  all  grounds  that  are  protected  from 
discrimination  in  EU  law,  which  also  includes  –  as  well  as  some  of  the  grounds  listed  above  –  the 
prohibition of discrimination on the ground of sex.14  

4.3  Establish  a  European  Strategy  for  Better  and  Safer  AI  for  Children,  in  line  with  the  European 
Strategy  for  a  Better  Internet  for  Children,  designed  to  empower  children,  while  also  protecting 
them  from  risks  and  potential  harm.  The  integrity  and  agency  of  future  generations  should  be 
ensured by providing Europe’s children with a childhood where they can grow and learn untouched 
by unsolicited monitoring, profiling and interest invested habitualisation and manipulation. Children 
should be ensured a free and unmonitored space of development and upon moving into adulthood 
should be provided with a “clean slate” of any public or private storage of data related to them.15 
Equally, children’s formal education should be free from commercial and other interests.  

5.

  MEASURE AND MONITOR THE SOCIETAL IMPACT OF AI 

We need to understand the possibilities and limitations of AI systems as a means to build the future we 
want, and the impact they have on individuals and society – not only in the short but also in the long term.   

5.1  Encourage research and development on the impact of AI on individuals and society, including the 
impact  on  jobs  and  work,  social  systems  and  structures,  equality,  democracy,  fundamental  rights, 
the  rule  of  law,  human  intelligence,  the  development  of  (cognitive  skills  of)  children.  Encourage 
conducting  an  evaluation  of  the  societal  impact  of  AI  as  a  requirement  in  AI  research  and 
development in parallel with funding for the development of a particular AI system. 

5.2  Support research and the development of tools to enable independent testing of AI systems by civil 
society  organisations  and  other  independent  parties  to  inform  individuals  about  the  most 
trustworthy AI solutions in the market or used by public service providers. 

5.3  Establish monitoring mechanisms at national and EU level to continuously analyse, measure and 
score the societal impact of AI. This can help us keep track of the positive and negative impact of AI 
on society and enable us to adapt or redirect strategies and policies on a continuous basis. A variety 
of indices can be considered to measure and score AI’s societal impact such as the UN Sustainable 
Development  Goals  and  the  Social  Scoreboard  Indicators  of  the  European  Social  Pillar.  The  EU 
statistical programme of Eurostat, as well as other relevant EU Agencies, should be included in this 
mechanism  to  ensure  that  the  information  generated  is  trusted,  of  high  and  verifiable  quality, 
sustainable and continuously  available. AI-based solutions can help the  monitoring and measuring 
its societal impact. 

14  

15  

See in this regard also Article 21 of the Charter of Fundamental Rights of the European Union: Any discrimination based on 
any ground such as sex, race, colour, ethnic or social origin, genetic features, language, religion or belief, political  or any 
other opinion, membership of a national minority, property, birth, disability, age or sexual orientation shall be prohibited.  
This  does  not  exclude  the  possibility  for  appropriate  exceptions  that  are  necessary  in  a  democratic  society  and  in  full 
compliance with fundamental rights.  

14 

 
 
                                                           
B.  Transforming Europe's Private Sector  

Despite the sizeable impact that European businesses expect from AI, only a small 
fraction  of  them  use  AI  actively  in  industrial  and  commercial  operations.  This  is 
particularly visible among small and medium enterprises (SMEs) that make up over 
99% of Europe’s businesses.16 Furthermore, since SMEs account for 56% of Europe's total turnover, it is 
likewise  important  that  larger  companies  in  Europe  accelerate  their  transformation.17  In  2018,  three 
quarters of European businesses did not adopt AI in any shape or form, just under a single quarter were in 
piloting and testing phases and reported difficulties of scaling, and only 2-3% of companies incorporated 
AI across their whole organisation.18 A multi-stakeholder approach, bringing all actors around the table to 
join forces, is crucial to foster sectoral AI ecosystems where most of the transformative power of AI and 
sustainable growth is expected. 

6.

  BOOST THE UPTAKE OF AI TECHNOLOGY AND SERVICES ACROSS SECTORS IN EUROPE 

The  adoption  of  AI  is  an  important  part  of  the  digitalisation  wave  that  European  industries  need  to 
undertake in order to adapt themselves to the demands of the  rapidly evolving digital economy. Policy-
makers  should  foster  their  transformation,  with  particular  attention  to  SMEs  who  form  the  important 
foundation of European economy. The uptake and acceleration of AI should happen both on the supply 
and demand side, through enabling policy and investment mechanisms. 

6.1  Earmark  significant  resources  in  the  InvestEU  programme  to  support  the  transformation  of 
European  enterprises  towards  AI-enabled  solutions.  Such  funding  should  be  made  available  for 
companies  of  all  sizes  and  in  all  sectors,  and  should  focus  on  the  uptake  and  incorporation  of 
Trustworthy AI solutions.  

6.2  Create  an  easy  avenue  for  start-ups  and  SMEs  to  funding  and  advice.  InvestEU,  the  European 
Innovation  Council  and  other  programmes,  together  with  the  network  of  Digital  Innovation  Hubs, 
should particularly help SMEs to define their AI transition plan and needs, propose attractive loans 
to finance their transformation, temporarily support the salaries of AI specialists to recruit, as well as 
financially supporting the re- or up-skilling of the workforce that is at risk of losing jobs or in need of 
a significant transformation of their jobs due to AI. This should also include support and advice on 
investment plans and intellectual property rights management. 

6.3  Foster the availability of legal and technical support  to implement Trustworthy AI solutions that 
comply  with  the Ethics  Guidelines.  The  network  of Digital  Innovation  Hubs  should  be  exploited  in 
this context, particularly to provide technical know-how to smaller companies who do not have the 
availability of sufficient resources and expertise for this purpose.   

6.4  Encourage companies to form partnerships with training programmes addressing all levels of AI 
training  (from  data  preparation  technicians  up  to  PhD  level  AI  specialists),  to  ensure  that  the 
content of the programmes combine state-of-the-art science and technology with practical aspects 
of AI-enabler industrial systems (e.g. development and test  methods, implementation and scaling, 
economics), to attract people with new skills, and to reskill their workforce so as to enable them to 

16  

17  
18  

For  more  information,  see  the  European  Commission's  latest  annual  report  on  SME's  (2017-2018),  accessible  at 
https://ec.europa.eu/growth/smes/business-friendly-environment/performance-review_en. 
See Eurostat statistics on small and medium-sized enterprises' total turnover in the EU.  
See for instance Artificial Intelligence in Europe, Outlook for 2019 and Beyond, EY (2018); PwC's Global Artificial Intelligence 
Study: Exploiting the AI Revolution (2017), and Artificial Intelligence Innovation Report, Deloitte (2018). 

15 

 
 
                                                           
work  with  AI  systems.  Incentivise  private  companies  to  invest  in  the  re-  and  up-skilling  of  their 
existing workforce, for instance through the above-mentioned partnerships.  

7.

FOSTER AND SCALE AI SOLUTIONS BY ENABLING INNOVATION AND PROMOTING TECHNOLOGY TRANSFER  

Next  to  university  and  institutional  research,  industrial  research  also  plays  a  key  role  in  fostering  AI 
innovation, especially with regard to the transfer of technology into the market economy to the benefit of 
consumers  or  business  users.  Such  innovation  should  be  incentivised,  for  instance  by  establishing 
competitions, creating recognised standards and encourage open access on FRAND terms (fair, reasonable 
and non-discriminatory) to facilitate technology transfer. This can be achieved by increasing support for 
the emergence and growth of companies that provide AI enablers and solutions, or that necessitate new 
technology and skills to transform. This includes start-ups and scale-ups, SMEs and larger companies, as 
well as sector-specific ecosystems. The emergence and growth of AI companies in Europe producing and 
exporting high-tech products, and competing on a global scale should be supported. Particular attention 
should be given to Europe's strong position in the B2B segment, which contributes a significant part of the 
industrial value in Europe.19  

7.1.  Boost  the  development  and  growth  of  AI  technology  firms  in  Europe  through  the  InvestEU 
programme.  Those  companies  can  also  help  drive  the  transformation  of  non-tech  companies  by 
supplying AI-enabled goods and services enabling efficiency gains and growth. Such funding should 
cover  all  stages  of  investments  (from  seed  money  for  start-ups  to  larger  scale  A  or  B  series 
investment,  including  growth  investment)  and  enable  Trustworthy  AI  solutions.  In  addition  to 
fostering  purely  software-developing  AI  companies,  we  should  also  encourage  SMEs  that  develop 
and manufacture AI-enabled hardware.  

7.2.  Facilitate  the  transition  of  AI  solutions  from  research  labs  to  testing  environments  and  to 
commercial markets. Spin-offs from AI research labs should be encouraged in order to enable open 
innovation and create a market of AI technology firms as well as create an attractive  European AI 
brand  for  talents  outside  academia.  Guidance  should  be  provided  to  ensure  the  participation  of 
industry in research collaborations and R&D-based innovation more generally without undermining 
intellectual property rights, market competition and opportunities for global cooperation.  

7.3.  Create  an  EU-wide  network  of  AI  business  incubators  that  connect  academia  and  industry.  This 
could be coupled with the creation of EU-wide Open Innovation Labs, which could be built further 
on the structure of the Digital Innovation Hub network.  

7.4.  Stimulate  beneficial  innovation  by  funding  EU  hackathons,  competitions and industry  challenge-
driven  research  missions  in  AI  across  various  sectors.  This  component  should  be  strengthened  in 
the  Horizon  Europe  programme.  Priority  may  be  considered  to  research  challenges,  data  and 
applications in B2B and P2C markets where Europe has a competitive edge to scale Trustworthy AI, 
and  where  disruptive  changes  could  be  created  through  substantial  research  efforts.  In  B2C 
segments,  such competitions can also be  steered towards applications ensuring a universal design 
approach  and  accessibility,  and  the  development  of  AI  products  and  services  for  creating  social 
good. Such initiatives may act as strong attractors of top talents from Europe and around the world, 
and  should  be  financed  by  substantial  public  funding  (jointly  by  the  European  Commission  and 
Member States), also with significant support by the industry. 

19  

See Notes from the AI Frontier: Modelling the Impact of AI on the World Economy, McKinsey Global Institute, 2018. 

16 

 
 
 
                                                           
8.

SET UP PUBLIC-PRIVATE PARTNERSHIPS TO FOSTER SECTORAL AI ECOSYSTEMS 

Individual companies form the nucleus of the economy. However, the biggest macro-economic impact is 
realised  through  economic  networks  or  ecosystems.  These  ecosystems  are  comprised  of  different 
stakeholders,  including  companies  of  different  types  and  sizes  (end-user  facing  and  subcontracting 
companies,  start-ups,  scale  ups,  SMEs,  large  companies)  but  also  research  institutions  bringing  latest 
innovation from lab to market, as well as the public sector (both as market actor and as policy-maker). We 
recommend that the uptake and scaling of AI systems in Europe be analysed and encouraged within the 
context  of  “enabling  AI  ecosystems”.20  This  needs  to  occur  on  a  sectoral  basis  to  gain  a  sufficiently 
granular understanding of the specific needs and challenges raised within the ecosystem.  

8.1 

8.2 

In  the  short  term,  conduct  a  sectoral-based  in-depth  analysis  of  several  selected  AI  ecosystems. 
Identify the opportunities and challenges in those sectors in terms of beneficial impact that can be 
achieved  with  AI  (for  humans  and  society,  private  sector  players,  public  sector  players  and  in 
research)  and  identify  the  requirements  to  enable  those  opportunities  in  terms  of  data  and 
infrastructure,  skills  building,  governance  and  regulation  as  well  as  funding  and  investment.  We 
propose to start this analysis in the second half of 2019 to better understand the specific needs of 
these sectoral AI eco-systems and to derive targeted recommendations both in terms of impacts to 
be achieved and enablers to be put in place.  

In the medium term, set up Sectoral Multi-Stakeholder Alliances (SMUHAs) for strategic sectors in 
Europe to build their AI ecosystems with the relevant stakeholders. These Alliances – similar to the 
concept  of  Public  Private  Partnerships  or  PPPs  –  should  bring  together  industry,  research  and 
academia, the public sector, civil society and user-focused organisations, as well as policy-makers to 
conduct a sector-based analysis of the challenges and opportunities generated by AI systems on a 
continuous basis. They should  take concrete  actions to meet the sector-specific needs in terms of 
targeted policies and enablers to tackle those challenges and opportunities.   

C.  Europe's Public Sector as a Catalyst of Sustainable Growth and Innovation  

Europe  has a strong public  sector that can play a significant  role when it comes  to 
the  uptake  and  scaling  of  Trustworthy  AI  and  establishing  a  Single  Market  for 
Trustworthy  AI  in  Europe.  The  EU’s  commitment  towards  the  modernisation  of 
public  administrations  was  already  confirmed  by  the  Ministerial  Declaration  on  e-
Government adopted in Tallinn on 6 October 2017.21 Deploying AI systems can help 
governments  make  better  evidence-based  policy-making  decisions,  deliver  better 
services  to  individuals,  groups  and  organisations  by  reducing  internal  costs, 

20  

Enabling  AI  ecosystems  can  be  viewed  as  “collaborative  arrangements  through  which  firms  combine  their  individual 
offerings  into  a  coherent,  customer-facing  solutions”.  See  R.  Adner,  Match  Your  Innovation  Strategy  to  Your  Innovation 
Ecosystem, Harvard Business Review 2006. Ecosystems function  conceptually as a means to understand the relationships 
between different organisations or parties that share a common motive, technology, platform or knowledge base. The AI 
ecosystem members are likely to have developed and deployed AI technology in different ways, to different extents and for 
different purposes. 

21   Ministers  in  charge  of  e-Government  policy  from  32 countries  of  the  European  Union  (EU)  and  the  European  Free  Trade 
Area (EFTA) unanimously committed to the vision laid out in the EU e-Government Action Plan 2016-2020 and in the new 
European Interoperability Framework that public administrations and public institutions in the EU should be open, efficient 
and  inclusive,  providing  borderless,  interoperable,  personalised,  user-friendly,  end-to-end  digital  public  services  to  all 
citizens  and  businesses  –  at  all 
levels  of  public  administration.  See  https://ec.europa.eu/digital-single-
market/en/news/ministerial-declaration-egovernment-tallinn-declaration. 

17 

 
 
 
 
                                                           
increasing  programme  effectiveness,  and  enhance  quality.22  This  should  not  lead  to  a  lower  quality  of 
human  relationships  within  public  services  or  a  reduction  of  such  services;  the  very  purpose  of  the 
contribution  of  AI  systems  in  the  public  sector  is  to be  human-centric,  and  lies  in  the  facilitation of  the 
tasks of civil servants to ensure better services to individuals.  

Public services  are critical to the  relationship between the state and citizens, groups and individuals.   AI 
has  the  potential  to  play  a  significant  role  in  improving  the  quality  and  efficiency  of  public  services, 
including empowering public servants, for example, through improving the accessibility and availability of 
information concerning public  services.   Given the capacity of AI services  to operate at scale,  at a highly 
granular level that can infer highly intimate information about individuals, these systems must be aligned 
with  the  concept  of  Trustworthy  AI.  For  instance,  the  development  and  deployment  of  those  systems 
should  occur  in  a  transparent  and  accountable  manner,  to  ensure  that  they  operate  in  ways  that  are 
consistent with the principles of good administration, respect for fundamental rights, democracy and the 
rule  of  law.  More  generally,  governments  have  the  crucial  task  to  safeguard  individuals'  fundamental 
rights, to protect them from harmful uses of AI, and to protect the integrity of public institutions.  

Today,  the  most  advanced  governments  are  increasingly  providing  application  programming  interfaces 
(APIs)  to  trusted  intermediaries  as  a  way  to  open  up  their  infrastructure  to  private  sector  services  and 
entrepreneurs,  taking  the  role  of  Government-as-a-Platform  (GaaP).23  In  this  manner,  the  public  sector 
can act as a catalyser for AI development in Europe. Through its role as a procurer, the public sector can 
also make use of public procurement strategies to not only incentivise the development and responsible 
innovation of AI systems for the public good, but also to promote responsible innovation and ensure such 
systems are trustworthy. 

9.

PROVIDE HUMAN-CENTRIC AI-BASED SERVICES FOR INDIVIDUALS  

9.1  Deliver  on  the  Tallinn  Declaration  on  e-Government24  and  put  mechanisms  in  place  to  provide 
borderless,  interoperable,  personalised,  user-friendly,  and  end-to-end  digital  public  services  to  all 
individuals  and  businesses  –  at  all  levels  of  public  administration.  Consider  adopting  a  proactive 
model  for  the  delivery  of  public  services  for  particular  contexts  and  services  in  which  they  might 
enhance the effectiveness and quality of public services whilst ensuring due respect for fundamental 
rights and the rule of law.25  

9.2  Where  an  AI-based  service  does  not  run  properly  or  when  an  individual  so  requests,  he  or  she 
should  be  able  to  interact  with  a  human  interlocutor,  when  there  is  a  significant  impact  on  the 
individual.  

9.3  Set up a single point of contact  for  individuals,  for example by deploying natural user interfaces 
that  can  redirect  individuals  to  the  sought  after  information  or  service  in  an  easily  accessible 

22  

23  
24  
25   

A  good  example  is  the  European  interoperability  framework  2016-2020,  but  also  the  GOV.UK  platform  developed  in  the 
United Kingdom, the “building blocks” model trialled by the European Commission by DG DIGIT and the Estonian X-Road 
platform, which even moved in the direction of a decentralized architecture. In the US, for instance, Deloitte considers that 
“automation could save 96.7 million federal hours annually, with potential savings of $3.3 billion; at the high end, this rises 
to 1.2 billion hours and potential annual savings of $41.1 billion”.  However, the main objective is not to make savings by 
cutting jobs in public services.   
O’Reilly, 2010, available at: http://chimera.labs.oreilly.com/books/1234000000774/ch02.html. 
See footnote 21 above. 
In Finland, the Aurora project which aims to bring such proactive services to currently undergoing a piloting phase, and the 
Finnish  parliament  has  recently  approved  a  law  about  the  secondary  use  of  data  that  contains  the  collection  and 
combination of data for such purposes.  

18 

 
 
 
                                                           
manner.26 Public services should invest in conversational user interfaces that can meet the needs of 
individuals  24/7,  serving  them  in  a  more  agile,  accessible  and  faster  way,  from  a  single  point  of 
contact. This could for instance be done through the use of chatbots or natural language interfaces 
with multilingual support, that can help individuals by redirecting them to the information or service 
that  they  seek,  and  that  could  also  simplify  the  filling  in  of  forms  in  a  conversational  manner. 
Feedback  mechanisms  that  allow  users  to  share  their  comments  on  the  interfaces  and  thus  help 
improving their AI models should be developed. Moreover, it must be ensured that such AI-enabled 
services are trustworthy, i.e. legal, ethical and robust.  

9.4  Develop  tools  to  ensure  that  public  services  can  be  deployed  for  all,  and  in  a  manner  that 
safeguards  individuals’  fundamental  rights,  democracy  and  the  rule  of  law.  Access  to  public 
services  through  natural  interfaces  should  be  ensured  for  the  entire  population,  regardless  of 
capability  (bearing  in  mind  for  instance  the  potential  hurdle  for  the  elderly  to  interact  with  those 
interfaces)  or  economic  class.  This  also  implies  ensuring  easy  access  for  all  to  the  internet,  which 
today is still not a reality.27 Moreover, individuals should always also be given the option to revert to 
a  human  interlocutor  based  on  the  principle  of  good  administration.28  Given  the  fact  that  some 
individuals  may  be  unable  to  access  or  make  use  of  AI-based  digital  services,  there  should  be  an 
option  to  revert  to  a  human  interlocutor.29
  Finally,  it  should  be  ensured  that  those  services  are 
provided without creating records of the request at an individual-identifiable level, and in a manner 
ensuring individuals' privacy. 

10.

  APPROACH THE GOVERNMENT AS A PLATFORM, CATALYSING AI DEVELOPMENT IN EUROPE 

10.1  Foster digitalisation by transforming public data into a digital format and continue collecting data 
that  can  be  used  to  evaluate  and  improve  public  services  as  well  as  to  develop  new  AI-based 
solutions.  In  case  it  concerns  personal  data,  it  should  be  ensured  this  happens  in  a  manner  that 
complies with privacy, data protection rules and other fundamental rights. 

10.2  Provide data literacy education to government agencies. Public servants  need to be  educated on 
what  data  to  prioritise,  how  it  should  be  handled  by  prioritising  quality,  how  to  process  it  in  a 
manner complying with relevant privacy and data protection rules, and how it can be used to boost 
competitiveness with data across industries in Europe 

10.3  Create  European  large  annotated  public  non-personal  databases  for  high  quality  AI  that  are 
reliable  and  trustworthy.  These  should  be  accessible  by 
legitimate  stakeholders,  including 
companies,  civil  society  organisations  and  research  institutes  in  Europe  to  develop  and  train  AI 
solutions. Such non-personal data sets can not only help fostering AI development, but can also offer 
a  remedy  against  false  news  by  helping  to  preserve  access  to  correct  information.  Foster  the 
development of standards for interoperability of public applications and public data reservoirs.30 

10.4  For procurement contracts on public services between a public sector organisation and a company, 
consider introducing a requirement that data produced in that context which does not infringe the 

26  

27  

28  
29  

30  

The  most  commonly  seen  single  point  of  contact  for  services  is  the  information  gateway  eesti.ee  of  the  Estonian 
government and can serve as a best practice example. 
In this regard, it should be explored whether the role of public libraries could be exploited, turning them into as civil centres 
providing Internet access and staff helping people to use public digital services. 
Following also the model of Article 41 of the Charter of Fundamental Rights of the European Union.  
This is particularly important given that 44% of European citizens between 16 and 74 years do not have basic digital skills. 
See 
at: 
Factsheet 
https://ec.europa.eu/commission/sites/beta-political/files/digital-skills-factsheet-tallinn_en.pdf. 
In this regard, the X-road protocol of the Estonian government can be considered as a best practice.  

of  October 

Commission 

accessible 

European 

Digital 

Skills, 

2017 

the 

on 

19 

 
 
                                                           
private company’s IP and which is of general public interest should be handed back to the public 
sector allowing its reuse for beneficial innovation or building better services in full compliance with 
fundamental rights, the rule of law and democracy. 

11.

  MAKE STRATEGIC USE OF PUBLIC PROCUREMENT TO FUND INNOVATION AND ENSURE TRUSTWORTHY AI 

11.1  Within public procurement processes, allocate substantive funding to innovation-driven, AI-based 
solutions.  This could function as  a public  sector  instrument to fund innovative solutions, including 
AI-based solutions, created by start-ups, SMEs, scale-ups and larger companies. 

11.2  Within procurement processes, ensure that potential risks of the use of AI by the government are 
identified, assessed and appropriately addressed. In order to safeguard individuals from risks, such 
as  for  instance  privacy  and  personal  data  infringements,  this  could  be  done  by  mandatorily 
consulting the relevant authorities (including data protection authorities) prior to the procurement 
of AI systems, particularly in those contexts where there can be a significant impact on individuals’ 
rights. 

11.3  Introduce clear eligibility and selection criteria that in the procurement rules and processes of EU 
institutions, agencies and Member States that require AI systems to be trustworthy (lawful, ethical 
and  robust),  ensuring  that  they  effectively  protect  people’s  personal  data,  privacy  and  autonomy. 
The  Ethics  Guidelines’  assessment  list  can  provide  a  helpful  means  to  operationalise  such 
requirement. 

12.

  SAFEGUARD FUNDAMENTAL RIGHTS IN AI-BASED PUBLIC SERVICES AND PROTECT SOCIETAL INFRASTRUCTURES  

Methods  should  be  created  to  validate  whether  the  government’s  decisions  that  rely  on  data-driven 
systems  were  biased against  individuals compared to other similar decisions, given that access to one’s 
own personal data is not enough to ensure the analysis of fair and just decisions that are in accordance 
with legal standards.   

12.1  Ensure the application of the Ethics Guidelines for Trustworthy AI  to AI systems deployed by the 

public sector.  

12.2  Make  available  to  any  individual  who  is  subject  to  an  AI-informed  governmental  decision  that 
produces legal effects or similarly significantly affects that individual, information on the logic of 
the algorithms and how data is used to inform such decisions, enabling the affected individual to 
understand,  evaluate  and  potentially  challenge  the  decision.  Individuals  should  also  receive  an 
explanation about how each specific decision about them was reached, regardless of whether or not 
it  was  based  on  his  or  her  personal  data.  Reference  can  also  be  made  to  the  existing  access  to 
documents and information obligations resting upon authorities at national and European level 

12.3  Fund  and  facilitate  the  development  of  AI  tools  that  can  assist  in  detecting  biases  and  undue 
prejudice  in  governmental  decision-making.  Those  tools  could  be  made  available  to  civil  society 
organisations,  NGOs,  individuals  and  other  interested  parties  that  are  questioning  the  integrity  of 
public governance or are searching for support and evidence to challenge certain policies.  

12.4  Ban AI-enabled mass scale scoring of individuals as defined in our Ethics Guidelines, and set very 
clear and strict rules for surveillance for national security purposes and other purposes claimed to be 
in the public or national interest in line with EU regulation and case law. Develop trustworthy ways 
to  do  this  where  legal,  necessary  and  proportionate,  and  ensure  that  this  is  not  used  in  ways  to 
suppress or undermine (political) opposition or democratic processes.   

20 

 
 
 
D.  Ensuring World-Class Research Capabilities  

Europe  must  aim  for  scientific  AI  leadership,  by  focusing  the  power  of  its  multi-
faceted  and  distributed  research  excellence  with  several  worldwide  renowned 
Centres of Excellence to establish and demonstrate its intellectual and commercial 
leadership in AI. Both purpose-driven and fundamental research in all aspects of AI 
must  be  secured  in  order  to  promote  AI  that  is  trustworthy  and  to  address  relevant  scientific,  ethical, 
sociocultural  and  industrial  challenges.  In  particular,  research  and  innovation  on  AI  that  address 
complementarity between AI systems and humans, that foster Trustworthy AI solutions and that address 
societal  challenges  should  be  promoted.  The  overarching  goal  is  to  ensure  world-class  research  that 
contributes  to  Europe’s  sustainability,  growth  and  competitiveness,  and  inclusion,  leading  to  individual 
and societal well-being. This necessitates a European research community that can unite through strong 
collaboration,  and that can join forces with  industry and society at  large  to build on  European research 
strengths and enhance Europe's well-being.  

13.

  DEVELOP AND MAINTAIN A EUROPEAN STRATEGIC RESEARCH ROADMAP FOR AI 

Based  on  its  strong  basic  and  applied  research  record,  Europe  needs  to  develop  an  ambitious  strategic 
research roadmap for AI, including an action plan to implement it.  

13.1  The  roadmap  should  include  major  open  disciplinary  and  interdisciplinary  fundamental  scientific 
questions  and  grand  challenges  of  global  relevance,  as  well  as  major  industrial  and  societal 
challenges where AI can be an enabler or a significant part of the solution. While basic research 
has a more general focus, the research needed to address industrial and societal challenges is mainly 
domain specific. Examples of important human-centric application domains especially beneficial to 
European society are the future of health, work, mobility and industry. 

13.2  The  roadmap  should  focus  on  areas  of  strategic  value  and  opportunities,  including  the 
embodiment of AI into cyber-physical-systems and robotics, designing AI algorithms for chips to be 
deployed in the Internet of Things (IoT), the integration of reasoning and learning (or hybrid AI) to 
achieve  learning  systems  that  facilitate  reasoning  and  explanation,  resource  aware  learning  even 
from small datasets, enabling digital twins and continuous learning, as well as techniques to enable 
Trustworthy AI. 

13.3  The roadmap should in particular foster research that can help ensuring AI solutions that meet the 
Trustworthy  AI  principles  and  requirements,  enabling  for  instance  requirements  such  as  human 
oversight,  privacy-by-design,  robustness,  non-discrimination  and  transparency  (including  the 
traceability  and  explainability  of  AI  systems).  The  roadmap  should  identify  potential  breakthrough 
findings  and  technologies  that  could  come  in  the  form  of  grand  challenges  of  AI.  Strategic 
breakthroughs  should  subsequently  be  translated  into  practice,  allowing  Europe  to  strengthen  its 
competitive capacities. 

13.4  The roadmap should be developed and regularly updated based on new developments in research 
and  technology  through  a  bottom-up  process  that  complements  the  top-down  policy-driven 
approach  of  governments  and  through  stakeholder  consultation.  This  roadmap  also  needs  to 
identify  impact  in  industry  and  society,  and  should  harmonise  and  complement  AI  research 
roadmaps developed by Member States and other organisations.  

13.5  While ensuring scientific independence, the roadmap should ensure for the needs and concerns of 

individuals through engagement with civil society representatives.  

21 

 
 
14.

INCREASE AND STREAMLINE FUNDING FOR FUNDAMENTAL AND PURPOSE-DRIVEN RESEARCH 

14.1  Provide dedicated, significant and long-term research funding for both fundamental and purpose-
driven  research  on  AI  to  maintain  competitiveness  of  European  research  and  companies,  and  to 
address relevant societal and industrial challenges. The funding should be aligned with the European 
AI research roadmap and should be independent of, and parallel to, any funding programme on ICT. 
Significant  investment  in  basic  funding  is  required  to  achieve  adequate  research  independence, 
which is necessary for fundamental excellent research in AI. 

14.2  Create  incentives  and  support  for  interdisciplinary  and  multi-stakeholder  research  for  example 
through large-scale challenge-driven research missions. While striving for technological leadership, 
ensure  the  involvement  of  social  scientists,  humanities  scholars,  as  well  as  other  interdisciplinary 
collaborations in European AI research projects. Increase permeability between disciplinary silos at 
universities and research institutions by easing admission to existing programmes and curricula from 
other disciplines and by establishing inter-disciplinary AI curricula and PhD programmes. Foster the 
involvement  of  civil  society  in  AI  research  programme  design  and  in  projects  (e.g.  through  citizen 
science) where appropriate. 

14.3  Simplify  and  streamline  the  structure  of  research  funding  instruments.  Reduce  the  overhead  in 
efforts  and  time  to  decisions  of  applying  for  grants,  for  example  through  early  reviews  from 
abstract-sized  submissions;  improve  the  quality and consistency  of  proposal  reviews,  and  increase 
the predictability of funding instruments and their timing to support long-term planning, using the 
European AI research roadmap. 

14.4  Establish  mixed  academic-industrial  transfer  labs,  mainly  funded  by  industry,  providing  specific 
research  for  the  companies  while  also  providing  funding  for  long-term  open  research  and 
collaboration  in  the  broader  research  area.  The  bridge  between  fundamental  research  and  the 
application  of  its  results  in  European  organisations  and  on  the  market  needs  to  be  shortened. 
Specifically  funded  transfer  projects  are  essential  to  achieve  this  objective.  European  AI  research 
centres located in industrial environments should also be strengthened to create sufficient traction 
for building and growing the European AI start-ups ecosystem that can help commercialising cutting-
edge research and technology. 

14.5  Initiatives funded by the Horizon Europe and Digital Europe programmes related to AI should build 
in  safeguards  and  conditions  to  ensure  that  public  funding  of  these  projects  benefits  the  public 
interest.  The  funding  should  in  particular  support  AI  solutions  that  are  in  line  with  the  Ethics 
Guidelines,  and  help  fostering  innovation  in  Trustworthy  AI  solutions.  Funding  of  public  research 
should be fully transparent, also taking into account indirect funding by interested parties through 
proxies.  Whilst  safeguarding  scientific  independence,  civil  society  should  be  engaged  in  the 
management of the programmes, both at agenda setting level and at implementing or monitoring 
level. Enhanced  trust  in  AI  systems  can  be  fostered  by  systematically  including  societal  needs  and 
concerns  related  to  AI  systems  in  the  design  and  implementation  phase  of  the  respective  EU 
research and funding programmes, such as Horizon Europe and Digital Europe, and the projects that 
will be funded thereunder. 

15.

  EXPAND AI RESEARCH CAPACITY IN EUROPE BY DEVELOPING, RETAINING AND ACQUIRING AI RESEARCHERS 

To facilitate world-class research, we need to create a stimulating and empowering research environment 
in  Europe  which  attracts  and  retains  researchers  in  AI.  Research  career  prospects  should  be  rich  in 
opportunity and competitive to retain and attract international AI talent. Young researchers, in particular, 

22 

 
 
 
have to feel that Europe is where exciting, future-proof, sustainable, ethically aligned, and trustworthy AI 
is developed, and where an inspiring and fulfilling public or private research career is possible. 

15.1  Acknowledge that a holistic strategy is needed to create the conditions for talents to find Europe 
attractive  as  a  research  environment,  such  as  the  availability  of  lively  and  vibrant  research 
environments,  stable  and  long-term  funding,  a  critical  mass  of  excellent  researchers,  competitive 
salaries,  world-class  infrastructure  with  unique  datasets,  competitive  computing  power  and 
dedicated engineering support. 

15.2  The  work  environment  should  offer  collaboration  between  researchers  and  start-ups,  larger 
companies and organisations, as well as society at large, encouraging researchers in academia to 
collaborate  with  industry  and  the  public  sector  (e.g.  through  consultancy  arrangements  or 
secondments), including the possibility of split-time positions. 

15.3  Create an open innovation culture through a lively, interdisciplinary and multidisciplinary research 
environment  around  world-class  research  centres.  Enable  and  foster  the  creation  of  research-
focused start-ups that develop translate and commercialise new AI products in Europe or can inject 
AI  technology  into  existing  products  and  services.  Collaboration  between  research  institutes  and 
such start-ups should be enabled. 

16.

  BUILD A WORLD-CLASS EUROPEAN RESEARCH CAPACITY 

The European AI research community is based on distributed AI smaller research centres and laboratories 
widely  spread  across  Europe,  each  with  respective  strengths  ranging  from  specialised  research  and 
innovation domains up to large-scale and broad international regions. They engage in cutting-edge basic 
and  applied  research,  connecting  with  local  and  international  industrial  and  start-up  partners  and  in 
particular, they attract young talents to the field of AI very early on. Europe is already home to a number 
of  large  centres  of  critical  mass  and  high  reputation  (also  embedded  in  international  industrial 
ecosystems), which we refer to as Centres of Excellence (CoEs). 

The  full  strength  and  diversity  of  Europe’s  AI  community  must  be  working  together  to  address  major 
grand challenges as well as scientific and technical breakthroughs. This requires a series of major capacity 
building  initiatives.  Such  initiatives  need  to  involve  multi-  and  interdisciplinary  expertise,  connect  and 
further  develop  existing  Centres  of  Excellence,  strengthen  smaller  research  centres  and  laboratories  to 
become  Centres  of  Excellence,  and  provide  specialised  infrastructure  for  AI.  This  capacity  should  be 
balanced between distributed, local research and large-scale pan-European initiatives. 

16.1  Strengthen  and  create  additional  Centres  of  Excellence  (CoEs)  that  address  strategic  research 
topics  and  become  a  European  level  multiplier  for  a  specific  AI  topic.  These  CoEs  should  receive 
specific  funding  in  order  to  create  a  significant  international  impact  in  their  areas  of  research, 
technology transfer and innovation. Moreover, while being interdisciplinary, these CoEs should have 
their own specific research focus. 

16.2  Nurture  and  support  Europe’s  AI  research  centres  and  laboratories  through  research  funding  to 
scale  up  to  worldwide  competition  and  help  them  strive  to  become  Centres  of  Excellence.  Such 
support includes specific fundamental and purpose-driven funding programmes, research professor 
funding,  access  to  mechanisms  to  grow  and  the  possibility  to  attract  researchers  of  world-class 
quality and reputation. 

16.3  EU institutions together with Member States should also create and fund networks consisting of AI 
Centres  of  Excellence,  as  well  as  smaller  AI  research  centres  and  laboratories.  These  networks 
should form thematic focal points for AI research, attracting the top talents in their respective focus 

23 

 
 
areas with exciting research challenges and top infrastructure and support. Networks of centres with 
critical mass have a high potential to not only produce significant research results but to also create 
impact well beyond academic research. 

16.4  Develop close collaborations with industry, the innovation ecosystem (e.g. DIH for AI and others) 
and  other  stakeholders  that  can  provide  feedback,  pick  up  new  results  early  on,  and  provide 
important  additional  funding.  This  collaboration  must  be  based  on  an  appropriate  model  for 
intellectual property rights and sharing of data and ideas. 

16.5  Create a dedicated Public-Private Partnership (PPP) in AI. It is critically important that this PPP does 
not compromise investment in local or Member States' initiatives in AI. The private and the public 
sector should be equal partners. 

16.6  Develop  a  cross-cutting  network  focused  on  Trustworthy  AI  across  European  universities  and 
research  institutions.  With  the  Ethics  Guidelines  published  on  8  April  2019,  Europe  has  taken  a 
strong initiative to lead the global debate on the applied ethics of AI. Consideration should be given 
to  support  the  development  of  a  Centre  of  Excellence  in  Trustworthy  AI  to  maintain  Europe's 
intellectual leadership. The  political institutions and the AI industry urgently  need such word-class 
experts for newly emerging technology, legal, ethical and social issues, focusing in particular on the 
framework  for  Trustworthy  AI  put  forward  in  the  Ethics  Guidelines.  A  cross-cutting  network  will 
provide a basis for research, teaching and disseminating AI ethics and other disciplines necessary to 
ensure  Trustworthy  AI  across  the  whole  European  system.31  It  will  support  the  coordination  of 
activities  relating  to  AI  ethics  and  other  relevant  disciplines,  which  involve  offering  continuously 
updated curricula on these disciplines at all European universities. It also involves dissemination of 
research results to the wider public or organising public debates for civil society. To cover the multi- 
and interdisciplinary nature of AI research, the network should not become "siloed" but involve and 
integrate the technical, practical, philosophical, legal, economic, social and humanistic aspects of AI 
in various proportions depending on their specific research focus.  

31  

The European University Association (which represents more than 800 universities and national rectors’ conferences in 48 
European countries) estimates that 720 professorial chairs in applied ethics will be needed for the network. 

24 

 
 
 
 
                                                           
CHAPTER II 

LEVERAGING EUROPE’S ENABLERS FOR 
TRUSTWORTHY AI 

25 

 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
CHAPTER II. LEVERAGING EUROPE'S ENABLERS FOR TRUSTWORTHY AI 

To achieve the impacts defined for each of the areas highlighted in Chapter I, the right foundation needs 
to be in place. Our foundational layer is described in terms of four enablers: (E) Data and Infrastructure, 
(F) Education and Skills, (G) a Governance and Regulatory framework and (H) Funding and Investment. 

(E) Fostering the development and uptake of Trustworthy AI requires the availability of data and a sound 
infrastructure.  This  includes  intense-computing  infrastructure  to  enable  the  scale-out  of  Europe’s  AI 
ambition, both concerning digital AI segments and physical segments embedded with digital components. 
Edge  computing  provides  a  particularly  interesting  platform  for  scaling  AI  in  a communications-efficient 
manner,  which  has  many  privacy-preserving  advantages.  Moreover,  given  that  data  is  an  indispensable 
raw material for developing AI, a safe, secure and high-quality infrastructure is needed that allows for the 
use and exchange of industrial and public data in a lawful, ethical and robust manner. Data donor schemes 
could  be  put  in  place  to  foster  socially  beneficial  AI  systems  built  on  personal  data,  with  the  highest 
attention  to  privacy  and  data  protection  rules.  Besides  ensuring  the  necessary  physical  infrastructures, 
Europe  should  build  a  cloud  infrastructure  that  can  support  the  development  and  deployment  of 
Trustworthy AI, and appropriate cybersecurity policies and expertise. 

(F)  Europe  must  invest  in  the  development  of  human  capital  for  artificial  intelligence,  fostering  the 
necessary skills and education in an AI-enabled world. We must educate and develop the best scientific 
minds in AI, which requires a significant increase in the uptake of science and technology subjects at every 
level of the education system, and a substantial redesign of education systems from pre-school to higher 
education. Conscious and well-informed children and other individuals will create a solid foundation for 
responsible and positive uses of AI systems and digital technologies more generally, and strengthen their 
personal skills on cognitive, social and cultural levels. A doubling-down of efforts is needed to increase the 
number  and  proportion  of  women  in  science  and  technology.  This  will  not  only  increase  the  available 
talent pool, but also foster the relevance and quality of research and innovation of AI systems for society 
as  a  whole.  Finally,  initiatives  must  be  supported  for  upskilling  and  reskilling  significant  parts  of  the 
current workforce to provide them with the appropriate knowledge, skills, and attitudes in a transforming 
employment environment. 

(G) Europe takes pride in its sound regulatory environment that enables and stimulates competition and 
innovation while safeguarding fundamental rights and protection from unacceptable risk or harm. Yet, the 
new challenges raised by AI require reflection on an appropriate governance framework and a review of 
the  adequacy  of  the  current  regulatory  regime,  pursuant  to  a  comprehensive  mapping  of  relevant  EU 
regulations  and  potential  legal  gaps  to  both  maximise  AI’s  benefits  and  prevent  and  minimise  its  risks. 
Such  a  review  should  generally  be  based  on  a  risk-based  approach  to  AI  policy-making,  and  take  into 
account  both  individual  and  societal  risks.  For  unacceptable  risks,  the  revision  of  existing  rules  or  the 
introduction of new regulation should be considered. Finally, to achieve the impacts put forward in these 
recommendations, it is crucial to deliver on a European Single Market for AI. 

(H)  European  Institutions and  Member  States,  together  with  the  private  sector,  must  create  a  lucrative 
funding and investment climate for Trustworthy AI in Europe.  Europe currently underperforms in terms 
of early stage innovation funding, as well as in investments in digital technologies and AI more generally. A 
concerted and integrated effort is needed to ensure that Europe can deliver on the  benefits that AI can 
bring to individuals and society, requiring a full range  of public funding mechanisms to be in place, and 
openness towards global competition. Besides foreseeing funding for the recommendations set out in this 
report,  Europe  must  build  an  environment  of  trust  whereby  the  private  sector,  jointly  with  all 
stakeholders, is motivated to invest in AI solutions and build the future that we want to create.  

26 

 
 
 
E.  Building Data and Infrastructure for AI  

Europe  needs  infrastructures  capable  of  delivering  secure,  privacy-preserving  data 
collection and storage, intense analytics, next-generation networks and software to 
support  the  development  and  delivery  of  AI  systems  at  scale.  This  requires 
investment in infrastructure, data, software and networks. 

17.

  SUPPORT AI INFRASTRUCTURES ACROSS MEMBER STATES 

17.1  Invest in the necessary intense-computing AI architectures capable to scale-out the ambition for AI 
in  Europe,  including  distributed  clusters  (cloud)  and  edge  computing,  large  RAM  and  fast 
networks.  Such  large  infrastructures  must  be  supported  in  DIH  networks,  interconnected  through 
high-speed  networks.  A  number  of  strategically-focused  infrastructures  for  simulation  of  physical 
models  (digital  twins)  may  be  needed  for  some  applications  (manufacturing,  automotive,  etc.) 
where  little  data  is  available  or  too  costly  to  produce  (e.g.  High-Performance  Computing).32 
Engineering  skills  needed  to  develop  and  maintain  such  infrastructures  must  be  developed  and 
supported. 

17.2  A  network  of  testing  facilities  and  sandboxes,  with  high-speed  networks  interconnecting  them 
must be organised with appropriate governance mechanisms to set legal and ethical standards. AI 
Centres  of  Excellence  (CoEs)  can enable AI applications to be  deployed and tested in “sandboxes” 
within well-defined conditions (see also Recommendation 29.2). 

17.3  Invest  in  fundamental  and  applied  research,  innovation  and  industrial  capacity  building  within 
edge-based  intelligence,  empowered  by  the  interplay  of  hardware,  software and  AI  capabilities. 
The  increasing  demand  for  connecting  “things”  and  physical  infrastructures,  powered  by  next 
generation  communication  networks  (5G)  and  edge-based  compute  infrastructure,  open  up  high 
market potential for real-time, power efficient and privacy preserving AI where Europe can seize the 
opportunity as early adopter and position itself as the global leader for edge intelligence, particularly 
serving B2B and P2C markets. To leverage that, a favourable policy and investment environment for 
5G roll-outs,  large  investment  volumes in software  toolchains for edge  hardware design, compute 
infrastructure  and  distributed  AI  (also  beyond  Machine  Learning)  are  needed  for  creating  new 
unicorns  in  Europe,  transforming  large  industries  to  digital  locomotives  and  securing  cutting-edge 
research and competence at a global scale. More prospective infrastructures will be needed in the 
longer  term,  to  extend  the  capabilities  of  existing  infrastructures,  such  as  optical  or  quantum 
computing.  

17.4  Liaise  with  EU  actions  and  funding  programmes  based  on  the  Electronic  Leaders  Group 
recommendations  regarding  dedicated  AI  co-processors  (edge  computing),  jointly  with  digital 
microcontrollers initiatives, as well as optical and quantum computing. Foster the development of 
edge  computing  hardware  with  the  European  semiconductor  industry.  Support  the  dissemination, 
free of charge, of AI and IoT hardware and associated software tools towards European universities, 
engineering  schools  and  the  different  start-ups  and  SMEs  ecosystems.  Edge  computing  is  not  an 
alternative to cloud-based AI, but an additional route for AI where Europe can have a competitive 
advantage  with  significant  business  impact  across  Europe  concerning  the  proximity  across  Europe 
with different stakeholders. 

32  

It is important to keep in mind that the models of HPC and AI applications often are different: HPC is mainly batch-based 
with very long run-times while AI applications often require interactive sessions with very many but relatively short runs.  

27 

 
 
                                                           
18.

  DEVELOP LEGALLY COMPLIANT AND ETHICAL DATA MANAGEMENT AND SHARING INITIATIVES IN EUROPE 

Data is an indispensable raw material for developing AI. With data volumes growing at 61% per annum33, 
four times more data will be created until 2025 than that which exists today. Ensuring that individuals and 
societies,  industry,  the  public  sector  as  well  as  research  and  academia  in  Europe  can  benefit  from  this 
strategic  resource  is  critical,  as  the  overwhelming  majority  of  recent  advances  in  AI  stem  from  deep 
learning  on  big  data.  A  safe,  secure  and  high-quality  data  infrastructure would  enable  Europe  to  better 
develop  and  train  AI  systems,  which  in  turn  can  be  steered  towards  applications  that  can  facilitate  the 
Sustainable Development Goals. A clear distinction must however be ensured between personal and non-
personal  data.34  A  fundamental  rights-based  personal  data  infrastructure  as  put  forward  in  the  GDPR 
should  be  fostered  and  its  enforcement  should  be  ensured.  At  the  same  time,  the  sharing  of  industrial 
data still poses a significant challenge, creating obstacles to collaboration between organisations and, in 
some cases, stifling innovation. Therefore, industrial and research solutions must be found for processing 
and  sharing  data  in  a  secure  and  privacy-respecting  way,  allowing  everyone  to  reap  its  benefits  to  the 
fullest.  

18.1  Set  up  national  and  European  data  platforms  for  AI  that  include  all  necessary  tools  for  data 
governance,  annotation,  and  storage,  next-generation  networks,  analytics  software  and,  most 
importantly, datasets35 through a structural and investment fund. European public data is currently 
found  in  silos  across  institutions  and  Member  States,  and  should  be  gathered  through  secure 
platforms that comply with data protection rules. A structural and investment fund for a European 
data  infrastructure  should  be  considered,  and  appropriate  governance  mechanisms  should  be 
established.  The  Commission  should  support  the  coordination  of  data  sharing  and  access  through 
ecosystems,  and  thus  leverage  data  as  a  competitiveness  factor  for  European  innovative  and 
sustainable  companies.  Areas  of  particular  focus  should  be:  opening  public  funded  data  sources, 
redirecting funding flows to foster  collaboration between public and private  sector and policies to 
address regulatory barriers. 

18.2  Consider  European  data-sharing  infrastructures  as  public  utility  infrastructures.  Data-sharing 
infrastructures should be seen as the basic building blocks of a society supported by AI technologies, 
creating trust and paving the way for companies to develop shared, trustworthy and interoperable 
data sharing platforms, with due consideration for privacy-, inclusion- and accessibility- by design.  

18.3  Support  an  EU-wide  data  repository  through  common  annotation  and  standardisation.  This 
requires investment in the development of methods and tools that render datasets comparable and 
useable  across  EU  borders.  The  development  of  domain-specific  data  representation  should  be 
considered,  as  well  as  standards  specific  to  particular  domains.  Standardised  data  and  machine 
learning parameter schemes can help ensure a common data format that allows combining diverse 
datasets in the public domain. An EU system for annotating datasets and standardisation should be 
specifically  supported  in  high  impact  domains  with  particular  domain-specific  content  (such  as 
employment, manufacturing or health).  

18.4  Support research on and development of industrial solutions for fast, secure and legally compliant 
industry  data  sharing  (e.g.  encryption)  and  stimulate  sharing  of  industry  data.  Avoid  data 

33  

34  
35  

for 

instance:  https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-

See 
2025.html.  
As for instance initiated in Regulation (EU) 2018/1807 on the free flow of non-personal data, applicable since 28 May 2019. 
This should build on the Directive on the re-use of public  sector  information, also known as the ‘PSI Directive’ (Directive 
2003/98/EC, which is currently under revision. 

28 

 
 
                                                           
monopolies  by supporting companies that contribute to a sustainable ecosystem.  Incentives could 
be  provided  for  creating  consortia  around  verticals  to  share  data  in  a  legally  compliant  manner, 
contributing  to  European  infrastructures  or  datasets,  or  doing  research  for  improving  such 
infrastructures.  Data-sharing  mechanisms  should  be  ethical  and  in  compliance  with  EU  data 
protection law. 

18.5  Foster  the  creation  of  trusted  data  spaces36  for  specific  sectors  (e.g.  healthcare,  automotive  and 
agri-food) where actors in a given value chain accept to share data, yet the rights related to the data 
rest with end users and/or the stages of the value chain where most of the value is being created. 
Industrial policy can help specify sector specific openness and non-discrimination requirements for 
access  to  trusted  data  spaces,  and  clarify  the  conditions  under  which  inter-firm  data-sharing 
cooperation  should  respect  competition  rules,  particularly  in  terms  of  membership  requirements, 
type of data shared, and access conditions for third parties. It should also determine how intellectual 
property frameworks impact the allocation of property rights within trusted data spaces. 

18.6  Develop  mechanisms  for  the  protection  of  personal  data,  and  individuals  to  control  and  be 
empowered by their data, thereby addressing some aspects of the requirements of trustworthy AI. 
Tools  should  be  developed  to  provide  a  technological  implementation  of  the  GDPR  and  develop 
privacy preserving/privacy by design technical methods to explain criteria, causality in personal data 
processing of AI systems (such as federated machine learning). Support technological development 
of anonymisation and encryption techniques and develop standards for secure data exchange based 
on  personal  data  control.  Promote  the  education  of  the  general  public  in  personal  data 
management,  including  individuals'  awareness  of  and  empowerment  in  AI  personal  data-based 
decision-making processes. Create technology solutions to provide individuals with information and 
control over  how their data is being used,  for example  for research, on consent  management and 
transparency across European borders, as well as any improvements and outcomes that have come 
from this, and develop standards for secure data exchange based on personal data control.  

18.7  Create a data donor scheme, allowing individuals to donate data for specific purposes, surrounded 

by clear governance and transparency, and safeguards for privacy protection. 

18.8  Consider the introduction of a data access regime on FRAND terms,  namely fair, reasonable,  and 
non-discriminatory. In addition, data interoperability amongst market players should be incentivised 
and  required,  where  appropriate.  In  specific  market  circumstances,  data  accumulation  by  one  or 
more firms can raise barriers to entry. In such contexts, data portability rights may not be enough, 
and more far-reaching interoperability requirements can help promote competition and innovation 
by  new  entrants,  while  however  respecting  legal  obligations  and  rights.  In  contemporary 
competition law,  interoperability requirements  can only  be  applied on dominant  firms upon proof 
that  data  access  is  indispensable  to  compete.  Yet  there  may  be  circumstances  in  which  sector 
specific  regulatory  frameworks  should  go  further,  by  laying  down  horizontal  interoperability 

36  

See  also  Data  Trusts:  A  New  Tool  for  Data  Governance,  by  Nesta  and  Element  AI  (2019),  available  at: 
https://hello.elementai.com/rs/024-OAQ-547/images/Data_Trusts_EN_201914.pdf.  Recently,  the  Canadian  government 
recognized data trusts, a third party stewardship model, as an “emerging solution” that could provide a secure and privacy-
enhancing means of sharing data in order to spur the development of AI innovations in a broad spectrum of the economy. 
Canada considers that data trusts could allow for greater sharing and use of data for socio-economically beneficial purposes 
within  a  framework  that  protects  against  abuses  of  that  data.  It  considers  that  data  trusts  could  benefit  research 
communities in areas such as health, clean technology or agri-business, among others. The United Kingdom is itself engaged 
in  data  trust  pilot  projects.  Europe  should  explore  its  own  initiatives  in  this  area  to  facilitate  data  sharing  for  research 
purposes and foster innovation. 

29 

 
 
                                                           
obligation  on  both  dominant  and  non-dominant  firms  to  facilitate  the  emergence  of  competitive 
market structures. 

19.

  SUPPORT EUROPEAN LEADERSHIP IN THE DEVELOPMENT OF AN AI INFRASTRUCTURE 

Digital  dependency  on  non-European  providers  and  the  lack  of  a  well-performing  cloud  infrastructure 
respecting European norms and values may bear risks regarding macroeconomic, economic and security 
policy considerations, putting datasets and IP at risk, stifling innovation and commercial development of 
hardware and compute infrastructure for connected devices (IoT) in Europe. Without focusing on Europe 
exclusively, we believe it is important to foster the development of such infrastructure also in Europe. 

19.1  Support  the  development  of  open  source  AI  software  libraries  including  the  Trustworthy  AI 
guidelines  and  in  line  with  the  latest  achievements  in  research.  By  properly  supporting  the 
development  of  such  AI  libraries,  building  on  strong  European  competence,  companies  and 
researchers  will  be  able  to  use  up-to-date  software,  offered  by  software  providers  operating  in 
Europe, providing support and training, which will also help increasing competitiveness of European 
companies  in  this  field.  Support  dedicated  training  on  such  AI  software  through  the  AI  Digital 
Innovation Hub (DIH) network. 

19.2  Support  mechanisms  for  cutting-edge  research,  innovation  and  commercial  development  of 
hardware  and  compute  infrastructure  for  connected  devices  and  the  Internet  of  Things  (IoT)  in 
Europe.  A  dedicated  budget  line  in  the  Digital  Europe  programme  for  this  purpose  could  be 
considered.  Consideration  should  be  given  to  developing  a  European  champion  that  can  provide 
cloud services with the option to maintain its datasets within Europe. We envisage cases whereby, 
for  certain  types  of  data,  an  option  to  guarantee  that  the  data  is  stored  in  Europe  might  be 
appropriate. 

20.

  DEVELOP AND SUPPORT AI-SPECIFIC CYBERSECURITY INFRASTRUCTURES 

The  AI  specific  cybersecurity  policy  should  be  formulated  to  be  user  centric,  systemic,  and  anchored  in 
open  and  pluralistic  process.  User  centric  cybersecurity  policies  must  protect  users’  rights  online,  by 
design and by default. New attacks appear which directly target the AI module in AI-based applications, 
while AI can help fighting cyberattacks. 

20.1  Develop an infrastructure for cybersecurity, building on the Cybersecurity Act adopted by the EU in 
spring 2019 to protect our networks, data, and users from risks. Holistic digital security approaches 
should  address  the  risk  of  malware  and  vulnerabilities,  social  engineering,  restrictions  on  the 
functionality of network, efforts to weaken the integrity of communications systems, and threats to 
anonymity, privacy, and other human rights exercised online. Develop an ecosystem for producing 
AI  techniques  allowing  Member  States  to  deploy  their  defence  against  cyberattacks.  We 
acknowledge the important role that ENISA has to play in this context. 

20.2  Develop  user-centric  AI  cybersecurity  policies  to  protect  users’  rights  online.  AI-specific 
cybersecurity policy should be  formulated to be  user-centric, systemic, and anchored in open and 
pluralistic  process.  Cybersecurity  policies  that  focus  excessively  on  state  operations  undermine 
international human rights obligations, threaten the peaceful use of the internet, and inhibit the free 
flow of and access to information. Fora where cybersecurity policies are formulated should involve 
effective  participation  of  all  stakeholders,  granting  equitable  access  to  documents  and  decision-
makers as well as designated opportunities for input and reporting. 

30 

 
 
20.3  Adopt  holistic  digital  security  approaches  to  address  the  risk  of  malware  and  vulnerabilities, 
restrictions on the functionality of network and other efforts to weaken the integrity of our societal 
infrastructures.37 This could for instance be done by encouraging EU hackathons and competitions 
that can help analysing the penetrability of IoT devices against cyberattacks. Build inherently robust 
systems  and  respective  processes  that  on  top  of  cyber  hygiene  requirements  also  implement 
methods and techniques prepared for “unknown unknowns” by monitoring systems behaviour and 
applying advanced analytics for better situational awareness and response. Close vulnerabilities such 
as  “paths  of  least  resistance”  by  focusing  on  the  overall  system  and  on  how  the  system  level 
security-by-design is implemented. Apply red-teaming, ensuring that the attacks are designed to be 
representative  of  what  an  actual  attacker  would  do,  considering  the  domain  specific  and  generic 
features.  In  some  cases,  a  responsible  disclosure  of  weaknesses  should  be  used  to  pressure 
operators to abandon defective practices (e.g. vulnerabilities that expose computer systems). 

20.4  Invest in AI-enabled cyber protection systems. The time for implementing such systems in practice 
should  be  shortened,  allowing  for  efficient  monitoring  and  triage  of  events,  incidents  and  more 
generally the behaviour of various control systems. AI techniques can be used to filter false-positives 
and  the  disruption  of  services,  and  provide  predictive  measures.  The  notion  of  zero-day  type  of 
attacks  should  be  extended  from  the  cybersecurity  field  to  the  general  field  of  AI-based  systems, 
with  the  specifics  of  intrinsic  new  types  of  vulnerabilities  and  potential  targeted  areas,  such  as 
training datasets, knowledge bases, algorithms and reasoning mechanisms. Consider repositories for 
sharing trusted info about vulnerabilities and exploits in AI systems, similar to those widely used and 
supported in the cybersecurity field.  

20.5  Beyond  safeguarding  public  infrastructures,  ensure  that  the  entire  data  transmission  system  is 
more  secure.  Since  an  increasing  amount  of  data  is  transferred  within  IoT  networks,  security 
concerns  on  transmitting  sensitive  business  data  across  these  networks  are  rising.  Traditional  IoT 
settings, where data is stored and analysed in a centralised cloud or data centre, are also vulnerable 
to  well-coordinated  cyberattacks  and  power  outages.  This  requires  a  holistic  approach  towards 
electricity and electronic networks that can be spurred by governments. 

F.  Generating appropriate Skills and Education for AI   

The  development  of  AI  systems  in  Europe  requires  a  massive  input  of  human 
intelligence. Europe needs more talents to lead ambitious fundamental word-class 
research  programmes  and  retain  them  with  attractive  research  and  working 
conditions.  Therefore,  the  attraction  of  talent  into  the  fields  of  science  and 
technology  is  of  paramount  importance  to  ensure  Europe’s  innovativeness  in  the 
long  run.  Furthermore,  in  the  future,  AI  systems  will  augment  and  complement  human  capabilities  in 
hybrid  work  settings.  Practitioners  in  all  areas  will  need  to  be  upskilled  with  digital  competences  to  be 
enhanced in their roles. At the  same  time, the future workforce will have to be equipped with a new  – 
human  centric  –  set  of  skills  that  empowers  them  on  a  cognitive  and  a  socio-cultural  level  face  the 
challenges ahead. 

37  

Two examples can be put forward in this regard. A first example concerns NotPetya, whereby an estimated $10 Billion of 
damages  occurred  through  a  combination  of  “EternaBlue”  (a  leaked  US  NSA  penetration  tool)  and  Mimikatz  (a  proof  of 
concept to demonstrate security fault by a French security researcher deployed from Russia to Ukraine that spread over the 
world  and  even  back  to  Russia).  A  second  example  concerns  the  life  threatening  attack  against  Schneider’s  Triconex 
controllers in a petrochemical plant in Saudi Arabia. The attack was diverted, but it was found that instead of just shutting 
down the plant or destroying data, it was aimed to trigger an explosion that would have cost lives. 

31 

 
 
 
                                                           
This  will  require  also  a  deep-reaching  change  in  education  systems.  Although  skills  and  education  are 
domains that fall to a large extent under Member States national and often even regional competences, 
the  European  Commission  should  participate  in  this  effort  to  trigger  these  fundamental  changes  in 
national educational systems and support activities to share experiences and best practices and to seize 
common opportunities. A wide-ranging and long-term action plan should be carried out, involving actors 
at  all  levels.  This  calls  for the  adoption of  a  multidisciplinary  approach  that mobilises  various  education 
and training stakeholders including education institutions at all levels, social partners, training providers, 
community  colleges  and  companies,  which  will  also  have  to  shoulder  parts  of  the  AI  upskilling  and 
reskilling requirements. 

21.

  REDESIGN EDUCATION SYSTEMS FROM PRE-SCHOOL TO HIGHER EDUCATION 

As AI is deployed more extensively and more intensely, there will be a strong need to educate everyone, 
starting  from  an  early  age,  in  digital  literacy,  coding  and  STEM.  Digital  literacy  is  already  being 
implemented in formal education in various forms as well as in more informal awareness initiatives across 
Member States and in pan-European contexts. These needs to be further supported and developed when 
it  comes  to  AI  development.  Importantly,  complementary  cognitive,  socio-cultural  and  interdisciplinary 
skills  will  empower  individuals  and  enable  the  mastery  of  sectoral  knowledge  in  combination  with  new 
technological  solutions.  Thus,  education  systems  have  to  prepare  people  not  only  with  digital  skills  but 
also  by  strengthening  people’s  human-centric  skills.  Here,  educators  should  receive  specific  attention, 
since  it  is  in  their  hands  to  prepare  for  the  upcoming  challenges.  The  DigCompEdu38  framework  of  the 
European Commission already provides here a valuable reference frame. 

21.1  Set  up  recommendations  and  incentives  to  adapt  national  education  systems  to  strengthen 
children  in  human-centric  key  skills:  (i)  cognitive  competences  like  problem  solving,  process  and 
quality  monitoring,  critical  thinking,  judgement  and  creativity,  etc.  (ii)  socio-cultural  competences 
like  empathising, 
innovation 
competences.  

leading,  persuading,  envisioning,  etc.  (iii)  entrepreneurial  and 

21.2  Besides fostering the development of skills in a holistic manner throughout different disciplines in 
the  education  system,  embed  mandatory  ICT  and  digital  literacy  classes  (i.e.  responsible  and 
positive use of AI systems and digital technologies, including awareness for better-internet-for-kids) 
in  formal  primary  and  secondary  education.  Emphasise  gender-sensitive  real-life  applications  for 
digital skills, incorporating project-based learning and hands-on experience. 

21.3  Direct  the  DigCompEdu  Framework  towards  educators  at  all  levels  of  education.  This  should 
happen  from  early  childhood  to  higher  and  adult  education,  including  general  and  vocational 
education  and  training,  special  needs  education,  and  non-formal  learning  contexts  and  ensure 
accessibility to learning resources and activities, and for all learners (based on universal design and 
accessibility standards).  

21.4  Recommend  Member  States  to  require  gender  competences  for  STEM  educators  in  the  hiring 
process  in  formal  education  systems.  More  generally,  set  incentives  to  offer  gender  sensitivity 
trainings for STEM educators. 

38  

The  European  Framework  for  the  Digital  Competence  of  Educators  (DigCompEdu)  is  a  scientifically  sound  framework 
describing  what  it  means  for  educators  to  be  digitally  competent.  It  provides  a  general  reference  frame  to  support  the 
development of educator-specific digital competences in Europe. DigCompEdu is directed towards educators at all levels of 
education,  from  early  childhood  to  higher  and  adult  education,  including  general  and  vocational  education  and  training, 
special 
see: 
https://ec.europa.eu/jrc/en/digcompedu. 

For  more 

information, 

non-formal 

education, 

contexts. 

learning 

needs 

and 

32 

 
 
                                                           
21.5  Support dedicated initiatives that encourage young people to choose AI subjects and related fields 
as a career. Provide sustained substantial incentives and funds for initiatives that focus on closing 
the “self-efficacy gender gap” in primary and secondary education systems. Target a proportion of 
50 % of female talents enrolling subjects like natural science, STEM and ICT in secondary and higher 
education by 2030. 

21.6  Support the implementation of educational AI technologies in primary and secondary education to 
facilitate  individual  learning  requirements  (e.g.  cognitive,  AI-based  tutoring)  39.  The  use  of  AI 
systems  in  education  should  strictly  comply  with  fundamental  rights,  with  due  account  for  the 
vulnerable position of children.  

21.7  Develop  and  offer  career  guidance  services  (e.g.  recommender  systems)  to  support  people  in 

finding and choosing education offers based on their individual skills (gaps). 

22.

  DEVELOP AND RETAIN TALENT IN EUROPEAN HIGHER EDUCATION SYSTEMS 

A lack of AI-related skills (including technical AI skills, skills for dealing with AI and managerial capabilities 
to extend AI in business) has been identified as the most important barrier to AI adoption in Europe.40 AI 
talents  with  expert  knowledge  are  required  who  are  capable  of  driving,  managing  and  conducting  AI 
activities  in  their  institutions  and  organisations.  The  creation  of  an  excellent  innovation  environment 
necessitates not only on funding. Europe also needs to attract, develop and retain a comprehensive talent 
pool of AI-developers, entrepreneurs and data analysts, and to create a beacon for talent. This will require 
dedicated  efforts  in  establishing  cross-disciplinary  AI  curricula  in  tertiary  and  post-graduate  education 
which  integrate  ethics,  humanities  and  technical  disciplines,  in  mainstreaming  AI-related  skills  in  all 
academic disciplines and professional fields, as well as in fostering cooperation spaces between AI experts 
and professionals. 

22.1  Develop and implement a European Curriculum in AI in collaboration with the European Excellence 
Centre of Trustworthy AI as proposed under Recommendation 16, to incorporate applied ethics of AI 
and  to  ensure  an  interdisciplinary  and  multidisciplinary  perspective  through  cooperation  across 
technical, humanist, social sciences, legal and philosophical approaches to AI. 

22.2  Implement  interdisciplinary  PhD  schools  and  Post-PhD  programmes  in  AI  (including  humanities, 
social sciences and ethics) in member state universities which closely collaborate with European AI 
research  hubs.  Provide  financial  scholarships  for  students  to  participate  in  such  dedicated  PhD 
programmes  and  post-PhD  programmes  and  include  these  programmes  in  calls  for  AI  Excellence 
Centres.  To  assure  a  certain  quality  across  Europe,  those  training  courses  and  studies  could  be 
certified.  

22.3  Increase  disciplinary  mobility  between  AI  associated  curricula.  This  can  be  done  through 
streamlined  admission  policies  of  universities  to  allow  for  permeability  between  disciplinary  silos 
and enable hybrid qualification of talent and interdisciplinary research and development. Implement 
credit accumulation models to receive AI-related degrees. 

39  

40  

See  Ritter,  S.,  Anderson,  J.  R.,  Koedinger,  K.  R.,  &  Corbett,  A.  (2007).  Cognitive  Tutor:  Applied  research  in  mathematics 
education,  in  Psychonomic  Bulletin  &  Review,  14,  Paaßen,  B.,  Gallicchio,  C.,  Micheli,  A.,  &  Hammer,  B.  (2018),  Tree  Edit 
Distance Learning via Adaptive Symbol Embeddings; and Stamper, J. C., & Koedinger, K. R. (2011). Human-machine student 
model discovery and improvement using DataShop. In International Conference on Artificial Intelligence in Education (pp. 
353-360). Springer, Berlin, Heidelberg. 
See Notes from the AI Frontier: Tackling Europe’s Gap in Digital and AI, McKinsey Global Institute, 2019. 

33 

 
 
                                                           
22.4  Mainstream and include skills related to data and AI in all academic disciplines and professional 
fields to increase the potential of areas where AI applications can be developed. This includes the 
development  of  a  state-of-the-art  MOOC  (Massive  Open  Online  Courses)  for  AI,  including 
elementary training on the basics as well as on relevant special topics, coupled with decentralised 
(regional)  face-to-face  training  activities  at  universities.  The  Commission  should  consider  the 
creation of an EU-wide MOOC on AI, accessible to all European citizens in all EU languages. All such 
programmes should ensure the concept of accessibility and universal design to include all sectors of 
society and to leave no one behind.  

22.5  Foster  cooperation  spaces  between  AI  researchers  and  professionals.  Encourage  universities  to 
increase  their  activities  in  AI  training  for  professionals  and  the  creation  networks  of  universities, 
vocational  schools  and  companies  to  build  living  lab  infrastructures  (e.g.  pilot  and  IoT  training 
factories),  in  which  universities,  vocational  schools  and  companies  develop  a  common  training 
concept. 

22.6  Support the  implementation  of postgraduate  and  supplement  curricula on  entrepreneurship and 
innovation  with  a  focus  on  digital  economy  and  data-driven  innovation  to  support  continuous 
learning.  This  also  includes  the  establishment  of  a  network  and  digital  platform  to  exchange, 
optimise and manage those (regional) training activities. 

22.7   Extend  existing  programmes  like  ERASMUS41,  European  Schoolnet42  and  e-twinning43  to  AI.  This 

creates synergies and allows to access existing know-how, networks and infrastructure.  

23.

INCREASE THE PROPORTION OF WOMEN IN SCIENCE AND TECHNOLOGY 

Today,  only  12%  of  leading  machine-learning  researchers  are  female.44  Empirical  evidence  shows  that 
despite the growing demand of ICT specialists and digital profiles, the percentage of Europeans with ICT-
related education is decreasing.45  Compelling evidence  of the  impact  of  gender stereotypes  is the  “self-
efficacy gender gap” which is basically non-existent (or even reversed in favour of girls) at the primary and 
lower secondary education levels, but starts to be measured in secondary levels and becomes even more 
pronounced at  the tertiary level.46  Furthermore,  the  prevalence of male norms and networks in science 
and  technology  professions  disadvantage  women  in informal  selection  and  promotion  processes.47  As  a 
consequence,  Europe  is  currently  not  exploiting  its  talent  pool,  causing  an  annual  productivity  loss.  For 
instance, in ICT-related industries only, this loss extends to EUR 16.2 billion for the European economy to 

41  

42  

43  

44  

45  

46  

47  

involved,  to  communicate,  collaborate,  develop  projects  and  share.  For  more 

The European Union's Erasmus+ programme is a funding scheme to support activities in the fields of Education, Training, 
Youth and Sport. For more information, see https://eacea.ec.europa.eu/erasmus-plus_en. 
European  Schoolnet  is  the  network  of  35  European  Ministries  of  Education,  based  in  Brussels,  bringing  innovation  in 
teaching  and  learning  to  Ministries  of  Education,  schools,  teachers,  researchers,  and  industry  partners.  For  more 
information, see: http://www.eun.org/home. 
eTwinning offers a platform for staff (teachers, head teachers, librarians, etc.), working in a school in one of the European 
countries 
information,  see: 
https://www.etwinning.net/en/pub/index.htm. 
UNESCO  Report  (2019):  “I’d  blush  if  I  could  –  Closing  Gender  Divides  in  Digital  Skills  through  Education”,  access: 
https://unesdoc.unesco.org/, last access 03.06.2019. 
Quirós, C. T., Morales, E. G., Pastor, R. R., Carmona, A. F., Ibáñez, M. S., & Herrera, U. M. (2018). Women in the digital age. 
European  Commission.  Retrieved  from  https://ec.europa.eu/digital-single-market/en/news/increase-gender-gap-digital-
sector-study-women-digital-age. 
Fraillon,  J.,  Ainley,  J.,  Schulz,  W.,  Friedman,  T.  and  Gebhardt,  E  (2014):  Preparing  for  Life  in  a  Digital  Age:  The  IEA 
International Computer and Information Literacy Study International Report. Amsterdam, international Association for the 
Evaluation of Educational Achievement. 
Haas, M., Koeszegi, S.T., and Zedlacher, E. (2016): Breaking Patterns: How Female Scientists Negotiate their Token Role in 
their Life Stories, Gender Work and Organization, Vol. 23 (4), pp. 397-413, doi:10.1111/gwao.12124. 

34 

 
 
 
                                                           
women leaving their digital jobs to become inactive.48 An increased proportion of women in science and 
technology  will  increase  the  relevance  and  quality  of  research  and  innovation  outputs  for  society  as  a 
whole.49 Consequently, closing the gender gap in digital skills requires aggressive and sustained efforts. 

23.1  Prioritise gender equality and gender mainstreaming in all AI policies by also making use of gender 
budgeting tools and the implementation of gender quota where appropriate. Target a proportion of 
female talents of at least 30 % in AI higher education, AI workforce and AI eco-systems by 2030. 

23.2  Attract  female  talents  into  the  field  of  AI  and  related  subjects  through  sustained,  dedicated  and 
substantial funding programmes and attractive scholarships based on inclusive practices (including 
intersectional approaches)  

23.3  Sponsor initiatives for networking and coaching for Women in AI. The importance of networking, 
coaching and meeting role models has been emphasised repeatedly in the literature on women in 
science and technology fields. Thus, it is essential to establish and fund AI mentorship programmes 
as well as to create safe spaces for women and girls both within and outside the formal education 
sectors to support and encourage women in AI careers. 

23.4  Incorporate  humanities,  social  sciences,  and  gender  research  into  AI  research  programmes  to 

increase diversity and guarantee a multidisciplinary approach. 

23.5  Address  gender  bias  in  algorithmic  decision-making,  e.g.  in  the  hiring  funnel,  institutionalized 
biases are particularly difficult to track. Address the fact that AI implementation specifically affects 
women  (professions)  in  labour  markets.  More  research  is  needed  to  understand  the  impact  on 
traditionally “female” jobs. 

24.

  UPSKILL AND RESKILL THE CURRENT WORKFORCE 

The Future of Jobs Report of the World Economic Forum50 forecasts a substantial reskilling need for every 
second employee within the next four years. Education and training thus have to be at the centre of the 
implemented  strategies  to  enlarge  the  talent  pool  for  Europe.  Continuous  training  measures will  play a 
key  role  in  helping  people  to  anticipate,  adapt,  upskill,  retrain  and  take  advantage  of  the opportunities 
presented by new AI-related activities as part of broader career support mechanisms. Continuous learning 
activities  and vocational education have  to be supported to cope  with the challenges  of digital skills,  as 
well as the new social and behavioural skills needed in a human-machine working environment.  

24.1  Create a right to continuous learning for all and implement it by law and/or collective agreements. 
Adapt continuous learning systems to equip workers with both tech-related and personal skills, and 
introduce  a  work-life-train  balance.  This  includes  the  need  for  strong  career  guidance  and 
professional  development  for  workers  whose  job  is  threatened  by  automation  with  important 

48  
49  

See footnote 44 above. 
Diversity  research  has  established  that  in  general  diversity  increases  problem  solving  capabilities  and  decision  quality, 
performance  and  innovation.  See  Schneider,  J.,  &  Eckl,  V.  (2016).  The  difference  makes  a  difference:  Team  diversity  and 
innovative  capacity.  Mimeo,  Gender  Diversity  Impact  (GEDII)  (2018):  Project  Deliverable  D4.3  Survey  Analysis  and 
Performance Indicator. Research Report. Online available at: https://www.gedii.eu (checked: 19.07.2018); Hoisl, K.; Gruber, 
M.  and  Conti,  A.  (2017):  R&D  Team  Diversity  and  Performance  in  Hypercompetitive  Environments.  In:  Strategic 
Management Journal 38 (7), 1455-1477; Lorenzo, R.; Voigt, N.; Schetelig, K.; Zawadzki, A.; Welpe, I.; Brosi, P. (2017): The 
Mix 
at: 
Through 
https://www.bcg.com/dede/publications/2017/people-organization-leadership-talent-innovation-through-diversity-mix-
that-matters.aspx  (checked:  19.07.2018);  Østergaard,  C.R.;  Timmermans,  B.;  Kristinsson,  K.  (2011):  Does  a  different  view 
create something new? The effect of employee  diversity on innovation. In: Research Policy 40 (3), 500-509; Schneid, M.; 
Isidor, R.; Steinmetz, H.; Kabst, R. (2016): Age diversity and team outcomes: a quantitative review. In: Journal of Managerial 
Psychology 31 (1), 2-17.  

Innovation 

Diversity. 

available 

Matters. 

Online 

That 

50    World Economic Forum, The future of Jobs (2018), accessible at: http://reports.weforum.org/future-of-jobs-2018/preface/. 

35 

 
 
                                                           
training  entitlements  and  the  development  “professional  passports”  to  ensure  the  portability  of 
skills. 

24.2  Define  critical  skills  in  sensitive  areas  with  human  safety  and  security  critical  implications  and 
create measures against skill deterioration. Address undesirable de-skilling of workers through AI, 
in particular in operations or processes that require human oversight or intervention. Promote and 
support the development of new skills transfer and acquisition programmes to enable workers made 
redundant or faced with the threat of redundancy due to automation and increased AI take-up to 
acquire new skills that will enable them to seek new forms of employment as the structure of the 
labour  market  is  reshaped  in  response  to  the  turn  to  increased  reliance  on  digital  services  and 
processes. 

24.3  Develop employment policies that support and reward companies who are setting up strategic up- 
and  reskilling  plans  for  the  development  of  new  data  and  AI-related  applications.  Encourage 
organisations  to  conduct  a  strategic  workforce  planning  including  a  plan  for  existing  workforce  to 
upskill towards AI, also with potential support from universities and consultancies. Employers should 
refrain from introducing new technologies with a disrupting impact on jobs without having initially 
properly worked on reskilling plans and alternatives for workers. 

24.4  Upgrade  the  European  Computer  Driving  License  (ECDL)  to  an  AI  Driving  License.  The  ECDL  is  a 
computer literacy programme supported by the ECDL Foundation.51 Europe should develop a similar 
syllabus and certification scheme for artificial intelligence. 

25.

  CREATE STAKEHOLDER AWARENESS AND DECISION SUPPORT FOR SKILLING POLICIES 

The enormity of these educational tasks calls for a number of social innovations, provided through a multi-
stakeholder model. Governments, social partners (employers and trade unions) and vocational education 
and  training  providers  are,  in  Europe,  the  main  stakeholders  defining  training  priorities,  ensuring  cross-
sectoral and sectoral funding, and delivering trainings to workers. These stakeholders need to be aware 
of, and be up to date with, the effects of AI on job markets. Furthermore, they have to be provided with 
appropriate decision support and in order to be able to pro-actively design effective policy measures. 

25.1  Increase  the  knowledge  and  awareness on  AI within  EU and national policy-making institutions. 
This  will  help  them  to  make  well-reasoned  policy  decisions.  Moreover,  effective  oversight  of 
regulated  entities  deploying  AI-enabled  tools  will  also  require  that  supervisors  are  equally 
knowledgeable  about  AI  and  aware  of  the  developing  trends  in  their  respective  regulatory 
perimeter.  One  method  to  achieve  this  is  through  setting  digital  affairs  committees  in  national 
parliaments gathering politicians with diverse backgrounds including AI experts. 

25.2  Foster the development of advanced skills and jobs forecasting algorithms to anticipate and timely 
address changes on the job market. This will allow the forecasting of job- and skills disruption across 
numerous sectors, and enable organisations, policy-makers and unions to collaborate on re- and up- 
skilling  programmes  and  for  workers.  At  the  same  time,  it can  help  addressing  one  of the  biggest 
challenges of the digital era, namely job insecurity and anxiety for the future generations. 

25.3  Reinforce or develop strong training and career guidance systems that support workers affected 
by technological disruption. Monitor changes in labour markets and make the European Social Fund 
and  the  European  Globalisation  Adjustment  Fund  (EGF)  more  responsive  by  dedicating  more 
programmes  to  up-skilling  strategies  and  making  EGF  intervention  more  preventive  and  less 

51  

For more information, see: http://www.ecdl.org.  

36 

 
 
                                                           
curative.  Furthermore,  maintain  digital  skills  and  job  coalition  initiatives  that  match  workers  and 
companies seeking their skills and encourage the creation of tools allowing employees to conduct a 
“self-assessment of employability”, so that they can map and visualise their competences. 

25.4  Include  critical  and  ethical  awareness  of  AI  in  existing  European  awareness  initiatives  and 
networks, such as those under the pan-European Better Internet for Kids programme. In addition, 
promote critical vigilance to industry interests in such awareness programmes. 

25.5  In public procurement processes of AI-enabled technology for education, include an assessment of 
embedded  interests,  ethics  and  social  impacts.  Such  products  should  not  be  deployed  ad  hoc  in 
educational settings based on the free availability or promotional access to educators or institutions, 
but  must  be  based  on  an  assessment  of  their  ethical  implications  and  the  commercial  or  other 
embedded  interests/possible  trade-offs  between  interests.  Promote  critical  and  ethical  awareness 
of available AI technology products used in education by educators and educational institutions and 
consider  developing  a  standard  for  AI  tools  in  education  built  on  the  seven  key  requirements  for 
Trustworthy AI as developed in our Ethics Guidelines. 

G. Establishing an appropriate governance and regulatory framework   

Ensuring  Trustworthy  AI  necessitates  an  appropriate  governance  and  regulatory 
framework. By appropriate, we mean a framework that promotes socially valuable 
AI  development  and  deployment,  ensures  and  respects  fundamental  rights,  the 
rule  of  law  and  democracy,  while  safeguarding  individuals  and  society  from 
unacceptable harm. On 8 April 2019, we published our Ethics Guidelines  that set 
out three components for Trustworthy AI: (1) lawful AI, (2) ethical AI and (3) robust 
AI. The Ethics Guidelines only deal with the two latter components, yet the first is 
equally  crucial.  Many  of  the  principles  set  out  in  the  Guidelines  reflect  existing  EU  law.  This  section 
complements the Guidelines by providing guidance on appropriate governance and regulatory approaches 
beyond voluntary guidance. 

We  acknowledge  that  ensuring  an  appropriate  framework  that  maximises  the  benefits  of  AI  and  that 
prevents and minimises its risks is no easy task. Europe takes pride in its sound regulatory environment 
that  enables  and  stimulates  AI  development  and  deployment  through  fostering  legal  certainty  and 
providing  a  distinct  global  competitiveness  element,  while  at  the  same  time  safeguarding  fundamental 
rights and protecting individuals and society from risk or harm, guided principally by the proportionality 
principle.  Yet  to  deliver  trustworthy  AI  for  Europe,  independent  and  meaningful  oversight  mechanisms 
need  to  be  established,  and  an  expansion  of  the  institutional  capacities,  expertise  and  instruments  of 
policymakers is needed. Yet little evidence is available to inform policy-making, due to the novelty of the 
technology,  the  lack  of  thorough  and  systematic  understanding  of  its  impacts  and  associated  business 
models, and the unpredictability of its uptake, development and evolution even in the short term. Bearing 
this in mind, here below we propose a number of recommendations that should already be considered by 
policy-makers at EU and national level. 

26.

  ENSURE APPROPRIATE POLICY-MAKING BASED ON A RISK-BASED AND MULTI-STAKEHOLDER APPROACH  

26.1  Adopt  a  risk-based  approach  to  regulation.  The  character,  intensity  and  timing  of  regulatory 
intervention  should  be  a  function  of  the  type  of  risk  created  by  an  AI  system.  In  line  with  an 

37 

 
 
 
approach  based  on  the  proportionality  and  precautionary  principle,  various  risk  classes  should  be 
distinguished as not all risks are equal.52 The higher the impact and/or probability of an AI-created 
risk, the stronger the  appropriate regulatory response  should be. ‘Risk’  for this purpose  is broadly 
defined to encompass adverse impacts of all kinds, both individual and societal.53 

26.2  For  specific  AI  applications  that  generate  “unacceptable”  risks  or  pose  threats  of  harm  that  are 
substantial,  a  precautionary  principle-based  approach  should  be  adopted  instead.54  Regulatory 
authorities  should  adopt  precautionary  measures  when  scientific  evidence  about  an 
environmental, human  health  hazard  or  other  serious  societal  threat  (such  as  threats  to  the 
democratic  process),  and  the  stakes  are  high.  Questions  about  the  kinds  of  risks  deemed 
unacceptable  must  be  deliberated  and  decided  upon  by  the  community  at  large  through  open, 
transparent  and  accountable  deliberation,  taking  into  account  the  EU’s  legal  framework  and 
obligations under the Charter of Fundamental Rights. 

26.3  Give  due  consideration  to  the  level  of  autonomy  in  AI-based  decision-making  (e.g.  is  it  an 
information  source  only,  a  support  function,  or  a  fully  autonomous  system  without  human 
involvement)  and the  autonomy in learning  when developing and updating policy measures for AI 
systems.  

26.4  Foster  a  principle-based  approach  to  regulation.  Unnecessarily  prescriptive  regulation  should  be 
avoided.  In  contexts  characterised  by  rapid  technological  change,  it  is  often  preferable to  adopt a 
principled-based  approach,  as  well  as  outcome-based  policies,  subject  to  appropriate  monitoring 
and enforcement. The European Commission should ground its policy measures on AI in EU values, 
as  discussed  and  presented  in  our  Ethics  Guidelines,  and  should  translate  our  aspirational  goal  of 
Trustworthy AI into a concrete set of indicators that can be used for monitoring the convergence of 
the European market towards the desired policy goals. 

26.5  Consider the adoption of a segment-specific methodology when further developing the regulatory 
framework for AI.  Both the  necessary measures to protect  individuals against  adverse effects and 
the market environment of AI products and services developed and deployed in the B2C, B2B and 
P2C contexts differ from each other and merit a tailored approach.   

27.

  EVALUATE AND POTENTIALLY REVISE EU LAWS, STARTING WITH THE MOST RELEVANT LEGAL DOMAINS 

27.1  Conduct a systematic mapping and evaluation of all existing EU laws that are particularly relevant 
to  AI  systems.  This  should  be  done  through  a  Commission  Inter-Service  group  that  ensures 
collaboration  across  different  DGs  or  the  Secretariat-General  to  avoid  “silo”-thinking.  Three 
questions are to be  addressed in particular: (1) To what extent  are the policy  and legal objectives 
underpinning  these  legislative  provisions  affected  by  AI  systems  and  in  what  ways?  (2)  To  what 

52  

53  

54  

This can for instance be done on the basis of their impact (acceptable or unacceptable) and/or  probability of occurrence 
(certain v uncertain).  
This includes not only tangible risks to human health or the environment, but also includes intangible risks to fundamental 
rights,  democracy  and  the  rule  of  law,  and  other  potential  threats  to  the  cultural  and  socio-technical  foundations  of 
democratic, rights-respecting, societies. For a discussion of adverse impacts associated with contemporary AI systems, see 
Council of Europe (2019), Revised draft study of the implications of advanced digital technologies (including AI systems) for 
the concept of responsibility within a human rights framework. Available at: https://rm.coe.int/a-study-of-the-implications-
of-advanced-digital-technologies-including/168094ad40. 
The European Commission has held that “whether or not to invoke the Precautionary Principle is a decision exercised where 
scientific information is insufficient, inconclusive, or uncertain and where there are indications that the possible effects  on 
the environment, or human, animal or plant health may be potentially dangerous and inconsistent with the chosen level of 
protection”. Communication from the European Commission on the Precautionary Principle, COM/2000/0001, available at: 
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52000DC0001. 

38 

 
 
                                                           
extent  are  existing  frameworks  for  monitoring,  information-gathering  and  enforcement  of  the 
legislative  measures  capable  of  providing  meaningful  and  effective  oversight  to  ensure  that  the 
policy  and  legal  objectives  are  still  effectively  met?  (3)  To  what  extent  does  existing  legislation 
operate in ways to promote and ensure the ethical principles and requirements set out in the Ethics 
Guidelines?  

27.2  For  civil  liability55  and  accountability  rules:  in  the  context  of  laws  in  areas  significantly  affecting 
individuals,  consider  whether  for  safety-critical  and  fundamental  rights-critical  applications  it  is 
necessary  or  desirable  to  introduce  traceability  and  reporting  requirements  for  AI  applications  to 
facilitate their auditability, ex-ante external oversight before AI systems can be deployed, systematic 
monitoring  and  oversight  by  competent  authorities  on  an  ongoing  basis,  and  the  obligation  for 
meaningful  human  intervention  and  oversight  when  using  AI  decision  in  specific  sectors  (e.g.  a 
human  doctor  to  check  a  medical  treatment  decision).  Finally,  civil  liability  rules  must  be  able  to 
ensure adequate compensation in case of harm and/or rights violations (either through strict or tort 
liability), and may need to be complemented with mandatory insurance provisions. 

27.3  For criminal law provisions: consider the need to ensure that criminal responsibility and liability can 

be attributed in line with the fundamental principles of criminal law.   

27.4  For  consumer  protection  rules:  consider  the  extent  to  which  existing  laws  have  the  capacity  to 
safeguard against illegal, unfair, deceptive, exploitative and manipulative practices made possible by 
AI  applications  (for  instance  in  the  context  of  chatbots,  include  misleading  individuals  on  the 
objective,  purpose  and  capacity  of  an  AI  system)  and  whether  a  mandatory  consumer  protection 
impact assessment is necessary or desirable. 

27.5  For data protection rules: consider whether existing laws allow sufficient access to public data and 
data  for  legitimate  research  purposes  whilst  preserving  privacy  and  personal  data  protection,  the 
appropriate  scope  of  intellectual  property  rights  protection,  and  whether  the  GDPR  mandated 
transparency  and explainability offers sufficient protection in light  of the limitation of its scope to 
the  processing  of  personal  data  and  the  fact  that  automated  decision-making  processes  can  also 
significantly  affect  individuals  when  the  system  is  not  fully  automated  or  based  on  non-personal 
data. 

27.6  For  non-discrimination  provisions:  consider  the  extent  to  which  laws  prohibiting  unlawful 
discrimination  require  the  explicitation  of  obligations  upon  AI  developers  to  verify  the  absence  of 
unjust  bias  in  AI  systems’  decisions,  and  the  adequacy  of  enforcement  mechanisms  against 
discriminatory outcomes. 

27.7  For  cyber-security  rules:  consider  the  extent  to  which  the  current  cybersecurity  regime  provides 

sufficient protection against cybersecurity risks posed by AI systems.  

27.8  For competition rules: consider the volume of data or incumbency data advantages – the building 
block of many AI systems – in the assessment of market power for the purposes of applying rules on 
anti-competitive  behaviour,  abuse  of  dominance  or  (algorithmic)  collusion,  and  when  evaluating 
mergers.  

55  

It should be noted that the European Commission has set up a separate Expert Group with the task to evaluate the current 
legal 
is  available  at: 
http://ec.europa.eu/transparency/regexpert/index.cfm?do=groupDetail.groupDetail&groupID=3592.  

this  Expert  Group  and 

information  on 

liability.  More 

framework 

its  work 

for 

39 

 
 
 
                                                           
28.

  CONSIDER THE NEED FOR NEW REGULATION TO ENSURE ADEQUATE PROTECTION FROM ADVERSE IMPACTS 

28.1  Examine the need for new regulation to address the critical concerns listed in our Ethics Guidelines 
for  Trustworthy  AI.  More  generally,  it  should  continuously  be  evaluated  whether  AI  systems 
generate  risks  that  are  not  adequately  addressed  by  existing  legislation.  In  particular,  individuals 
should not be subject to unjustified personal, physical or mental tracking or identification, profiling 
and  nudging  through  AI  powered  methods  of  biometric  recognition  such  as:  emotional  tracking, 
empathic  media,  DNA,  iris  and  behavioural  identification,  affect  recognition,  voice  and  facial 
recognition and the recognition of micro-expressions. Exceptional use of such technologies, such as 
for  national  security  purposes,  must  be  evidence  based,  necessary  and  proportionate,  as  well  as 
respectful of fundamental rights.  

28.2  Monitor and restrict the development of automated lethal weapons, considering not only actual 
weapons, but also cyber attack tools that can have lethal consequences if deployed. With respect to 
offensive LAWS56, advocate to the Member States to actively participate in the ongoing international 
debate, involve internationally recognised, non-military funded scientists and academics, experts in 
artificial  intelligence,  and  propose  to  international  partners  the  adoption  of  a  moratorium  on  the 
development of offensive LAWS.  

28.3  Monitor the development of personalised AI systems built on children’s profiles and ensure their 
alignment  with  fundamental  rights,  democracy  and  the  rule  of  law.  Consider  introducing  a  legal 
age at which children receive a “clean data slate” of any public or private storage of data related to 
them as children.57   

28.4  For AI systems deployed by the private sector58 that have the potential to have a significant impact 
on human lives, for example by interfering with an individual’s fundamental rights at any stage of 
the  AI  system’s  life  cycle59  and  for  safety-critical  applications,  consider  the  need  to  introduce:  a 
mandatory  obligation  to  conduct  a  trustworthy  AI  assessment  (including  a  fundamental  rights 
impact assessment which also covers for example the rights of children, the rights of individuals in 
relation  to  the  state,  and  the  rights  of  persons  with  disabilities60)  and  stakeholder  consultation 
including  consultation  with  relevant  authorities;  traceability,  auditability  and  ex-ante  oversight 
requirements;  and  an  obligation  to  ensure  appropriate  by  default  and  by  design  procedures  to 
enable effective and immediate redress in case of mistakes, harms and/or other rights infringement. 

29.

  CONSIDER WHETHER EXISTING INSTITUTIONAL STRUCTURES, COMPETENCES AND CAPACITIES NEED REVISION TO 

ENSURE PROPORTIONATE AND EFFECTIVE PROTECTION 

We  recommend  a  systematic  evaluation  of  the  extent  to  which  existing  institutional  structures, 
competences,  capacities,  resources,  investigation  and  enforcement  powers  arising  under  existing 
legislation are capable of adequately ensuring meaningful and effective information-gathering, monitoring 
and enforcement  of legal standards in ways that provide  proportionate and effective  protection.  In this 
regard, consideration may be warranted for the necessity or desirability of the points listed here below. 

56  

57  

These  are  military  robots  which,  without  any  human  intervention,  can  select,  engage,  attack  and  hit  civilian  and  military 
targets. 
This  does  not  exclude  the  possibility  for  appropriate  exceptions  that  are  necessary  in  a  democratic  society  and  in  full 
compliance with fundamental rights. 
For AI systems in the public sector, please refer to Recommendation 12.1. 

58  
59   When referring to AI systems with a high or significant risk for individuals, reference can be made to Art 35 of the GDPR, 

which could serve as a model for a definition. 

60   With due regard to the UN Convention on the Rights of persons with disabilities. 

40 

 
 
                                                           
29.1  Ensure the consolidation of EU institutions’ “policy cycle” approach by more systematic monitoring 
and  periodic  ex-post  evaluation  of  regulatory  measures,  and  by  enhancing  stakeholder 
consultations. The stakeholder consultation system of the Commission should be adapted to the AI 
age, and important ethical questions should be approached with the help of a wide consultation of 
civil society. In this context, the European AI Alliance could be further developed and relied on as an 
important channel.  

29.2  Consider  agile  policy-making  solutions  such  as  the  creation  of  regulatory  sandboxes  to  help 
stimulating innovation without creating unacceptable risks. The creation of regulatory sandboxes 
should involve multiple public and private stakeholders (including NGOs) to support innovators and 
allow  for  high  pace  viability  assessments  for  new  innovations  and  business  models,  without 
hampering the public interest and the protection of individuals. The limitations of sandboxes should 
be  assessed,  and  the  sandbox’  transparency  and  accountability  must  be  ensured,  including  the 
availability  of  avenues  of  redress  in  case  of  harm  or  adverse  impact.  Sandboxes  can  also  help 
impact 
developing  impact  assessment  capabilities,  which  facilitates  the  fundamental  rights 
assessment of experimental AI implementations. 

29.3  Institutionalise a dialogue on AI policy with affected stakeholders to define red lines and discuss AI 
applications  that  may  risk  generating  unacceptable  harms,  including  applications  that  should  be 
prohibited  and/or  tightly  regulated  or  in  specific  situations  where  the  risk  for  people’s  rights  and 
freedoms would be too high and the impact of this technology would be detrimental to individuals 
or society as a whole. This could for instance be done through the European AI Alliance. 

29.4  Develop auditing mechanisms for AI systems. This should allow public enforcement authorities as 
well  as  independent  third  party  auditors  to  identify  potentially  illegal  outcomes  or  harmful 
consequences generated by AI systems, such as unfair bias or discrimination.  

29.5  Ensure that the use of AI systems that entail interaction with end users is by default accompanied 
by procedures to support users in accessing effective redress in case of infringement of their rights 
under applicable laws. These procedures should be accompanied by simple explanations and a user-
friendly procedure, and should entail interaction with a human interlocutor whenever possible and 
chosen by the  user. Access to justice  and effective  redress  are key elements of building consumer 
trust and thus are an important part of Trustworthy AI. 

29.6  Foster the availability of redress-by-design mechanisms. This entails establishing – from the design 
phase – mechanisms to ensure alternative systems and procedures with an adequate level of human 
oversight (human in the loop, on the loop or in command approach) to be able to effectively detect, 
audit, and rectify incorrect decisions taken by a "perfectly" functioning system, for those situations 
where the AI system’s decisions significantly affects individuals.  

29.7  In addition, we urge policy-makers to refrain from establishing legal personality for AI systems or 
robots.  We  believe  this  to  be  fundamentally  inconsistent  with  the  principle  of  human  agency, 
accountability and responsibility, and to pose a significant moral hazard. 

30.

  ESTABLISH GOVERNANCE MECHANISMS FOR A SINGLE MARKET FOR TRUSTWORTHY AI IN EUROPE  

Achieving the Single European Market is crucial both to ensure a level playing field in terms of protective 
regulation, as well as to enable competitiveness of European companies both in Europe and globally. The 
New Legislative Framework adopted in 2008 can be an enabling framework to accelerate this. Subject to 
the provisions of subsidiarity and proportionality, we propose that new regulatory frameworks related to 
AI systems, including all digital aspects associated therewith, should be developed at European level. 

41 

 
 
30.1  Harmonise  regulation  –  including  its  implementation  and  enforcement  mechanisms  –  across  the 
EU and foster cooperation between Member States regarding regulatory frameworks relevant to AI 
throughout  Europe.  In  particular,  complete  and  harmonise  the  EU  data  regulatory  framework. 
Harmonisation should however not preclude existing higher standards of protection at national level 
with  regards  to  individuals,  for  instance  regarding  individuals'  health  and  safety  and  consumer 
protection. 

30.2  Cumulative  regulatory  interventions  at  the  national  level  should  be  avoided,  unless  strictly 
satisfying  the  principles  of  subsidiarity,  necessity  and  proportionality.  Fragmentation  of  rules  at 
Member  State  level  should  be  avoided  and  the  creation  of  a  true  European  Single  Market  for 
Trustworthy AI systems and services should be prioritised.61 The relevant EU law mapping exercise 
proposed under Recommendation 27.1 should also identify areas where harmonisation of rules may 
be appropriate to foster this goal. 

30.3  Establish  a  comprehensive  strategy  for  Member  State  cooperation  for  the  enforcement  of 
regulation relevant to AI through enforcement authorities such as data protection, consumer and 
competition  authorities,  ensuring  that  they  cooperate  across  sectors  systematically  and  apply  EU 
regulations  in  a  consistent  manner  throughout  different  Member  States  and  that  they  have 
adequate and equally effective resources and enforcement tools in place. 

30.4  Foster the harmonisation of Trustworthy AI standards by contributing to the Guidelines’ piloting 
process  and  ensuring  an  appropriate  follow-up.  With  the  aim  to  establish  a  European-wide 
framework  for  trustworthy  AI,  the  Ethics  Guidelines  put  forward  a  practical  assessment  list.  To 
better  understand  how  this  guidance  can  be  implemented  in  different  applications,  sectors  and 
Member States, a piloting process was established. Member States should encourage stakeholders 
to participate in this process and ensure funding to enable civil society organisations to participate. 
European Institutions and (national) public organisations should participate themselves in case they 
develop, deploy or use AI systems. Moreover, the Commission should ensure an appropriate follow-
up process to the revised assessment list in early 2020. 

30.5  The European Union should consider the setting up an institutional structure for Trustworthy AI to 

fill an existing governance gap. It would be tasked with the following missions: 

-  Contribute  to  developing  the  EU’s  framework  and  policy  for  Trustworthy  AI,  ensuring  that  AI  is 
lawful, ethical and robust, by advising EU institutions and supporting them in the implementation 
of such framework; 
Provide guidance to stakeholders on how their AI applications can comply with the applicable law 
and the principles and requirements for Trustworthy AI; 

- 

-  Assist  the  public  and  private  sector  in  the  application  of  a  risk-based  approach.  This  includes 
providing  opinions  for  the  assessment  of  the  intensity,  probability  and  unacceptability  of  AI-
created risks; 

-  Oversee  politically  defined  system-wide  targets  for  AI  applications  that  can  significantly  impact 

individuals and society, considering systemic effects both in terms of benefits and risks; 

-  Help standard setting organisations define target outcomes for Trustworthy AI, in particular in the 

context of public-private cooperation on standards; 

61  

At  the  same  time,  we  acknowledge  that  there  may  be  specific  sectors  where  additional  regulatory  requirements  apply 
compared to the future horizontal policy framework for AI in the EU: sectors such as banking, insurance and healthcare are 
already exposed to significant regulatory requirements that are likely to overlap with the emerging policy framework for AI. 
In these sectors, as well as any other sector that will features such overlaps, it is essential that the European Commission 
performs regular fitness checks to ensure that legislation is proportionate, clearly designed and effectively implements. 

42 

 
 
                                                           
Promote cooperation amongst Member States for the implementation of Trustworthy AI; 

- 
-  Host and update a repository of best practices that are deemed compliant with applicable law and 

the Trustworthy AI principles and requirements; 

-  Contribute  to  preparing  for  socio-economic  changes  brought  about  by  AI,  for  example  through 

awareness campaigns and training measures for public institutions. 

30.6  Develop  guidance  for  stakeholders  on  how  to  ensure  compliance  with  Trustworthy  AI’s  first 
component, lawful AI, for each of the seven requirements. In the Guidelines, guidance is provided 
on how to ensure the implementation of ethical and robust AI for each of the seven requirements. 
However, as those requirements are also already captured by a number of legal obligations, and as 
legal uncertainty exists regarding the extent to which such legal obligations are already in place, a 
guidance document on this first component would be welcomed.  

30.7  Consider a range of certification mechanisms for AI systems, and assess the necessity of technical 
standards  and  other  standardisation  measures.62  The  development  of  co-regulation  mechanisms 
for the certification of AI systems at EU level could counter fragmentation of standards, but could 
also help provide the means to assess the quality of an AI solution after deployment and possibly to 
decide which solution is best. Feedback received through the piloting phase of the Ethics Guidelines 
could  help  assess  the  necessity  and  shape  of  any  such  mechanism.  As  concerns  standardisation, 
Europe should develop a clear strategy in terms of key standardisation fora and adequate resources. 
A  European  vision  is  needed  on  the  main  components  of  Trustworthy  AI  that  may  necessitate 
standards. Finally, it should be ensured that AI systems comply with mandatory sectoral certification 
requirements that already exist today.63  

H.  Raising Funding and Investment  

The availability of capital is essential for rapid market expansion, and Europe has a 
challenging  position  with a  two-  to  five-fold  investment  gap  compared  with  other 
world regions. Europe currently attracts only ~11% of global VC funding, with ~50% 
going to US and the rest mostly to China.64 An assessment of the economic activity 
growth due to AI until 2030 shows that the value at stake for Europe is significant: if 
no actions are taken, the EU28 will suffer a deterioration of its innovation capital, which would result in a 
loss of €400 billion in cumulative added value to GDP by 2030.65 On the other hand, if Europe develops 
and diffuses AI according to its current assets and digital position relative to the rest of the world, it could 
add  €2.7  trillion,  or  19%,  to  output  by  2030.66  Investments  in  companies  supported  by  the  European 
Investment Fund across the EU shows a stabilisation of around €90 million per year over 2017-2018 in the 
EU27  (€128  million  for  the  EU28)  for  about  50  AI-related  deals  (70  for  the  EU28),67  which  is  clearly 
insufficient for the EU to be a major actor in volume of investments. The EU therefore urgently needs an 
intensification of investments. 

62  

63  

64  

65  
66  
67  

Consideration  should  be  given  to  the  failure  of  certification  systems  to  safeguard  against  the  implementation  of  ‘cheat 
devices’ in diesel vehicles by a leading car manufacturer on a massive scale, and lessons learned from that experience. 
See  in  this  regard  also  the  New  Legislative  Framework  for  instance,  on  which  more  information  can  be  found  at: 
https://ec.europa.eu/growth/single-market/goods/new-legislative-framework_en. 
See  KPMG,  Venture  Pulse,  2018,  https://assets.kpmg/content/dam/kpmg/xx/pdf/2019/01/kpmg-venture-pulse-q4-
2018.pdf. 
See McKinsey Global Institute, Notes from the AI frontier, Tackling Europe’s Gap in Digital and AI, February 2019. 
Ibidem. 
Data includes all deals notified to the European Investment Fund up until 31/1/2019 and might not yet reflect all 2018, Q4 
deals. 

43 

 
 
 
                                                           
The  private  investment  gap  in  AI  in  Europe,  which  “totalled  around  € 2.4-3.2 billion  in  2016,  compared 
with  € 6.5-9.7  billion  in  Asia  and  €  12.1-18.6  billion  in  North  America",68  was  duly  acknowledged  in  the 
Commission’s  AI  strategy  of  April  2018,  which  sets  an  ambitious  objective:  increase  investment  and 
reaching a total (public and private sectors combined) of at least €20 billion per year in the course of the 
next decade. To achieve this, the Coordinated Plan on AI prepared by the Commission and Member States 
set out a number of investment  actions. Furthermore,  the Commission proposed a number of financing 
initiatives  in  its  7-year  Multiannual  Financial  Framework  (MFF),  for  instance  in  terms  of  research  areas 
(such as explainable AI, unsupervised machine  learning or energy and data efficiency) or infrastructures 
(upgrading  the  pan-European  network  of  AI  excellence  centres,  creating  additional  Digital  Innovation 
Hubs, and establishing world-leading testing and experimentation facilities). The implementation of these 
actions  requires  the  swift  adoption  by  Member  States  of  the  MFF  and  its  instruments.  Amongst  those 
instruments, the Digital Europe programme (of which €2.5 billion is allocated to the uptake of AI) and the 
Horizon  Europe  programme  (of  which  €700  million  is  allocated  to  research  in  AI  and  other  digital 
technologies) are important steps. We address in this section the need to steer public funding towards the 
recommendations set out  in the  sections above, as we believe  this can help leveraging private  funding, 
and create a more attractive investment environment for trustworthy AI in Europe.  

31.

  ENSURE ADEQUATE FUNDING FOR THE RECOMMENDATIONS PUT FORWARD IN THIS DOCUMENT  

Throughout  this  document,  various  recommendations  necessitate  dedicated  funding  mechanisms  to  be 
put  in  place  by  the  European  Commission  together  with  Member  States  in  order  to  enable  their 
implementation. We limit ourselves here to the most significant ones: 

31.1  Transitions taking place on the job market and the workplace require a European transition fund 
to help managing this in a socially responsible way (Recommendation 3.6). Moreover, the re- and 
up-skilling  of  the  workforce  in  Europe,  and  the  securing  of  a  work-life-train  balance,  will  require 
significant  public  funding,  particularly  at national level  –  which will need  to be supported by even 
more significant levels of private funding.  

31.2  The  earmarking  of  substantial  funding  in  the InvestEU  programme  for AI  (Recommendations  6.1 
and  7.1)  can  help  organisations to  tackle  the  digital  transformation.  While  structurally  this  agency 
could be part of the European Investment Bank, its methods and instruments need to be adapted to 
the specific needs of the AI ecosystem.  

31.3  The  proposed  Multi-Stakeholder  Alliances  that  will  enable  AI  ecosystems  on  a  sectoral  basis 
(Recommendation 8.2) will necessitate the availability of public funds that can help back solutions 
proposed by the relevant stakeholders. Those Alliances can not only help in analysing the concrete 
investment  needs  for  the  sector  in  question  from  different  angles,  but  can  also  stimulate  funding 
commitments from industry through a collaborative environment of trust that such ecosystem can 
create. 

31.4  Within public procurement processes, both at EU and national level, a substantive amount should 
be  allocated  to  innovation-driven  solutions  (Recommendation  11.1).  This  can  serve  as  an 
instrument to fund innovative AI-based solutions in Europe. 

31.5  Dedicated,  significant  and  long-term  research  funding  is  needed  for  fundamental  and  purpose-
driven  research  on AI to maintain competitiveness of  European companies and  address  relevant 
societal  challenges  (Recommendation  14.1).  In  particular,  funding  should  be  made  available  to 

68  

See Euralarm report, available at: https://www.euralarm.org/download/87_e78189598ff22b4a957c46350831c66c. 

44 

 
 
                                                           
collaborative research, which can help creating projects of a critical mass on targeted topics, rather 
than focusing solely on individual projects without global coherence. This can help bringing research 
teams  together  to  work  towards  common  objectives.  Currently,  no  instrument  is  available  at 
European level that would support this type of high-level basic research, which could nevertheless 
play an anchoring role for researchers to stay in Europe, and attract the best ones from abroad. 

31.6  A structural and investment fund for European data infrastructure is proposed in order to support 
ecosystems to coordinate data sharing and access (Recommendation 18.1). Opening publicly funded 
data sources, and redirecting funding flows to foster collaboration between public and private sector 
is critical to enhance Europe’s competitiveness. 

31.7  The establishment of an institutional structure at EU level for Trustworthy AI will likewise require 
resources to be made available (Recommendation 30.5). Such resources should come both from the 
European  Union's  budget  and  from  Member  States,  as  close  cooperation  with  relevant  Member 
State authorities will be necessary.  

32.

  ADDRESS THE INVESTMENT CHALLENGES OF THE MARKET  

The forecast of the AI worldwide market value shows a fast growth, with AI reaching $118 billion by 2025 
from $9.5 billion in 2018.69 AI is a family of deep technologies at a stage of development where volumes of 
investments make a difference in terms of innovation speed and market share, notably in areas that will 
underpin  the  future  digital  systems  and  services.  With  the  digital  economy  being  characterised  by  a 
“winner  takes  all”-logic  and  diminishing  returns  for  late  entrants,  it  is  essential  for  European  actors  to 
invest as first movers so as to have the possibility to capture large market shares. Slow movers will have to 
catch up with established market players, which is expected to be difficult, especially as market growth is 
estimated to decline after having reached  a high in 2021.70 The market pull for European AI  start-ups is 
rather weak but needs to be anticipated to ensure that the engines of the European economy embrace 
the shift to AI, bringing benefits to the European economy.71 

32.1  Introduce EU level thematic actions to build synergies between national initiatives, to encourage 
new  public  initiatives  and  to  mobilise  and  leverage  private  investments.  Financing  by  EU 
Institutions  and  Member  States  is  crucial  for  creating  bigger  funding  pools  and  leveraging  private 
investments  to  AI-focused  start-ups  and  SMEs  –  and  for  accelerating  the  digital  transformation of 
Europe.  Member  States  need  to  seek  such  co-investments  at  European  level  (e.g.  through  their 
National  Promotional  Banks)  to  crowd-in  additional  investments  by  the  private  sector.  Such  co-
investments  also  have  to  address  the  large  investments  targeting  company  growth,  beyond  the 
priority actions of Member States already identified in the Coordinated Plan for AI. 

32.2  Encourage  Member  States  to  create  and  expand  the  testing  and  experimentation  facilities 
proposed  in  the  EU  initiative  of  the  Digital  Europe  programme,  and  raise  awareness  by  SMEs, 
notably from traditional sectors of the economy, to adopt and digitally transform. A dense network 
of Digital Innovation Hubs should be actively diffusing digital and AI-enabled technologies.  

32.3  Ensure financing avenues for larger market deals that can help established companies grow and 
scale up in the digital economy (“scale without mass” until generating revenues). Although existing 
European and national instruments primarily aim at the market gap in equity finance for the early- 

69  

70  
71  

Intelligence  Market  Forecasts,  https://www.tractica.com/research/artificial-intelligence-market-

Tractica,  Artificial 
forecasts, 19Q1. 
Ibidem. 
Roland Berger, Artificial Intelligence – A strategy for European start-ups, 2018. 

45 

 
 
                                                           
and growth stage of start-ups and SMEs (seed-series A), it is equally critical to address the market 
gaps of bigger deals, in the order of €100 million and more (series C and D).72 

32.4  Set up a European Coalition of AI Investors (ECAI), which gathers companies in Europe that pledge 

to allocate funding for and investment in the European AI ecosystem. 

33.

  ENABLE AN OPEN AND LUCRATIVE CLIMATE OF INVESTMENT THAT REWARDS TRUSTWORTHY AI 

33.1  Encourage  the  Commission  to  work  with  European  financial  institutions,  such  as  the  European 
Investment  Bank,  to  develop investment  guidelines  that  take  into  account  the  Ethics Guidelines, 
leading  to sustainable business  developments.  This could take  the form of a criterion in the social 
proofing of future financial investments such as InvestEU. The appraisal of the Ethics Guidelines by 
all  stakeholders,  and  notably  industry  and  other  international  organisations,  indicates  how 
technologies with human-centric values are critical to ensuring societal acceptance.  

33.2  Ensure  the  EU  remains  an  open  economy,  and  a  lucrative  place  for  innovators  and  investors. 
Europe's lucrative investment environment is the sum of a number of enabling elements – from its 
labour  and  immigration  regime  to  the  legal  certainty  created  through  its  regulation  –  that  as  an 
aggregate  can  provide  the  necessary  level  of  attraction  to  bring  in  investors.  Achieving  the  AI 
investment target set by the EU will require an increase in the EU's investment attractiveness. In the 
current context of global trade uncertainties and protectionist actions by other world regions, it is 
essential that  the  EU  remains a champion of free trade  and investment in the world, while  taking 
decisive action to counter unfair practices by third countries.73  

72  
73  

KPMG, Venture Pulse (2018), https://assets.kpmg/content/dam/kpmg/xx/pdf/2019/01/kpmg-venture-pulse-q4-2018.pdf. 
EPSC, “EU Industrial Policy After Siemens-Alstom, Finding a New Balance Between Openness and Protection”, March 2019,  
https://ec.europa.eu/epsc/sites/epsc/files/epsc_industrial-policy.pdf. 

46 

 
 
 
 
 
                                                           
 
CONCLUSION: KEY TAKEAWAYS 

Europe has set its overarching ambition on a human-centric approach to Artificial Intelligence. In our first 
deliverable, this concept was captured in the notion of Trustworthy AI, which we characterised in terms of 
three components – being lawful, ethical and robust  – and in line with the core tenets of the European 
Union: fundamental rights, democracy and the rule of law. Our Ethics Guidelines for Trustworthy AI hence 
constituted a crucial first step in delineating the type of AI that we want and do not want for Europe.  

While such delineation is necessary, it is however not enough to ensure that Europe can also materialise 
the beneficial impact that AI can generate. Taking the next step, this document therefore presents a set of 
policy  and  investment  recommendations  on  how  Trustworthy  AI  can  actually  be  developed,  deployed, 
fostered and scaled in Europe, all the while maximising its benefits whilst minimising and preventing its 
risks.  

We recall that Trustworthy AI is not an end itself, but can be a means to enhance individual and societal 
well-being.  This  requires  sustainability,  in  order  to  safeguard  our  societal  and  natural  environment  for 
generations to come. It requires growth and competitiveness, so as to grow the pie, secure employment 
opportunities  and  generate  beneficial  progress.  And  it  requires  inclusion,  to  allow  everyone  to  benefit 
therefrom.  Technology  is  a  crucial  driver  of  innovation  and  productivity,  and  AI  is  one  of  the  most 
transformative technologies of our time. We believe that Trustworthy AI can help achieving sustainability, 
growth and competitiveness, and inclusion – thus contributing to individual and societal well-being. 

For  this  purpose,  we  formulated  33  concrete  recommendations  addressed  to  the  European  Institutions 
and Member States. These focus on four main areas where we believe Trustworthy AI can help achieving a 
beneficial impact, starting with humans and society at large, and continuing then to focus on the private 
sector, the public sector and Europe’s research and academia. At the same time, we looked into the main 
enablers  needed  to  facilitate  those  impacts,  evolving  around  the  availability  of  data  and  infrastructure, 
skills and education, appropriate governance and regulation, as well as funding and investment.  

We list here below the main takeaways from our report: 

1.  Empower and protect humans and society 

Using Trustworthy AI to enhance our well-being implies important prerequisites, in particular securing 
individual  and  societal  empowerment  and  protection.  First,  individuals  need  to  be  aware  of  and 
understand  the  capabilities,  limitations  and  impacts  of  AI.  Second,  they  must  have  the  necessary 
education and skills to use the technology, to ensure that they can truly benefit therefrom as well as 
being  prepared  for  a  transformed  working  environment  where  AI  systems  will  become  ever  more 
prevalent. And third, they need adequate safeguards from any adverse impact that AI might bring. 

2.  Take up a tailored approach to the AI landscape 

When  positioning  Europe  in  the  AI  landscape,  policy-makers  must  adopt  a  tailored  approach.  They 
should consider  the  “big picture”, by looking at AI’s overall impact  on  –  and potential for  –  society, 
while  simultaneously  understanding  the  sensitivities  of  AI  solutions  in  B2C,  B2B  and  P2C  contexts, 
both as digital products and services only, and as digital solutions embedded in physical systems. The 
underlying logic of each of those segments across different sectors should be analysed and harnessed, 
both in terms of impacts to be made and the enablers needed, including governance measures.  

3.  Secure a Single European Market for Trustworthy AI 

A major achievement in Europe over the last few decades has been the creation of the Single Market, 
which – as we move into the new economic and technological wave that is created by AI – must also 

47 

 
 
focus  on  a  Single  European  Market  for  AI.  This  is  a  complex  and  multifaceted  undertaking  which 
includes the avoidance of market fragmentation, for instance through the harmonisation of legislation 
where appropriate, while at the same time maintaining a high level of protection of individuals’ rights 
and  freedoms  across  all  Member  States.  Establishing  a  level  playing  field  for  Trustworthy  AI  across 
Europe can benefit individuals and organisations by removing barriers to procure lawful, ethical and 
robust AI-enabled goods and services, while also ensuring a competitive position on the global market 
through economies of scale enabled by large integrated markets.  

4.  Enable AI ecosystems through Sectoral Multi-Stakeholder Alliances  

Implementing  the  recommendations  put  forward 
in  this  document  necessitates  stakeholder 
cooperation. It is only by joining forces and bringing all relevant actors – from civil society, industry, 
the  public  sector  and  research  and  academia  –  around  the  table,  that  we  can  make  a  difference. 
Together, these can help giving shape to vibrant AI ecosystems on a sectoral basis, which can enable a 
more granular and much-needed analysis of the specific needs, challenges and opportunities to seize 
the benefits of AI within those respective sectors. We propose to start laying the groundwork for this 
in the second half of 2019, building on the current report. 

5.  Foster the European data economy  

Europe  must  foster  and  invest  in  a  European  data  economy.  The  data-driven  economy  is  a 
cornerstone for the EU’s future success in the global market. A whole set of policy actions are needed 
for  European  organisations  to  generate  societal  benefits  and  succeeding  in  global  competition, 
including provisions for data access, data sharing, use of data, re-use of data and data interoperability, 
all  the  while  ensuring  high  privacy  and  data  protection  standards  for  individuals.  This  also  requires 
putting in place the necessary (physical) infrastructures to enable the other building blocks needed to 
develop and deploy Trustworthy AI in Europe.  

6.  Exploit the multi-faceted role of the public sector  

The  public  sector  plays  a  vital  role  in  steering  Europe’s  future.  It  is  uniquely  placed  to  deliver  and 
promote  human-centric  and  Trustworthy  AI  services,  leading  by  example,  while  ensuring  a  strong 
protection of fundamental rights. Public procurement-based innovation provides a great opportunity 
not  only  to  incentivise  the  development of  novel  AI solutions  that  can optimise  public  services,  but 
also  to  foster  Trustworthy  AI  solutions  amongst  European  companies  of  all  sizes,  and  to  create 
beneficial solutions in their own right for application elsewhere. Finally, the public sector is in a crucial 
position to bring all stakeholders together and maximise joint efforts towards our overarching aims. 

7.  Strengthen and unite Europe’s research capabilities 

Europe must focus the power of its distributed research  landscape to establish and demonstrate its 
intellectual  and  commercial  leadership  in  AI.  It  should  strengthen  and  create  additional  Centres  of 
Excellence  in  AI,  and  foster  collaboration  with  other  stakeholders,  including  small  and  large 
companies, the public sector, as well as society at large. An ambitious research roadmap for AI should 
be developed, which includes grand challenges of global relevance, respects and fosters Trustworthy 
AI,  and  substantially  impacts  human-centric  application  domains.  It  should  focus  on  Europe's 
strengths, opportunities and areas of strategic importance, and be constantly updated based on new 
developments and  stakeholder  consultation.  Research  funding  should  be  increased  and  streamlined 
to build a unified AI capacity for Europe, which will ensure world-class research capabilities that can 
also help develop, retain and acquire AI talent. 

48 

 
 
 
8.  Nurture education to the Fourth Power  

A sufficiently deep and wide skills base is needed to support our Trustworthy AI ambitions. This starts 
with raising awareness and providing education on AI’s capabilities, challenges and limitations, as well 
as  teaching  appropriate  skills  to  deal  with  this,  whilst  ensuring  an  inter-  and  multidisciplinary 
perspective.  Primary  (1),  secondary  (2)  and  tertiary  (3)  education  models  need  to  take  this  into 
consideration, and continuous learning (4) – including on-job learning – must secure the re- and up-
skilling of individuals for the new digital era in Europe, establishing a work-life-train balance. 

9.  Adopt a risk-based governance approach to AI and an ensure an appropriate regulatory framework 

Ensuring Trustworthy AI requires an appropriate governance and regulatory framework. We advocate 
a  risk-based  approach  that  is  focused  on  proportionate  yet  effective  action  to  safeguard  AI  that  is 
lawful,  ethical  and  robust,  and  fully  aligned  with  fundamental  rights.  A  comprehensive  mapping  of 
relevant EU laws should be undertaken so as to assess the extent to which these laws are still fit for 
purpose  in  an  AI-driven  world.  In  addition,  new  legal  measures  and  governance  mechanisms  may 
need  to  be  put  in  place  to  ensure  adequate  protection  from  adverse  impacts  as  well  as  enabling 
proper enforcement and oversight, without stifling beneficial innovation.  

10. Stimulate an open and lucrative investment environment 

Europe  must  harness  its  lucrative  investment  environment.  The  new  Horizon  Europe  and  Digital 
Europe  programmes  set  firm  steps  towards  enhanced  European  investment  levels  in  AI,  yet  much 
more needs to be done on the public side, and real successes can only be achieved through significant 
private  sector  support.  We  believe  that  sectoral  multi-stakeholder  alliances  that  foster  trust  across 
policy-makers, industry and academia as well as society, can help securing those investments, as well 
as steering them towards Trustworthy AI.  

11. Embrace a holistic way of working, combing a 10-year vision with a rolling action plan  

To achieve these goals, Europe needs a holistic strategy with a long-term vision that can capture the 
opportunities  and  challenges  of  AI  for  the  next 10 years.  At  the  same  time,  a framework  is  needed 
that allows  continuous monitoring of the landscape and adapting impactful actions on a short-term 
rolling basis. In this regard, the yearly update of the Commission’s and Member States' Coordinated 
plan  for  AI  is  a  welcome  development  and  should  be  secured.  In  a  competition  amongst  different 
economic  entities,  the  single  most  important  element  is  the  capability  to  apply  and  learn  fast  and 
consistently  over  a  long  period  of  time.  This  report  on  Policy  and  Investment  Recommendations, 
together  with  earlier  published  Ethical  Guidelines  on  Trustworthy  AI,  is  a  first  step  to  establish  the 
for  their 
for  this  durable  strategy.  However,  a 
foundation 
implementation is crucial if we truly wish to move the needle in Europe.  

long-term  follow-up  scheme 

As concrete next steps to pave the road for this new way of working, in the second half of 2019 we will (1) 
run a piloting phase of the Ethics Guidelines for Trustworthy AI to enable their improvement and secure 
their  (sectoral)  relevance,  and  we  propose  to  (2)  instigate  a  limited  number  of  sectorial  AI  ecosystem 
analyses. Building on this report’s cross-sectoral recommendations, we believe it is now necessary to learn 
which and impactful actions should be undertaken for various strategic sectors, covering all the areas of 
impact and the enablers mentioned in this report.  

A major opportunity is knocking on Europe’s door. That opportunity is AI-enabled. Europe’s readiness to 
respond to this opportunity must be ensured, which requires action now. We wish to convey a sense of 
urgency to policy-makers both at European and national level to gain momentum in applying Trustworthy 
AI for the benefit of individuals and societies in Europe.  

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This Document was prepared by the members of the High-Level Expert Group on AI  

listed here below in alphabetical order 

Pekka Ala-Pietilä, Chair of the AI HLEG 
     AI Finland, Huhtamaki, Sanoma 
Wilhelm Bauer 
     Fraunhofer 
Urs Bergmann  
     Zalando 
Mária Bieliková – Co-Rapporteur 
     Slovak University of Technology in Bratislava 
Cecilia Bonefeld-Dahl – Co-Rapporteur 
     DIGITALEUROPE 
Yann Bonnet 
     ANSSI 
Loubna Bouarfa – Co-Rapporteur 
     OKRA 
Stéphan Brunessaux 
     Airbus 
Raja Chatila 
     IEEE Initiative Ethics of Intelligent/Autonomous Systems &   

Sorbonne University 

Mark Coeckelbergh 
     University of Vienna 
Virginia Dignum – Co-Rapporteur 
     Umea University 
Luciano Floridi 
     University of Oxford 
Jean-Francois Gagné  
     Element AI 
Chiara Giovannini 
     ANEC 
Joanna Goodey 
     Fundamental Rights Agency 
Sami Haddadin – Co-Rapporteur 
     MSRM, TUM 
Gry Hasselbalch 
     The thinkdotank DataEthics & University of Copenhagen 
Fredrik Heintz – Co-Rapporteur 
     Linköping University 
Fanny Hidvegi 
     Access Now 
Eric Hilgendorf 
     University of Würzburg 
Klaus Höckner 
     Hilfsgemeinschaft der Blinden und Sehschwachen 
Mari-Noëlle Jégo-Laveissière 
     Orange 
Leo Kärkkäinen – Co-Rapporteur 
     Nokia Bell Labs 
Sabine Theresia Köszegi – Co-Rapporteur 
     TU Wien  
Robert Kroplewski 
     Solicitor & Advisor to Polish Government 
Elisabeth Ling 
     RELX 

Pierre Lucas 
     Orgalim – Europe’s technology industries 
Ieva Martinkenaite – Co-Rapporteur 
     Telenor 
Thomas Metzinger      
     JGU Mainz & European University Association 
Catelijne Muller – Co-Rapporteur 
     ALLAI Netherlands & EESC 
Markus Noga 
     SAP 
Barry O’Sullivan, Vice-Chair of the AI HLEG  
     University College Cork  
Ursula Pachl – Co-Rapporteur 
     BEUC 
Nicolas Petit  
     University of Liège  
Christoph Peylo 
     Bosch 

Iris Plöger 
     BDI 
Stefano Quintarelli 
     Garden Ventures 
Andrea Renda – Co-Rapporteur 
     College of Europe Faculty & CEPS 
Francesca Rossi 
    IBM  
Cristina San José 
     European Banking Federation 
George Sharkov 
     Digital SME Alliance 
Philipp Slusallek 
     German Research Centre for AI (DFKI) 
Françoise Soulié Fogelman – Co-Rapporteur 
     AI Consultant 
Saskia Steinacker  
     Bayer 
Jaan Tallinn 
     Ambient Sound Investment 
Thierry Tingaud 
     STMicroelectronics 
Jakob Uszkoreit 
     Google 
Aimee Van Wynsberghe  
     TU Delft  
Thiébaut Weber – Co-Rapporteur 
     ETUC 
Cecile Wendling 
     AXA 
Karen Yeung – Co-Rapporteur 
     The University of Birmingham 

Mária Bieliková, Cecilia Bonefeld-Dahl, Loubna Bouarfa, Virginia Dignum, Sami Haddadin, Fredrik Heintz, Leo Kärkkäinen, Sabine 
Köszegi, Ieva Martinkenaite, Catelijne Muller, Markus Noga, Ursula Pachl, Andrea Renda, Françoise Soulié Fogelman, Thiébaut Weber 
and Karen Yeung acted as co-rapporteurs for this document.  

Pekka Ala-Pietilä is Chairing the AI HLEG. Barry O'Sullivan is Vice-Chair, coordinating the work on this document. Nozha Boujemaa, 
Vice-Chair until 1 February 2019 coordinating the AI HLEG’s first deliverable, also contributed to the content of this document.  

Nathalie Smuha coordinates the AI HLEG and provided editorial support.  

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