Digitalisation, artificial intelligence and algorithmic management in the workplace : shaping the future of work : cost of non-Europe.
Date: 2025-10-27
Institutions: Directorate-General for Parliamentary Research Services, Smilari, Alessandro, Tobback, Ilse
Subjects: EU law, artificial intelligence, digitisation, fundamental rights, labour relations, principle of legal certainty, productivity, regulatory policy, social well-being, working conditions, workplace

Digitalisation, 
artificial 
intelligence and 
algorithmic 
management in 
the workplace: 
Shaping the 
future of work 

Cost of  
non-Europe 

STUDY 

EPRS | European Parliamentary Research Service 

European Added Value Unit 
PE 774.670 – October 2025 

Digitalisation, artificial 
intelligence and 
algorithmic management 
in the workplace: Shaping 
the future of work 

Cost of non-Europe 

This study investigates the integration and effects of artificial intelligence 
and  algorithmic  management  (AM)  technologies  within  European 
workplaces,  extending  beyond  the  realm  of  platform  work.  It  thoroughly 
examines  the  European  regulatory  framework  for  the  use  of  AM 
technologies,  engages  in  stakeholder  interviews,  and  analyses  four  case 
studies.  

The study estimates that workers' exposure to AM could rise from 42.3 % to 
55.5 % in the medium term, generating both productivity opportunities and 
challenges  in  employment  relations,  working  conditions,  and  workers' 
wellbeing. It identifies key regulatory gaps in AM use at work and explores 
the role EU legislation could play in addressing the gaps. It develops three 
policy  options:  adopting  a  recommendation;  amending  a  package  of 
existing legislation; and adopting a new legislative instrument to regulate 
the use of AM.  

The added value of acting at EU level has been identified in ensuring the 
same level of protection for all workers, improving working conditions and 
safeguarding fundamental rights. For companies, having a common set of 
rules would enhance the level playing field and increase legal certainty. 

EPRS | European Parliamentary Research Service 

 
 
 
AUTHORS 

This  study  has  been  written  by  Barbara  De  Micheli  [Fondazione  Giacomo  Brodolini],  Giancarlo  Dente 
[Fondazione Giacomo Brodolini], Michele Faioli [Fondazione Giacomo Brodolini and Università Cattolica del 
Sacro Cuore], Alessandro Smilari [Fondazione Giacomo Brodolini], Ela Omersa [Milieu], Sophie Patras [Milieu], 
Martina Ravaglia [Milieu], Sophie Vancauwenbergh [Milieu], Mikkel Barslund [KU Leuven], Ilse Tobback [KU 
Leuven]  at  the  request  of  the  European  Added  Value  Unit  of  the  Directorate  for  Impact  Assessment  and 
Foresight, within the Directorate-General for Parliamentary Research Services (EPRS) of the Secretariat of the 
European Parliament. 

The  authors  would  like  to  thank  Eurofound  and  the  European  Agency  for  Safety  and  Health  for  their  kind 
collaboration on this study through early access to their survey data.  

ADMINISTRATORS RESPONSIBLE 

Lenka Jančová, Margarida Arenga Albuquerque and Filippo Sinicato, European Added Value Unit, European 
Parliamentary Research Service. 

To contact the publisher, please e-mail eprs-europeanaddedvalue@europarl.europa.eu.  

LINGUISTIC VERSIONS 

Original: EN 

Manuscript completed in October 2025.  

DISCLAIMER AND COPYRIGHT 

This  document  is  prepared  for,  and  addressed  to,  the  Members  and  staff  of  the  European  Parliament  as 
background  material  to  assist  them  in  their  parliamentary  work.  The  content  of  the  document  is  the  sole 
responsibility of its authors and any opinions expressed herein should not be taken to represent an official 
position of the Parliament. 

Reproduction  and  translation  for  non-commercial  purposes  are  authorised,  provided  the  source  is 
acknowledged and the European Parliament is given prior notice and sent a copy. 

Brussels © European Union, 2025. 

Photo credits: © Andrey Popov / Adobe Stock. 

PE 774.670 
ISBN: 978-92-848-3082-4 
DOI: 10.2861/0136788 
CAT: QA-01-25-226-EN-N 

eprs@ep.europa.eu  
http://www.eprs.ep.parl.union.eu (intranet) 
http://www.europarl.europa.eu/thinktank (internet) 
http://epthinktank.eu (blog) 

 
 
 
 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work  

Glossary 

Acronym 

Meaning 

AI 

AI Act 

AIA 

AM 

CEC 

CJEU 

DGQ 

DPIA 

EC 

EDPB 

EESC 

ESENER 

EU 

Artificial intelligence 

Artificial Intelligence Act (proposed EU Regulation) 

Algorithmic impact assessment 

Algorithmic management 

Confédération Européenne des Cadres 

Court of Justice of the European Union 

German Association for Quality 

Data Protection Impact Assessment 

European Commission 

European Data Protection Board 

European Economic and Social Committee 

European Survey of Enterprises on New and Emerging Risks 

European Union 

EU-OSHA /OSHA 

European Agency for Safety and Health at Work 

EWCS 

FRIA 

GDPR 

GenAI 

GPAI 

HR 

HRM 

IAO 

ILO 

IPA 

OECD 

OJ 

OSH 

PWD 

TFEU 

European working condition survey 

Fundamental Rights Impact Assessments 

General Data Protection Regulation (EU 2016/679) 

Generative AI 

General-purpose AI (Model) 

Human resources 

Human resources management 

Fraunhofer Institute for Industrial Engineering 

International Labour Office 

Fraunhofer Institute for Manufacturing Engineering and Automation 

Organisation for Economic Co-operation and Development 

Official Journal 

Occupational safety and health  

Platform Work Directive (COM/2021/762) 

Treaty on the Functioning of the European Union 

I 

 
 
 
EPRS | European Parliamentary Research Service 

Executive summary 

This study is the result of a research commissioned to support the European Parliament's Committee 
on  Employment  and  Social  Affairs  (EMPL)  in  preparing  a  legislative  own-initiative  report  on 
digitalisation,  artificial  intelligence  (AI)  and  algorithmic  management  (AM)  in  the  workplace.  The 
study reviews the use and impact of AI and AM systems on employment  relations and identifies 
policy options to address emerging challenges relating to the use of AM in the workplace. The study 
methodology  has  relied  on  both  desk  research  and  stakeholder  interviews  to  provide  evidence-
based input for future EU legislative action.  

The study examines the deployment of AI and AM technologies across European workplaces. The 
integration  of  automation  and  AI  into  workplaces  is  transforming  managerial  practices,  notably 
through AM, which automates core functions such as giving instructions, monitoring and evaluating 
workers. Once associated mainly with platform work, AM is now spreading widely across traditional 
sectors in the European Union.  

Prevalence of algorithmic management (AM) 

Most  current  AM  applications  assist  or  augment  human  managers  rather  than  fully  automate 
decisions, but often increase work intensity despite oversight. Even digital tools not designed for 
management may de facto function as AM.  

EU-wide surveys reveal inconsistencies in the measurement of the adoption of AM. According to the 
2024 European Working Condition Survey (EWCS), 42.3 % of EU workers are affected by AM, with 
country variation, from 27 % in Greece to 70% in Denmark. Eurobarometer reports higher averages 
(51.3 %), while the 2024 European Survey of Enterprises on New and Emerging Risks estimates only 
14.5 % of firms use AM. The OECD survey shows even higher shares due to broader definitions. The 
above  surveys  differ  in  unit  of  observation,  wording  of  questions,  items  covered  and  targeted 
respondent, and should be regarded with caution. 

According  to  surveys,  AM  adoption  patterns  vary  by  firm  size  and  worker  characteristics.  Large 
companies adopt AM more often, and full-time, permanent, and highly educated staff are more likely 
to be exposed to AM. Gender differences are modest but vary by country. Additionally, unionised 
workplaces often show higher AM exposure. At sector level, finance and transport report the highest 
prevalence of AM usage, while agriculture and construction show the lowest.  

Health and safety implications 

In general, health and safety implications of AI and AM present a complex scenario: while AI can 
enhance  physical  safety  by  performing  hazardous  tasks  or  facilitate  the  adoption  of  preventive 
measures,  AM  engenders  considerable  psychosocial  risks.  Continuous  monitoring,  performance 
pressure from automated metrics, and diminished human oversight may contribute to heightened 
stress,  anxiety,  and  burnout  risk.  Notably,  27 %  of  managers  surveyed  by  the  OECD  expressed 
concerns about insufficient protection of workers' physical and mental wellbeing. 

Impact assessment modelling shows that overall AM exposure could rise from 42.3 % to 55.5 % EU-
wide in the medium term. Statistical modelling suggests that such an increase in worker exposure 
to AM, without policy intervention, results in a slight increase in worker stress levels and measurable 
deterioration in work-life balance, autonomy, and rest periods. 

II 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Broader challenges related to AM and AI 

The  change  driven  by  AI  and  AM  presents  both  productivity  opportunities  and  risks  to  worker 
autonomy,  wellbeing  and  fundamental  rights.  While  some  technologies  can  enhance  worker 
autonomy by alleviating demanding tasks, others may diminish discretion through strict instructions 
and  continuous  oversight.  Stakeholder  interviews  validate  these  findings  while  highlighting 
emerging concerns about emotional surveillance tools; respondents emphasised that successful AM 
implementation  requires  early  worker  engagement,  transparent  system  design,  and  meaningful 
human oversight, with training and consultation identified as essential elements. 

From  a  regulatory  perspective,  the  analysis  shows  that,  although  the  EU  has  established  a 
comprehensive  digital  acquis  that  alongside  existing  labour  and  occupational  health  and  safety 
(OSH)  legislation  can  be  applied  to  AM,  the  unique  characteristics  of  workplace  organisation 
generate specific challenges that remain insufficiently addressed.  

Several  legal  instruments  already  provide  important  safeguards.  The  General  Data  Protection 
Regulation  (GDPR)  guarantees  robust  data  protection  rights  and  restricts  fully  automated 
decisionmaking. However, its reliance on individual consent and its lack of collective dimension can 
be  problematic  in  employment  contexts.  The  EU  Artificial  Intelligence  Act  (AI  Act)  introduces 
transparency,  human  oversight  and  conformity  assessment  obligations  for  high-risk  AI  systems. 
However,  its  market-oriented  design  overlooks  employment  power  imbalances  and  collective 
dimensions.  The  OSH  acquis  imposes  binding  duties  on  employers  to  ensure  safe  working 
environments,  including  protection  against  psychosocial  risks  arising  from  monitoring  and 
algorithmic scheduling. However, the legal framework does not contain AM-specific duties. These 
challenges  have  partially  been  addressed  by  the  Platform  Work  Directive  (PWD).  This  Directive 
offers workplace-specific protections, but it remains limited to persons performing platform work, 
leaving  the  majority  of  the  EU  workforce  without  equivalent  safeguards.  Together,  these 
instruments provide a baseline of protection, but they do not form a coherent, workplace-specific 
framework. 

The regulatory mapping highlighted some key gaps (more details in Table 7): 

•  Uneven personal coverage: EU labour and OSH acquis generally apply to 'workers', a 
term  which  is  defined  differently  by  each  Member  State.  The  PWD  addresses  this 
issue in platform work, but its protections do not extend to the wider workforce. 
•  Transparency,  information  and  consultation  rights:  While  existing  labour  rules 
include generic duties to inform and consult, they do not clearly require disclosure 
about the deployment of AM systems and small undertakings are exempt from general 
information and consultation obligations. PWD and the AI Act introduce transparency 
obligations, with the former containing more detailed provisions, including a collective 
dimension. 

•  Specific AM safeguards: Although human oversight, explanation rights and impact 
assessments are embedded across instruments, they are fragmented and limited in 
scope. For example, GDPR Data Protection Impact Assessments (DPIA) only apply to 
companies above certain thresholds, AI Act Fundamental Rights Impact Assessments 
(FRIA) largely exclude private employers, and detailed safeguards (such as the right 
to human oversight, explanation and human review) are confined to platform work. 

III 

EPRS | European Parliamentary Research Service 

•  Organisation  of  working  time:  Despite  the  universal  application  of  core  labour 
standards on working time and rest periods, there are no explicit protections against 
algorithmic scheduling, pace-setting or surveillance outside the PWD.  

•  Data protection and privacy in the workplace: The EU digital acquis provides general 
rules  and  guarantees  for  data  governance  and  the  protection  of  personal  data. 
Specific protective mechanisms and risks identified in workplace contexts are in place 
only for platform work (PWD) or have been set out in national legislation. 

•  Bias and discrimination: EU equality law prohibits discrimination based on specific 
grounds (e.g. sex, age, ethnicity), but provides no safeguards against algorithmic bias 
related to socioeconomic status or employment type. The AI Act does not impose on 
employers any ongoing duties to detect bias. The PWD does so, but only for persons 
performing platform work. 

•  Health and safety: In principle, the OSH framework can cover psychosocial risks, but 
it contains no AI-specific duties, such as addressing AI-driven surveillance or pace-
setting. The PWD has established more specific protection measures but is limited in 
its personal scope. 

•  Social dialogue: AM deployment often proceeds without adequate consultation with 
workers, except in the case of platform work. Consultation triggers under EU labour 
law do not explicitly cover the introduction of AM systems. 

•  Unsymmetrical enforcement and remedies: Enforcement is fragmented across data 
protection, market surveillance, labour and OSH authorities, with little coordination. 
Remedies  are  primarily  individualised,  and  collective  redress  is  limited  outside  the 
platform  economy.  This  leaves  workers  without  effective  means  of challenging  AM 
practices. 

Policy options 

The study develops and evaluates three policy options (relative to the baseline) that would address 
the challenges relating to the use of AM in the workplace: 

•  Adopting a non-binding Council recommendation (policy option 1) 
•  Amending a package of labour and OSH instruments (policy option 2) 
•  Adopting  a  targeted  legislative  initiative  on  algorithmic  management  (policy 

option 3). 

It finds that, soft-law approach (policy option 1) offers minimal worker protection improvements 
and  reduced  legal  certainty.  As  Member  States  can  decide  whether  to  follow  up  on  the 
recommendation by adopting a non-binding or binding measure, impacts would differ. Adopting a 
non-legislative measure could encourage some actors to implement protective measures, but the 
protection would be uneven, especially for workers in smaller businesses. In case of a legislative 
measure,  diverging  protection  across  the  EU  also  persists  as  different  countries  might  adopt 
different measures, addressing different sets of challenges. 

Policy option 2 would entail amending a package of existing legislation such as the OSH Framework 
Directive, the Information and Consultation Directive, the Working Time Directive, the Directive on 
Transparent and Predictable Working Conditions and finally, the Platform Work Directive (currently 
under  transposition).  Such  amendments  would  entail  legal  complexity,  as  well  as  introducing 
uncertainty and compliance costs, not only for companies deploying AM, but also for third parties 

IV 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

(not involved in AM), due to changes in long-standing legislation. Amending existing legislation may 
also  fail  to  regulate  AM  proportionally  to  its  impact  in  different  use  cases,  potentially  negatively 
affecting SMEs. 

A new legislative initiative on AM (policy option 3) could address all the above concerns. While both 
policy options 2 and 3 have the potential to cover most or all identified gaps and decrease negative 
impacts on workers, option 3 could be better targeting the AM use in the workplace without risking 
affecting  third  parties  not  involved  in  AM.  Policy  option  3  offers  the  highest  increase  of  legal 
certainty for companies across the EU. Similar to policy option 2, compliance costs will occur but 
could be mitigated to a certain extent, for example by avoiding the need to adapt to diverging rules 
across  the  EU.  This  approach  would  likely  be  more  proportional  and  incur  lower  costs  for  public 
administration and companies compared to option 2. For more details on the assessment, see Table 
10 in section 4.2.4.  

Responsible AM use in the workplace: balancing opportunities and risks 

The labour market changes driven by AI and AM present both productivity opportunities and risks 
to worker autonomy, wellbeing, and fundamental rights. The use of AM could increase productivity 
(e.g.  through  streamlining  processes),  efficiency  (e.g.  through  an  automatic  scheduling,  for 
example)  and  enhance  compliance  with  labour  law  (e.g.  through  enforcing  working  time 
requirements). While some technologies can enhance worker autonomy by alleviating demanding 
tasks, others may diminish discretion through strict instructions and continuous oversight. Realising 
benefits while minimising drawbacks requires a human-centred approach incorporating robust data 
protection measures, worker consultation, and collective bargaining to ensure ethical deployment 
of these technologies across European workplaces.  

European added value  

Not acting at EU level would negatively impact both workers and companies as the use of AM is 
expected to increase. Workers will likely experience worsening of some negative impacts relating to 
AM  use  such  as  increased  stress  levels  or  a  loss  of  autonomy.  Some  Member  States  have  been 
already  taking  action  to  address  the  risks  relating  to  the  use  of  AM  but  their  approaches  differ. 
Diverging national rules could contribute to additional fragmentation of the single market and imply 
additional  compliance  costs  for  companies.  Such  costs  would  mostly  impact  companies  doing 
business across several Member States and small and medium enterprises.  

Acting at EU level would therefore bring benefits compared to action at national level. The added 
value of acting at EU level has been identified in ensuring the same level of protection for all workers, 
improving their working conditions and fundamental rights (such as privacy and mental health) on 
the one hand and enhancing level playing field and increased legal certainty for companies on the 
other hand.  

V 

 
 
EPRS | European Parliamentary Research Service 

Table of contents 

1. Task 1 – Use of AM in the workplace ________________________________________________  1 

1.1. Framework, definitions and literature review ______________________________________  1 

1.2. Types of AM, diffusion in Europe, AM–humans interactions, challenges and future _______  4 

2. Task 2 – Impact on employment relations  __________________________________________  13 

2.1. Methodological approach  ____________________________________________________  13 

2.2. AM impacts on the workplaces ________________________________________________  14 

2.3. AM and interviews results, validation of the approach and future trends  ______________  21 

2.4. Analysis of case studies  _____________________________________________________  23 

2.4.1. Case studies _____________________________________________________________________ 24 

3. Task 3 – Regulation of AI and AM  _________________________________________________  31 

3.1. Regulatory framework applicable to AI and AM in the workplace _____________________ 32 

3.1.1. EU digital acquis __________________________________________________________________ 32 

3.1.2. EU labour and OSH acquis  _________________________________________________________ 39 

3.1.3. Platform Work Directive (PWD) _____________________________________________________ 42 

3.2. Key provisions and mechanisms to protect workers related to AM in the workplace _____ 44 

3.2.1. Information provisions and transparency ______________________________________________ 44 

3.2.2. Specific safeguards for AM (e.g. human oversight, right to explanation, ex-ante assessment and 
continuous monitoring) _________________________________________________________________ 49 

3.2.3. Organisation of working time _______________________________________________________ 54 

3.2.4. Data protection and privacy ________________________________________________________ 56 

3.2.5. Bias and discrimination ____________________________________________________________ 59 

3.2.6. Health and safety  ________________________________________________________________ 61 

3.2.7. Social dialogue (e.g. collective bargaining, social partners agreements) ____________________ 63 

3.2.8. Redress (individual and collective) and enforcement  ___________________________________ 67 

3.3. Conclusion ________________________________________________________________ 69 

4. Task 4 – Policy options and impact assessment ______________________________________  81 

4.1. Policy options ______________________________________________________________  81 

4.1.1. Option 1 – Recommendation and guidance to Member States  ____________________________ 81 

4.1.2. Option 2 – Amending existing directives to address legislative gaps _______________________ 82 

4.1.3. Option 3 – New legislative directive  _________________________________________________ 83 

VI 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

4.1.4. Pros and cons of the different policy options __________________________________________ 84 

4.2. Impact assessment _________________________________________________________  87 

4.2.1. Defining the baseline – no additional EU legislation _____________________________________ 87 

4.2.2. Impact of policy option 1 ___________________________________________________________ 89 

4.2.3. Impact of policy option 2  __________________________________________________________ 92 

4.2.4. Impact of policy option 3  __________________________________________________________ 94 

4.2.5. Summary of impacts ______________________________________________________________ 97 

REFERENCES __________________________________________________________________  102 

ANNEX A – ALGORITHMIC MANAGEMENT DEFINITIONS _______________________________ 110 

ANNEX B – DESCRIPTION OF SURVEY DATA SOURCES ________________________________ 112 

ANNEX C – AM PREVALENCE BY WORKER AND WORKPLACE CHARACTERISTICS _________ 114 

ANNEX D – CONTACTS INTERVIEWED AND INTERVIEWS' STRUCTURE ___________________ 118 

VII 

 
 
 
EPRS | European Parliamentary Research Service 

Table of figures 

Figure C1 – AM exposure by economic activity (EU27, %) ______________________________ 116 

Figure C2 – AM exposure by occupational groups (EU27, %) ___________________________ 116 

Table of tables 

Table 1 – Measure of AM prevalence (%)  ____________________________________________ 8 

Table 2 – Impacts of algorithmic management on workers and workplaces ________________  16 

Table 3 – Dimensional analysis of AM workplace impacts ______________________________  18 

Table 4 – Estimated effects of AM on selected outcomes (EU, EWCS2024) _______________  21 

Table 5 – Overview of the terminology relevant to determine the scope __________________ 35 

Table 6 – Overview of selected EU labour instruments ________________________________ 40 

Table 7 – Overview of the legal assessment _________________________________________ 73 

Table 8 – Pros and cons of different policy options (summary table) _____________________ 86 

Table 9 – Baseline development in impact on workers  ________________________________ 88 

Table 10 – Comparative assessment of policy options for addressing AM in the workplace ___ 98 

Table C1 – AM prevalence among employees by company size (EWCS2024) ______________ 114 

Table C2 – AM prevalence of employees by sex, education and age (EWCS2024) __________ 115 

Table C3 – AM prevalence of employees' additional characteristics (EWCS2024)  __________ 117 

VIII 

 
 
  
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work  

1. Task 1 – Use of AM in the workplace  

1.1. Framework, definitions and literature review 

The increasing integration of artificial intelligence (AI) into the workplace is reshaping traditional 
managerial roles, giving rise to the nuanced concept of algorithmic management (AM). This section 
of the report explores the extent to which AM technologies are used in traditional workplaces across 
the European Union, moving beyond platform work, and aims to design a theoretical framework for 
understanding their widespread use in the workplace. It will specifically investigate the types of tools 
being used, such as instruction, monitoring, or evaluation tools, and the level and nature of human-
robot  interaction,  categorised  by  EU-OSHA  as  co-existence,  cooperation,  and  collaboration.  The 
literature sources for this desk review include publications from various EU-level bodies, such as the 
European Commission,  Eurofound,  Cedefop,  EU-OSHA,  and  the  European  Parliament,  as  well  as 
international organisations like the ILO and OECD. Additionally, the review draws upon academic 
research, including peer-reviewed journal articles and prominent working paper series, and various 
sector-specific reports. As a starting point we recall a preliminary overview of key definitions1:  

•  An  AI  system  is  defined  as  a  machine-based  system  that,  for  explicit  or  implicit 
objectives, infers from its input how to generate outputs such as predictions, content, 
recommendations,  or  decisions  that  can  influence  physical  or  virtual  environments. 
This definition is aligned with the final version adopted in the EU Artificial Intelligence 
Act (AI Act)2 . AI systems are designed to operate with varying levels of autonomy 
and may exhibit adaptiveness after deployment. 

•  Algorithms  are  automated  instructions  for  performing  tasks,  ranging  from  simple 
'if...then' statements to complex mathematical equations. They are the building blocks 
of  AI  and  facilitate  machine  learning,  allowing  digital  systems  to  learn  through 
experience  and  autonomously  improve  task  performance  over  time.  Eurofound 
provides  a  simpler  definition  of  algorithms  as  'a  set  of  precisely  defined  steps  and 
rules to accomplish a task'3. 

•  Generative AI  (GenAI)  models  are  a  category  of  deep-learning  models  trained  on 
extensive datasets that can be directed to generate new content in various formats, 
including  text,  images,  sounds,  and  videos.  The  AI  Act  also  includes  provisions  for 
general-purpose AI (GPAI) models, which are AI models trained with large amounts 
of  data,  displaying  significant  generality,  and capable  of  competently  performing  a 
wide  range  of  distinct  tasks,  and  able  to  be  integrated  into  various  downstream 
systems or applications. Frontier AI is a related but narrower class of AI systems with 
general-purpose functionality, whose capabilities are relatively advanced and novel. 
•  Algorithmic  management  (AM),  is  the  use  of  AI  to  partially  or  fully  automate 
traditional  managerial  functions  like  giving  instructions,  monitoring,  or  evaluating 
workers. This transformative process extends to the automation of core managerial 

1  OECD 2025, ILO 2024, Eurofound 2024. 

2 

3 

Regulation (EU) 2024/1689.  

Eurofound, 2018. 

1 

 
EPRS | European Parliamentary Research Service 

functions, including, but not limited to, the issuance of instructions to the workforce, 
the continuous monitoring of employee activities, and the formal evaluation of their 
performance. This pervasive reorientation of managerial practice is observable across 
a  diverse  spectrum  of  economic  sectors,  exerting  a  significant  impact  on  both 
conventionally  employed 
individuals  and  those  operating  as  self-employed 
professionals. 

While  artificial  intelligence  (AI),  as  a  broader concept,  involves  a set  of  technologies  that  enable 
computers to perform a variety of advanced functions, including the ability to see, understand and 
translate  spoken  and  written  language,  analyse  data,  perform  operational  activities  and  make 
recommendations, thus simulating human intelligence in different tasks and fields. The focus of the 
study is to assess the use of AI impact in management related activities in the workplace, namely on 
algorithmic management (AM). In order to avoid misunderstandings and confusions, in the following 
paragraphs we will use the term AM, implying an implicit reference to involved AI technologies. AI 
will be used only when referring to technologies other than AM having an impact in the workplace 
and when needed to better explain technical implications of AM definition. 

Recent  documents  from  EU-level  bodies,  international  organisations,  and  academic  research 
indicates that AM, is increasingly prevalent beyond the gig economy in conventional workplaces. 
Studies by the OECD, ILO, and Eurofound, show that AM tools are widespread in Europe (average 
79 % of firms surveyed) and the United States (90 %), though less so in Japan (40 %), with varying 
intensity  of  use  across  countries4.  The  functions  automated  by  AM  systems  include  direction, 
evaluation,  and  discipline,  often  described  by 
recommending/restricting, 
recording/rating, and replacing/rewarding. These tools can be specific-purpose or general-purpose 
digital technologies, such as AI-powered route planning or even instant messaging apps, which can 
indirectly influence work organisation. The advent of machine-readable behavioural data, machine 
learning,  and  online  labour  platforms  has  spurred  academic  interest  in  algorithm-driven  human 
resource  management  across  various  domains,  including  staffing,  performance  management, 
compensation, workforce planning, and job design5. This application of HR management algorithms 
is also termed 'algorithmic management,' signifying the use of digital data processed by software 
algorithms to enhance or automate HR management decisionmaking6. 

'six  Rs' 

like 

Nearly a decade has passed since Lee et al.'s (2015) foundational study on Uber's use of algorithms 
for  managing  remote  'gig'  workers.  Since  then,  similar  trends  have  emerged  in  traditional  work 
environments, with 'standard' employees (e.g., full-time, employed) increasingly managed by HRM 
algorithms  with  minimal  human  oversight7.  Consequently,  scholars  have 
investigated  the 
implementation  and  impact  of  HR  management  algorithms  in  diverse  empirical  settings  using 
various conceptual frameworks8. 

4  OECD, 2025; ILO, 2024; Eurofound, 2024. 

5 

Cheng and Hackett, 2021; Leicht-Deobald et al., 2019; Meijerink and Bondarouk, 2023; Strohmeier and Piazza, 2015. 

6  Meijerink et al., 2021. 

Cheng and Hackett, 2021; Jarrahi et al., 2021. 

Kellogg et al., 2020; Leicht-Deobald et al., 2019; Meijerink and Bondarouk, 2023; Parent-Rocheleau and Parker, 2022. 

7 

8 

2 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

These developments have led to the growth of  the  algorithmic management literature in several 
ways:  the  volume  of  scholarly  publications  on  AM  has  surged  in  recent  years  and  has  evolved 
conceptually;  scholars  have  drawn  upon  theories  such  as  labour  process  theory  and  the  job 
demands-resources  model  to  elaborate  on  how  AM  embody  rational  control9,  normative  control 
mechanisms10, or function as work designers11. The maturity of this field is evident in review studies12 
that synthesise empirical findings on algorithmic management and its consequences.  

These  reviews  highlight  the  impact  of  algorithmic management  on  worker-related  outcomes  like 
autonomy13, personal integrity14 , dignity15, and job quality16. For a chronological overview over the 
last 10 years of the definition of algorithmic management, see Annex A. 

Within  this  rapidly  evolving  operational  landscape, AM  emerges  as  a  'third  element'  that  actively 
engages with both workers and employers, fundamentally reshaping, as we will see, the dynamics 
of  the  employment  relationship17.  This  emergent  digital  intermediary  possesses  the  capacity  to 
influence  the  very  definition  of  contractual  terms,  the  systematic  assignment  of  tasks,  and  the 
intricate  processes  of  dispute  resolution,  often  interacting  synergistically  with  pre-existing  legal 
frameworks. Given that AM frequently exercise powers that closely mirror those traditionally vested 
in the employer, a rigorous academic inquiry into this phenomenon necessitates a simultaneous and 
integrated consideration of the AI legal framework at the European Union level, the broader labour 
legal  framework  also  at  the  EU  level,  and  the  specific  domestic  labour  orders  prevalent  across 
Member States (as shown in Task 3). 

AM  involves  replacing  management  functions  with  machines  that  exercise  at  least  some  level  of 
decisionmaking  authority  over  workers.  This  includes  the  full  or  partial  automation  of  direction, 
evaluation,  and  discipline or  rewards.  It  is  a  diverse  set  of  technological  tools  and  techniques  to 
remotely manage the workforce, relying on data collection and surveillance of workers to enable 
automated or semi-automated decisionmaking. AM concerns the use of digital data for continuously 
tracking and evaluating worker behaviour and performance, and the automatic implementation of 
algorithmic  decisions.  It  also  refers  to  the  use  of  computer-programmed  procedures  for  the 
coordination of labour input in an organisation. 

The  degree  to  which  AI  capabilities  enable  AM  can  be  understood  through  different  levels  of 
autonomy and functional sophistication.  

The  following  points  outline  distinct  levels  based  on  AI's  autonomy  and  the  sophistication  of  its 
functions within AM. 

9 

Kellogg et al., 2020. 

10  Galière, 2020.  

11 

Parent-Rocheleau and Parker, 2022. 

12  Cheng and Hackett, 2021; Malik et al., 2022; Parent-Rocheleau and Parker, 2022. 

13  Meijerink and Bondarouk, 2023; Rahman et al., 2023; Shapiro, 2018. 

14 

15 

Leicht-Deobald et al., 2019. 

Lamers et al., 2022. 

16   Goods et al., 2019; Wood et al., 2019. 

17   Faioli, 2025. 

3 

 
EPRS | European Parliamentary Research Service 

AI-assisted management (lower AI autonomy)  

•  Description: AI tools primarily serve to provide data and insights to human managers, 
thereby  supporting  their  decisionmaking  processes  without  directly  automating 
managerial tasks. The human manager retains full interpretative and decisionmaking 
authority.  

•  Example: An AI-powered analytics dashboard displays worker performance metrics, 

which a human manager then interprets and uses to inform their decisions. 

AI-augmented management (medium AI autonomy) 

•  Description:  AI  systems  actively  participate  in  managerial  tasks  by  generating 
proposals,  recommendations,  or  initial  actions.  However,  this  participation  is 
consistently  coupled  with essential  human  oversight  and  the  explicit  possibility  for 
intervention and final approval. The AI acts as a sophisticated collaborator.  

•  Example: An AI scheduling tool proposes optimised work shifts, but a human manager 
reviews and approves the suggested schedule before it is finalised and implemented. 

AI-automated management (higher AI autonomy), the core of AM 

•  Description: AI systems independently perform managerial tasks with minimal or, in 
specific  instances,  no  direct  human  intervention.  This  level  constitutes  the  core  of 
algorithmic management, where algorithms autonomously issue instructions, monitor 
performance, or conduct evaluations. This level is significantly enabled by advanced 
AI capabilities:  

o  Embodied AI: AI agents with a physical presence or operating within physical 
environments (e.g., robots in warehouses) can autonomously manage tasks in 
physical  workspaces,  assigning  duties  to  human  workers,  monitoring  their 
progress via sensors, and providing real-time instructions.  

o  Agentic  AI:  AI  systems  capable  of  pursuing  tasks  independently  of  human 
control  can  autonomously  set  goals  for  teams  or  individuals,  dynamically 
allocate  resources,  and  adjust  strategies  based  on  performance  metrics, 
requiring minimal human oversight.  

o  Frontier AI/General-Purpose AI (GPAI): highly capable AI models trained on 
vast  datasets  and  displaying  significant  generality  across  a  wide  range  of 
tasks.  GPAI  provides  the  advanced  capabilities  necessary  for  complex  and 
sophisticated autonomous managerial functions.  

•  Examples: Automated task allocation in platform work, or AI systems autonomously 
evaluating job applications based on predefined criteria. A GPAI system could analyse 
worker  skills  and  project  needs  to  autonomously  form  optimal  teams,  provide 
personalised feedback, or identify skill gaps for training recommendations. 

1.2. Types  of  AM,  diffusion  in  Europe,  AM–humans  interactions, 
challenges and future  

The widespread adoption of AM technologies in European workplaces is driven by the promise of 
significant  productivity  gains  and  the  automation  of  managerial  functions,  though  it  also  raises 
concerns  about  worker  wellbeing  and  control.  This  phenomenon  is  part  of  the  broader 

4 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

'platformisation  of  work'  or  'data-driven  workplace'  trend,  where  practices  initially  prevalent  in 
digital  labour  platforms  are  expanding  into  conventional  employment  settings  like  warehouses, 
manufacturing, retail, and healthcare. While algorithms can automate internal processes, many do 
not  necessarily  involve  advanced  AI  learning,  instead  assisting  decisionmaking  or  handling 
predefined situations18. 

Recent OECD research covering four EU countries (France, Germany, Italy, and Spain) found that 
79 %  of  firms  use  at  least  one  AM  tool19.  This  figure  is  higher  than  previously  understood  and 
suggests a significant evolution in managerial roles through automation. However, the intensity of 
use  varies,  with  European  firms  typically  demonstrating  moderate  usage,  adopting  three  to  five 
tools. This contrasts with the United States, which shows a higher adoption rate (90 %) and more 
intense use (ten or more tools), while Japan has a lower adoption rate (40 %) and intensity (typically 
one tool). The growth of these systems in the EU is continuous, with a positive relationship found 
between  the number  of AM  technologies  a  firm  currently  uses  and  its  plans  for  future  increased 
usage20. Tools are generally categorised into three main types based on their managerial functions. 

Instruction tools. These are relatively common in EU firms, with a 69 % adoption rate for at least 
one instruction task. They include software to allocate work schedules, work activities, and provide 
instructions on how to perform tasks21. 

Monitoring tools. These show varied adoption patterns, with 33 % of European firms using basic 
monitoring tools, such as software to track working time (44 % of EU firms) and the completion of 
work activities (40 % of EU firms). Less prevalent, but raising more concerns, are tools that monitor 
the speed of work (15 % in EU firms), content and tone of conversations/emails (6 % in EU firms), 
worker health and safety (15 %), worker fatigue or alertness (5 %), or track location (12 %). OSHA 
reports that workers in the EU indicate digital technologies are used to determine speed of work 
(52 %), increase surveillance (37 %), and supervise/monitor work or behaviour (25 %)22. 

Evaluation tools. These are less common than instruction tools in Europe, with a 35 % adoption rate. 
These tools are used for functions like setting targets for workers, rewarding good performance, 
sanctioning  poor  performance,  and providing  ratings  or  leaderboards.  They  are  considered more 
likely to result in biased treatment if not carefully implemented23. 

Beyond  these  categories,  specific  tools  include  robotics  in  logistics  for  storage  and  picking 
operations, where robots are managed by algorithmic software to navigate warehouses and interact 
with workers. AI-powered route planning tools are used in logistics to synthesize information for 
informed decisions on travel routes, though they can also lead to extensive monitoring of drivers. 
Instant messaging apps like WhatsApp are also noted as general-purpose digital technologies that 
can exert strong control and pressure over workers. 

18  

ILO, 2024. 

19   OECD, 2025. 

20   OECD, 2025; OSHA, 2024; ILO, 2024; Eurofound, 2024. 

21   OSHA, 2024. 

22   OSHA, 2024. 

23   OSHA, 2024. 

5 

 
EPRS | European Parliamentary Research Service 

EU-OSHA classifies human-robot interaction in the workplace into three categories24: 

•  Co-existence. This represents minimal engagement, where humans and robots briefly 
cross paths without shared objectives or synchronised activities. An example given is 
a transport robot passing a warehouse supervisor. 

•  Cooperation.  This  involves  a  deeper  level  of  interaction  with  common  goals  but 
separated responsibilities, where humans and robots perform different subtasks that 
contribute to the same outcome. 

•  Collaboration.  This  constitutes  the  most  integrated  form  of  interaction,  featuring 
simultaneous  work  on  identical  tasks  or  objects  with  ongoing  coordination  and 
dynamic task allocation based on situational needs. 

While  AM  has  the  potential  for  full  automation,  where  AM  systems  independently  perform 
managerial  tasks  with  minimal  human  intervention,  many  AM  systems  currently  deployed  in 
companies  do  not  fully  automate  decisionmaking.  Instead,  they  are  primarily  used  to  assist 
decisionmaking processes or to handle specific situations where parameters are predefined. This 
suggests that much of the interaction falls within the 'AI-assisted' or 'AI-augmented' management 
levels, where AI provides data and insights to human managers or actively participates with human 
oversight  and  intervention  possibilities.  For  instance,  in  some  logistics  and  healthcare  settings, 
human decisionmaking remains prominent, even where tasks are automated or supported by AM. 
However,  the  push  for  efficiency  often  leads  to  these  systems  increasing  work  intensity,  even  if 
human oversight is present.  

The  ILO  emphasises  that  technologies  not  explicitly  designed  for  work  coordination  can  still 
significantly influence work organisation and digital monitoring, regardless of their primary intended 
purpose25.  This  means  that  'even  if  these  digital  tools  or  algorithmic  systems  are  not  explicitly 
intended  to  manage  work  processes,  de  facto  they  can  be  considered  as  part  of,  or  at  least 
equivalent to, algorithmic management systems that produce effects on work organisation'. 

The  use  of  AM  in  EU  traditional  workplaces  aims  to  streamline  processes  and  achieve  efficiency 
gains. However, this also presents significant challenges and risks, particularly concerning job quality 
and worker wellbeing. Key concerns may include: 

•  Work  intensification  and  psychosocial  risks.  Digital  technologies  are  linked  to 
increased work intensity and time pressure. Workers report increased stress, reduced 
work  autonomy,  and  poor  communication  where  these  technologies  are  present. 
Continuous surveillance and performance pressure can lead to burnout and mental 
health issues. 

•  Monitoring  and  surveillance.  AM  tools  embed  a  strong  potential  for  worker 
monitoring  and  surveillance,  potentially  shifting  the  power  balance  towards 
management  and  raising  privacy  concerns.  This  can  result  in  workers  feeling 
constantly watched and a lack of transparency about how their data is used. 

•  Autonomy and control. While some technologies can increase worker autonomy by 
freeing them from strenuous tasks or enabling more intellectually challenging work, 

24   OSHA, 2024. 

25  

ILO, 2024. 

6 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

others, through precise instructions and constant monitoring, can reduce discretion 
and flexibility. 

•  Trustworthiness concerns. Managers themselves report concerns regarding unclear 
logic  (explainability),  and 
accountability,  difficulty  understanding  the  tools' 
inadequate protection of workers' physical and mental health. There is also a notable 
discrepancy, with many managers believing employees are aware of AM use, while 
worker surveys suggest a lack of awareness. 

Establishing the extent of AM usage across EU countries is difficult due to the broad definition of 
AM. There are four data sets collected during 2024 available that are informative of the prevalence 
of AM usage in the EU: European Working Conditions Survey (EWCS2024, from Eurofound), the 
Special Eurobarometer 554 (EB2024, European Commission) the European Survey of Enterprises 
on New and Emerging Risks 2024 (ESENER2024, from EU-OSHA) and the OECD survey on AI 2024 
(OECD2024, from OECD)26. The OECD2024 survey only covers four EU countries (DE, ES, FR, IT), 
whereas  the  other  three  surveys  cover  all  EU-27  countries.  The  EWCS2024  and  EB2024  survey 
workers, while ESENER2024 and OECD2024 are surveys at the company level. 

The surveys allow for an analysis of the prevalence of AM across the EU (Table 1). However, it is clear 
from  Table 1,  that  establishing  a  clear  benchmark  for  the  prevalence  of  algorithmic management 
across the EU is not feasible based on the available survey evidence, as they differ considerably in 
levels and variation among countries. Differences are to be expected since the four 2024 sources 
differ in unit of observation (workers in EWCS and Eurobarometer vs establishments in ESENER and 
the OECD survey), and question wording, items covered, as well as targeted respondents in the case 
of  ESENER2024  and  OECD2024  (Annex  C).  For  example,  EWCS  asks  workers  whether  their 
performance is monitored by 'a computer programme', while ESENER asks firms whether they use 
'machines, systems or computers' to monitor performance, and the OECD survey refers to 'software 
to partially or fully automate' monitoring, a broader concept that is likely behind the higher shares 
of firms reporting the use of AM in the OECD2024 survey. Moreover, comparing surveys targeting 
company-level respondents is not directly comparable to surveys targeting workers. For example, 
company-level adoption does not imply that all employees within that firm are subject to the AM 
tool.  

Further complexity in interpreting the survey outcomes is added by the fact that country rankings 
are  not  correlated  across surveys.  Consider  Denmark,  where  70.1 %  of  workers  self-report  being 
subjected to AM in the EWCS2024 survey, the largest reported number in that survey. In the EB2024 
survey, only 47 % report AM being used in their workplace, which is below the EU average of 51.3 % 
for  that  survey.  For  these  reasons,  the  estimates  in  Table  1  should  be  interpreted  with  caution, 
highlighting the need for a robust definition of AM. 

26   See Annex B for a description of the surveys. 

7 

 
 
EPRS | European Parliamentary Research Service 

Table 1 – Measure of AM prevalence (%) 

COUNTRY 

SHARE OF 
WORKERS (%), 
EWCS 2024 

SHARE OF 
WORKERS (%), 
EB2024 

SHARE OF 
COMPANIES 

SHARE OF 
COMPANIES 

(%), ESENER 2024 

(%), OECD 2024 

42.3 

50.5 

58.6 

35.6 

34.9 

43.8 

38.1 

70.1 

43.1 

27.2 

41.9 

41.7 

37.1 

48.9 

33.4 

52.5 

47.3 

31.0 

58.4 

40.9 

39.5 

65.9 

39.4 

26.8 

EU 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

8 

51.3 

65.6 

58.9 

44.4 

62.7 

38.7 

47.5 

47 

53.3 

36.1 

51.8 

69.1 

53.1 

41 

54.6 

56.6 

52.8 

47.6 

66.2 

52.7 

63.3 

41.3 

52.2 

41.3 

14.5 

20.6 

16.7 

18.5 

15.2 

13.6 

16.5 

17.6 

20.1 

6.0 

12.0 

31.2 

19.9 

11.6 

13.2 

15.1 

7.7 

21.6 

13.9 

16.9 

18.6 

10.7 

10.8 

11.2 

78 

78 

81 

76 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

COUNTRY 

SHARE OF 
WORKERS (%), 
EWCS 2024 

SHARE OF 
WORKERS (%), 
EB2024 

SHARE OF 
COMPANIES 

SHARE OF 
COMPANIES 

(%), ESENER 2024 

(%), OECD 2024 

41.9 

50.9 

40.0 

40.0 

68.8 

55 

49.9 

46.4 

14.6 

12.6 

12.1 

13.0 

RO 

SE 

SI 

SK 

US 

Japan 

90 

40 

Source: Authors' calculations based on EWCS2024, ESENER2024, Eurobarometer2024 and OECD2024. 

According to the EWCS2024, 42.3 % of workers in the EU have their work influenced by AM. There 
is  a  significant  variation  among  countries,  from  27.2 %  (GR)  to  70.1 %  (DK),  and  also  between 
countries considered similar on other labour market related parameters, such as BE (58.6 %) and DE 
(38.1 %). The EB2024 survey generally report higher numbers of AM usage among workers with an 
EU  wide  average  of  51.3 %.  There  is  considerable  variation  across  EU,  with  the  highest  recorded 
prevalence of AM usage in Finland (69.1 %) and the lowest in Greece (36.1). Looking at the numbers 
from a company angle, the share of firms using AM (ESENER2024) is considerably lower at 14.5 %. 
This reflects, to some extent, that these systems are more prevalent in large companies. Again, there 
is a large variation among countries, ranging from 6.0 % (EL) to 31.2 % (FI). As noted above, the share 
of companies does not correlate well with the share of workers across countries. The OECD2024 
survey  reports  much  higher  usage  of  AM  than  the  ESENER  survey  (most  comparable)  and  the 
EWCS2024. While 16.5 % of German companies report using AM, 78 % of firms report the use of AM 
in the OECD2024 survey. These differences are likely due to a broader question in the OECD survey. 

In the following section, the results reflect the EWCS2024, as this is the largest survey with worker-
level  characteristics.  Where  feasible  and  relevant,  comparisons  are  made  with  the  three  other 
surveys.  

Larger companies are more likely to use AM. For all countries, except the Netherlands, usage is much 
more prominent in medium and large companies compared to micro and small companies. In most 
countries, large companies (those with more than 250 employees) tend to have higher AM usage 
rates  than  medium-sized  companies,  although  this  pattern  is  not  universal.  The  same  pattern  is 
observed in the ESENER2024 survey of companies (not reported). Here, large companies are more 
than twice as likely to use AM compared to micro companies (28 % vs. 13 %). In the OECD2024, this 
pattern is only found in France. In Germany, Italy, and Spain, there is little difference in size between 
large and small companies (micro companies are not covered by the survey). 

In terms of employee characteristics, in most countries, men are more likely to experience AM at the 
workplace, though the difference is not large (44 % for men versus 41 % for women, Annex C, table 
C2). This holds for most individual countries, with a few exceptions (BG, CY, DK, FI, HR, IT, NL). 
Women  are  particularly  overexposed  to  AM  in  Cypress  (8  ppt.,  more  likely),  Croatia,  and  the 

9 

 
 
 
 
  
 
  
  
 
  
EPRS | European Parliamentary Research Service 

Netherlands  (both  5  ppt.).  Men  are  particularly  overexposed  in  the  Czech  Republic  (16  ppt.), 
Luxembourg (10 ppt.) and France (9 ppt.). 

There is a clear educational gradient in exposure to AM in the workplace. People with low educational 
attainment are less likely to work alongside AM. In most countries, those with the highest educational 
attainment  are  also  most  likely  to  be  exposed  to  AM.  Sweden  is  a  notable  exception.  At  the 
aggregate EU level, exposure to AM decreases with age. However, there is a lot of variation among 
countries  and  the  age  pattern  is  sometimes  non-monotonous.  In  four  countries,  AM  exposure 
increases with age (DK, FI, FR, IT). 

Exposure  also  varies  across  other  worker  characteristics  (Figure  C1,  Annex  C).  Workers  in 
establishments  with  a  union  or  OSH  representative  are  consistently  more  exposed  than  those 
without (EU: 47 % vs 32 %), a pattern visible in almost all Member States and strongest in Austria, 
Finland and France. A pattern which likely reflects the fact that represented workplaces tend to be 
larger and more formalised, where AM tools are more common. The Netherlands is the main outlier, 
with  similarly  high  exposure  regardless  of  representation.  Exposure  is  generally  higher  among 
workers with a permanent contract than among those without (EU: 44 % vs 35 %). This difference is 
sizeable  in  Austria,  Malta,  Luxembourg  and  Estonia.  A  few  countries  deviate,  with  the  Czech 
Republic, Croatia, Romania and Slovakia showing higher exposure among non-permanent workers, 
and Germany showing little difference. A similar pattern holds for working time: full-time workers 
are more exposed than part-time workers (EU: 44 % vs. 34 %), with large gaps in Bulgaria, Lithuania, 
and Austria. Exceptions include Cyprus, the Czech Republic, Portugal and Romania, where part-time 
workers report higher exposure. By migrant background, there is no EU-wide gap (migrants 41 %, 
natives 43 %), but heterogeneity is pronounced. Migrants are markedly more exposed in Bulgaria, 
Hungary, Luxembourg and Denmark, while natives are more exposed in Malta, Greece and Spain. 
These  contrasts  likely  mirror  country-specific  sectoral  sorting  of  migrant  and  native  workers. 
Sectoral  differences  in  AM  exposure  are  pronounced  (Figure  C1,  Annex  C).  Workers  in  financial 
services report the highest exposure (69 %), followed by transport (56 %) and industry (45 %), all 
above  the  EU  average  (42 %).  Public  administration,  other  services  and  health  are  close  to  the 
average. Exposure is lower in commerce and hospitality (39 %) and education (34 %), falling further 
in construction (30 %) and lowest in agriculture (24 %). 

Exposure to AM also varies by occupation. Clerical support workers report the highest exposure 
(55 %),  followed  by  technicians  and  associate  professionals  (49 %),  managers  (47 %)  and  both 
professionals  and  plant/machine  operators  (45 %),  all  above  the  EU  average  (42 %).  Service  and 
sales workers and craft trades are lower (both at 35 %), with elementary occupations at 25 % and 
skilled agricultural, forestry and fishery workers lowest at 16 %. 

AM tools are already prevalent in EU countries, with an average adoption rate of 79 % of firms using 
at least one such tool. This is a higher prevalence than previously understood, indicating a significant 
shift in managerial roles through automation. However, the intensity of use varies significantly by 
country and by sector (OECD 2025, ILO 2024, Eurofound 2024). In particular, as mentioned, United 
States shows the highest adoption (90 %) and intensive use, with over three-quarters of firms using 
ten or more of the 15 surveyed tool categories. U.S. firms commonly use all types of tools, including 
monitoring  and  evaluation  and  European  Countries  (France,  Germany,  Italy,  Spain)  exhibit  high 
prevalence  (averaging  79 %)  but  moderate  intensity,  with  most  firms  using  three  to  five  tools. 
Instruction  tools  are  relatively  common  (69 %),  followed  by  basic  monitoring  tools  (33 %,  e.g., 

10 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

tracking working time). Tools with higher potential risk, such as those for sensitive data monitoring 
or comprehensive evaluation, are less common. Beyond these, traditional workplaces also employ 
robotics  in  logistics,  AI-powered  route  planning  in  transport,  and  even  general-purpose  digital 
technologies like instant messaging apps (e.g., WhatsApp), which can exert significant control and 
pressure on workers. Paradoxically, despite the often-precarious nature associated with these roles, 
many workers in algorithmically managed jobs report liking their work and feeling they have choices. 
This 'choice-based consent' allows individuals to make frequent, albeit narrow, choices, fostering a 
sense of mastery that can mask the structurally problematic aspects of the work, contributing to the 
growing appeal of what's been termed the 'good bad job'27 . 

Employers that have not adopted AM technologies may face with several barriers28, in particular: 

•  High  Cost.  This  is  the  most  common  barrier  universally,  reported  by  79 %  of  firms 

across all surveyed countries. 

•  Staff Resistance. In France, Germany, Italy, Japan, and Spain, staff resistance is the 
second-most  important  reason  for  non-adoption,  ranging  from  56 %  to  67 %.  This 
suggests  that  in  these  regions,  workers'  attitudes  significantly  influence  adoption, 
potentially making non-adopting firms less likely to adopt in the future. 

•  Concerns  for  Workers.  Managers  in  European  countries  and  Japan  frequently  cite 

concerns for workers' safety and health as a reason for not adopting. 

•  Lack of Skills and Data. In the United States, the primary barrier is a lack of employee 
skills (68 %), followed by a lack of high-quality data (33 %) and IT infrastructure (33 %). 
Interestingly, despite these barriers, non-adopting U.S. managers are more likely to 
anticipate future adoption of AM tools. 

•  Confidentiality.  A  qualitative  challenge  is  the  reluctance  of  establishments  to 
transparently share their experiences with AM, especially in sectors like logistics, due 
to confidentiality concerns and the potentially sensitive nature of research findings. 

The rise of AM, while promising in many ways, casts a long shadow over the future of work. It poses 
significant challenges to the quality of jobs and the wellbeing of employees, a concern highlighted 
by  the  International  Labour  Organization29.  Even  the  managers  tasked  with  implementing  these 
systems voice considerable apprehension. One of their biggest worries, cited by 28 % of managers, 
is  unclear  accountability.  When  an  AM  system  makes  a  poor  decision,  who  is  responsible?  This 
ambiguity  persists  despite  regulations,  like  those  in  the  EU,  stressing  the  importance  of  human 
oversight. Closely following this concern, 27 % of managers struggle with the lack of explainability 
or opacity of these systems. It's often incredibly difficult, sometimes even for the AI's creators, to 
understand  the  logic  behind  an  algorithm's  decisions  or  recommendations.  This  makes  it  nearly 
impossible for workers or their representatives to challenge or even comprehend how these systems 
are  impacting  their  jobs.  Another  significant  concern,  also  reported  by  27 %  of  managers,  is  the 
inadequate protection of workers' physical and mental health. This issue is particularly acute in the 
US, where AM adoption is more widespread and potentially more intrusive. Interestingly, there is a 
disconnection regarding workers' awareness. While many managers believe their employees are well 

27   Cameron, 2024. 

28   OECD 2025, ILO 2024, Eurofound 2024. 

29  

ILO, 2024. 

11 

 
EPRS | European Parliamentary Research Service 

aware of AM's presence, surveys suggest that a substantial number of employees remain in the dark. 
Finally, though less frequently reported by managers (10 %), the potential for bias and discrimination 
within AM systems is a serious underlying issue. If these algorithms are trained on historical data 
that reflects existing societal stereotypes, they can inadvertently perpetuate and even amplify those 
biases. 

Beyond managers' perceptions, direct impacts on workers may include30: 

•  Work  intensification  and  time  pressure.  AM  systems  are  linked  to  increased 
workload,  pace,  and  time  pressure.  This  can  lead  to  increased  stress,  exhaustion, 
burnout, and related physical and mental health issues, as workers may feel compelled 
to meet performance targets without adequate breaks. 

•  Reduced autonomy and control. Algorithmic systems can limit worker discretion and 
flexibility  by  providing  precise  instructions  and  constant  monitoring.  This  can 
centralise  knowledge  and  reduce  workers' 
in  problem-solving, 
potentially degrading work content. 

involvement 

•  Monitoring  and  surveillance.  AM  systems  have  a  strong  potential  for  continuous 
worker monitoring and surveillance, including tracking location, keystrokes, and even 
biometric data like voice and facial expressions. This shifts the power balance towards 
management and raises significant privacy concerns, leading to anxiety and distrust. 
•  Erosion of work-life balance. The demand for constant availability and continuous 
connectivity blurs the boundaries between work and personal life, increasing unpaid 
labour  (e.g.,  time  spent  waiting  for  tasks)  and  making  it  difficult  for  workers  to 
disengage from work. 

•  Skill transformation and potential deskilling. While AM can create demand for new 
roles  (e.g.,  data  managers,  robot  technicians)  and  skills  (e.g.,  analytical,  problem-
solving, communication skills), it can also automate routine tasks, potentially reducing 
the demand for specific skills and leading to deskilling as algorithms perform more 
complex functions. 

•  Challenges  to  Industrial  Relations.  There's  often  a  lack  of  awareness  and  limited 
involvement  of  trade  unions  in  decisions  regarding  technological  change  and  AM 
implementation. This weakens workers' collective bargaining power and their ability 
to shape the terms of technology adoption, making it difficult to contest issues like 
working hours or monitoring. 

30   OECD 2025, ILO 2024, Eurofound 2024. 

12 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

2. Task 2 – Impact on employment relations 

2.1. Methodological approach 

The  impacts  of  AM  are  multifaceted,  presenting  both  benefits  and  significant  challenges.  While 
managers, particularly in the US, perceive improvements in managerial decision quality, speed, and 
their own job satisfaction, there are growing concerns regarding trustworthiness, such as unclear 
accountability, difficulty understanding the algorithm's logic, and inadequate protection of workers' 
physical  and  mental  health.  For  workers,  AM  often  leads  to  increased  surveillance,  work 
intensification, reduced autonomy and discretion, and potential deskilling, contributing to a paradox 
where  time  needed  for  paid  work  shrinks  but  availability  for  work  expands.  This  can  centralise 
knowledge  and  weaken  high-involvement  approaches.  Some  research  suggests  that  workers 
surprisingly report liking AM and finding choice, leading to a phenomenon of 'choice-based consent' 
that can mask structural problems, extending agency beyond mere resistance or compliance31. 

Our  methodology  involves  a  two-phase  approach,  an  initial  desk  review  followed  by  stakeholder 
interviews. The desk review will define the full spectrum of impacts of AM in the workplace. We 
examine the effects on: 

•  Employment relationships. Covering contracts, work arrangements, and collective 

bargaining. 

•  Employment  structure.  Including  workforce  skillset  evolution,  potential  shortages, 

and shifts in demand. 

•  Working conditions. Such as working hours, work organisation, training needs, and 

work-life balance. 

•  Health  and  safety.  Addressing  work  intensity,  mental  health  concerns,  and  other 

relevant aspects. 

The  review  also  identifies  broader  impacts  on  productivity,  working  hours,  health,  safety,  and 
fundamental  rights.  We  draw  upon  a  variety  of  sources,  including  academic  literature,  industry 
reports from organisations like OSHA, Eurofound, ILO, and unions, policy and regulatory documents, 
case studies, media and news for current trends and emerging concerns. We collected and organised 
this  data  to  assess  key  impact  areas,  compare  findings  across  sources,  and  identify  trends, 
contradictions,  and  knowledge  gaps.  The  desk  review  qualitatively  defines  and  assesses  these 
impacts. 

Following the desk review, we conducted five interviews—one with each type of stakeholder: EU-
level social partners (unions and company organisations), EU associations of managers, and AI Ethics 
experts, plus an additional interview with a US academic focusing on future trends and challenges. 
These  interviews  served  to  validate  the  desk  review's  findings,  gather  nuanced  feedback,  and 
identify emerging trends and challenges not yet analysed32. 

31  

ILO, 2024; Eurofound, 2024. 

32   See Annex D, Interviewed stakeholders and scheme for interviews. 

13 

 
EPRS | European Parliamentary Research Service 

2.2. AM impacts on the workplaces 

AM  has  expanded  significantly  from  digital  labour  platforms  to  conventional  workplaces.  This 
widespread  adoption  is  driven  by  the  promise  of  productivity  and  efficiency  gains.  Managers 
surveyed by the OECD, for instance, often perceive that AM tools improve the quality, speed, and 
autonomy of their decisionmaking due to increased information availability, thereby enhancing firm 
productivity.  However,  this  comprehensive  review  identifies  broader  impacts  on  productivity, 
working hours, health, safety, and fundamental rights33. 

Working  hours  are  significantly  transformed  by  AM,  which  enables  highly  precise  matching  of 
workloads and workers in real-time, often leading to 'atomised and punctuated time' where paid 
work is offered in increasingly shorter and scattered units34. Despite this efficiency, workers often 
experience  pressure  for  'incessant  availability'  to  secure  these  small,  scattered  tasks,  blurring 
work/non-work boundaries and leading to an expansion of uncompensated work-related activities 
such  as  waiting  for  shifts  or  planning  fragmented  time  units.  This  can  also  result  in  work 
intensification  as  algorithms  push  for  maximum  output,  as  seen  in  sectors  like  logistics  where 
continuous  monitoring  of  pick-rates  and  breaks  aims  to  maximize  efficiency  and  can  remove 
workers' ability to control pace35. 

Regarding health and safety, AM systems pose significant psychosocial risks, which are increasingly 
recognised  as  workplace  hazards.  Constant  monitoring,  such  as  speech  analytics  or  keystroke 
tracking, is linked to worker stress, anxiety, and distrust, especially when metrics are perceived as 
unfair or opaque36. While some technologies like robots can improve physical safety by performing 
hazardous tasks, studies indicate that human-robot interaction is associated with a faster pace of 
work and increased surveillance. The reduction or absence of human intervention in decisionmaking 
and  diminished  interactions  with  managers  and  peers  can  further  lead  to  stress,  social  isolation, 
reduced  autonomy,  and  de-skilling,  negatively  affecting  mental  health  and  job  satisfaction.  For 
example, a Swedish case study in transport found that a ride scheduling system that optimises pick-
up and drop-off schedules increases stress and pressure, neglecting basic worker needs like food 
and bathroom breaks. Managers themselves report  concerns about the inadequate protection of 
workers' physical and mental health due to AM tools37. 

From  the  perspective  of  fundamental  rights,  AM  introduces  critical  challenges  related  to  data 
protection,  privacy,  transparency,  and  non-discrimination38.  Algorithmic  systems  collect  vast 
amounts of personal data on worker behaviours, locations, performance, and even emotional states, 
raising privacy concerns and increasing information asymmetry where workers often lack access to 
how algorithms work or what data is used.  

33   OECD, 2025. 

34   Piasna, 2024. 

35   OECD, 2025; Doellgast, Wagner, O'Brady, 2023. 

36   OSHA, 2024; Piasna, 2024. 

37   OSHA, 2024; Piasna, 2024. 

38   HAI Stanford, 2025; Ciucciovino, 2024; Faioli, 2025; Gent, 2024; Peruzzi, 2024; Adams-Prassl et al., 2023; De Stefano 

and Wouters, 2022; Aloisi, 2022. 

14 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Reports  from  organisations  like  OSHA  and  Eurofound,  alongside  academic  literature  and  case 
studies, frequently highlight these challenges and the need for a human-centred approach, strong 
data  protection  guarantees,  worker  consultation,  and  collective  bargaining  to  mitigate  negative 
impacts and ensure ethical deployment of AM39. At the forefront of these concerns is a noticeable 
intensification of work and a rise in psychosocial risks. AM systems can push employees work harder 
and faster. This often translates into increased workloads, an accelerated pace, and an unrelenting 
demand for performance, which, in extreme cases, can even lead to accidents and musculoskeletal 
disorders.  Studies  paint  a  clear  picture:  workers  under  intense  digital  monitoring  and  AM  are 
significantly  more  likely  to  experience  increased  monotony  and  stress40.  Beyond  the  immediate 
workload,  AM  can  subtly  pressure  individuals  into  extended  availability,  demanding  more  labour 
effort without necessarily increasing paid time. This blurs the line between professional and personal 
life,  sometimes  leading  to  a  state  of  perpetual  working.  The  constant  digital  surveillance,  the 
pressure from automated metrics, and a perceived lack of human oversight can culminate in serious 
stress, burnout, and significant mental health issues. Tellingly, even managers recognise this danger, 
with a notable 27 % reporting inadequate protection of workers' physical and mental health when 
AM tools are in play.  

Another pervasive challenge is the threat of surveillance, intrusion into privacy, and a troubling lack 
of transparency. AM tools inherently carry a substantial risk of monitoring employees, often leading 
to  deep  intrusions  into  their  private  lives  by  potentially  accessing  very  personal  information. 
Electronic  monitoring,  for  instance,  can  feel  profoundly  invasive41.  Compounding  this  is  a 
widespread lack of transparency about how these AM systems actually function and how they are 
being used. This opacity severely hinders meaningful worker participation and constructive social 
dialogue. Managers themselves are frequently troubled by this, with 27 % expressing concerns about 
the  lack  of  explainability  of  algorithmic  decisions  and  28 %  citing  unclear  accountability  when  an 
algorithmic recommendation is made. What is more concerning is that many workers remain entirely 
unaware that AM tools are even being used. And even when they are, they often have no option to 
opt out of data collection, access the data gathered on them, or request corrections42. AM can also 
gradually chip away at a worker's autonomy and potentially degrade the richness of their job. AM 
systems  can  directly  dictate  tasks  with  such  precision  that  they  leave  little  room  for  human 
discretion or individual judgment. This means less freedom in how one approaches their work.  

Furthermore, the implementation of AM can inadvertently centralise knowledge within the company, 
potentially  making  an  individual  worker's  problem-solving  activities  feel  superfluous.  This  can 
diminish  the  inherent  richness  and  complexity  of  a  job,  weakening  traditional  'high-involvement' 
management styles that thrive on active worker participation and optimisation43. Algorithms, while 
seemingly objective, are not immune to human flaws. They can, in fact, reinforce existing forms of 
gender  and  racial  inequality  because  they  often  learn  from  historical  data  that  carries  inherent 
societal  biases.  Evaluation  tools,  in  particular,  appear  more  susceptible  to  resulting  in  biased 

39   Eurofound, 2024; OSHA, 2024; HAI Stanford, 2025; Treu, 2024; Peruzzi, 2024; Piasna, 2024; Doellgast, Wagner and 

O'Brady, 2023; Aloisi, 2022. 

40   OECD, 2025; OSHA, 2024. 

41   OSHA, 2024. 

42   OSHA, 2024. 

43   OSHA, 2024. 

15 

 
EPRS | European Parliamentary Research Service 

treatment. This is a recognised concern, with 10 % of managers reporting that algorithmic decisions 
are not entirely free from bias and discrimination, and this concern is notably higher in regions like 
the US and Japan compared to Europe44. Finally, a significant and thorny issue is accountability. A 
notable concern among managers is the profound lack of clarity regarding who is responsible when 
an algorithmic decision or recommendation turns out to be incorrect.  

The cross-analysis may determine the following results. However, the outcomes vary, and there is a 
need for a human-centred approach, robust data protection, active worker consultation, and strong 
collective  bargaining  to  mitigate  negative  impacts  and  ensure  ethical  deployment.  Here's  a 
qualitative  assessment  of  AM's  impacts  on  productivity,  working  hours,  health,  safety,  and 
fundamental rights, alongside key trends, contradictions, and knowledge gaps, based on previous 
literature review45 .  

Table 2 – Impacts of algorithmic management on workers and workplaces 

IMPACTS ON PRODUCTIVITY 

Efficiency 

A  primary  motivation  behind  AM's  introduction  is  to  maximize  profit,  increase 
productivity,  and  foster  efficiency  gains.  Case  studies  in  logistics  and  healthcare 
generally  show  a  positive  impact  on  productivity  through  the  streamlining  and 
simplification  of  work  processes.  For  instance,  a  logistics  company  saw  technology 
provide  an  overview  of  inbound  and  outbound  volumes,  aiding  worker  planning  and 
ensuring sufficient coverage during peak times. In healthcare, data analytics improved 
productivity  and  streamlined  information  flows,  optimising  internal  processes  and 
automating  manual  tasks.  Managers  often  perceive  AM  tools  as  leading  to  improved 
quality  of  managerial  decisionmaking,  increased  information  availability,  and  faster 
decisionmaking 

Work 
intensification 

While direct evidence of job losses resulting directly from these technologies is limited 
in  some  European  cases,  AM  may  pave  the  way  for  future  labour  displacement  by 
facilitating  efficiency  gains.  The  quest  for  efficiency  frequently  leads  to  work 
intensification, driving workers to operate at higher speeds and for longer periods, often 
without proportional increases in paid time. 

IMPACTS ON WORKING HOURS AND WORK ORGANISATION 

Pressure 

AM systems frequently lead to increased work intensity, performance pressure, stress, 
and  exhaustion.  This  can  manifest  as  increased  workload,  faster  pace,  and  higher 
demand  for  a  job,  potentially  leading  to  accidents  and  musculoskeletal  disorders.  In 
some cases, like South Africa 46, workers reported being forced to go outside premises 
for  toilet breaks, with biometric records monitoring their time, making  the workplace 
feel like a 'prison'. 

44   OSHA, 2024. 

45   OECD, 2025; OSHA, 2024; ILO, 2024; Litardi, Adăscăliței, Widera, 2024. 

46   Digital technologies for worker management: implications for safety and health. A comparative study of two 

automotive companies in Belgium and Italy – EUOSHA 2024. 

16 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Atomised time 

Skill 
degradation 

AM fosters a model where employers can organise and purchase labour input in shorter, 
discontinuous time units, shifting risks and costs associated with inefficiency to workers. 
This  results  in  tasks  being  precisely  timed,  and  workers  being  paid  only  for  'active' 
periods,  often  leaving  unpaid  gaps.  Automated  systems  require  incessant  availability 
from workers to access small, scattered units of paid activity, blurring work/non-work 
boundaries.  This  continuous  availability  can  lead  to  a  state  of  perpetual  working  and 
make it difficult for workers to enjoy free time. 

AM systems can directly impact workers' job autonomy and lead to a reduced level of 
discretion in tasks. Precise algorithmic instructions often leave minimal room for human 
manoeuvre. The implementation of AM can contribute to the centralization of knowledge 
within the company, potentially making workers' problem-solving activities superfluous 
and thus reducing the richness of work content and leading to de-skilling. 

Work-life 
balance 

The blurring of boundaries between paid work and personal life, driven by pressures for 
extended availability, can result in stress, burnout, and mental health issues. 

IMPACTS ON HEALTH AND SAFETY 

Psychosocial 
risks 

This is a major concern. AM is linked to increased stress, anxiety, exhaustion, and mental 
health  problems  due  to  constant  surveillance,  performance  pressure  from  automated 
metrics,  lack  of  human  oversight,  and  reduced  human  interaction.  The  emphasis  on 
productivity  can  lead  to  an  unhealthy  competitive  environment  and  social  isolation 
among  colleagues,  weakening  team  spirit  and  support.  Reduced  interactions  with 
supervisors, when AM systems replace them, can also increase stress. 

Physical safety 

While advanced robots can enhance physical safety by performing hazardous tasks, and 
some case studies show improved safety by reducing physical strain and fatigue, other 
research suggests that human-robot interaction can be associated with increased work 
intensity and surveillance, which may indirectly affect physical safety. 

IMPACTS ON FUNDAMENTAL RIGHTS 

Surveillance 

AM  tools  present  a  significant  risk  of  worker  surveillance  and  can  lead  to  serious 
intrusions into workers' private lives by accessing intimate or personal information. This 
constant  monitoring  can  cause  anxiety  and  distrust.  For  example,  cameras  in  trucks 
monitoring drivers' seatbelt use and every movement highlight the lack of privacy. 

Lack of 
transparency 
and 
explainability 

There is a pervasive lack of transparency about how AM systems operate and are used, 
hindering worker participation and social dialogue. Many workers are unaware that AM 
tools are being used, and even when aware, they often cannot opt out of data collection, 
access their collected data, or request corrections. 

Discrimination 

Algorithms can reinforce existing forms of gender and racial inequality as they often rely 
on historical data, which may contain inherent biases. 

Accountability 

A  significant  concern  among  managers  (28 %)  is  the  lack  of  clarity  regarding  who  is 
responsible when an algorithmic decision or recommendation is incorrect. Establishing 
liability  for  harm  caused  by  AI  systems,  especially  for  psychological  harm,  can  be 
complex. 

Freedom of 
association and 

The pervasive control and individualization of the employment relationship can weaken 
workers' bargaining positions and hinder collective representation and social dialogue. 

17 

EPRS | European Parliamentary Research Service 

collective 
bargaining 

QUALITATIVE ASSESSMENT, TRENDS, AND CONTRADICTIONS 

Varying 
impacts 

The  impact  of  AM  is  far  from  homogeneous,  with  similar  technologies  yielding  very 
different  effects  depending  on  the  country,  sector,  and  existing  institutional  and 
regulatory frameworks. For instance, European countries (Italy and France) tend to show 
milder  negative  impacts  due  to  stronger  regulatory  safeguards  and  union  presence, 
while other countries may show more direct evidence of negative impacts like worker 
monitoring and surveillance. 

Human 
involvement 

Despite  the  automation,  human  agency,  negotiation,  and  oversight  remain  critical. 
Managerial goals can be process-oriented (focusing on systemic problem elimination) 
rather than solely workforce-oriented (focused on work intensification), influenced by 
the regulatory context and worker voice. 

Contradiction 
in perceptions 

There is a notable contrast between managers' perceptions and workers' experiences. 
Managers often report benefits and an awareness of AM tool use, yet other research and 
worker  surveys  reveal  significant  negative  impacts  on  job  quality,  mental  health,  and 
often  a  lack  of  awareness  or  understanding  from  the  workers'  side.  This  highlights  a 
knowledge gap between management and the workforce regarding the full implications 
of AM. 

Research gaps 

The phenomenon of AM is still relatively new, and there is limited empirical evidence, 
particularly quantitative data and in-depth qualitative studies beyond the gig economy 
and warehousing. Further research is needed to understand the long-term impacts, how 
outcomes vary by tool type, and the effectiveness of different governance measures. 

Source: Authors' elaboration. 

The impacts of AM can be also structured into a matrix covering the three key dimensions above 
mentioned. The following table provides a synthesis of impacts and examples and is based on the 
outcomes mentioned above.  

Table 3 – Dimensional analysis of AM workplace impacts  

DIMENSION 

IMPACT DESCRIPTION 

KEY EXAMPLES 

Employment 
relationship: This 
dimension concerns the 
legal and contractual 
aspects of work, 
including employment 
contracts, and types of 
work arrangements  

AM introduces a 'third 
element' into the 
traditional worker-
employer relationship, 
influencing how 
contracts are defined, 
tasks are assigned, and 
disputes are managed, 
often interacting with 
existing legal 
frameworks. 

Recruitment and selection processes: explicitly 
classified as high-risk under the AI Act due to their 
potential to significantly impact future 
employment opportunities. For example, an AM 
system might screen resumes or conduct initial 
interviews. A 'case' could involve a candidate 
alleging discrimination based on factors like gender 
or age because the AI system was trained on 
biased data. 

AM-driven performance evaluation and 
termination: AM systems can monitor worker 
performance, efficiency, or behaviour to inform 
evaluations or decisions about contract renewal or 
termination. A 'case' here could involve a worker 

18 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

DIMENSION 

IMPACT DESCRIPTION 

KEY EXAMPLES 

Employment structure:  
This element focuses on 
the composition and 
characteristics of the 
workforce, including 
skill sets, potential 
shortages, and changes 
in job demand 

AM tools can automate 
tasks, changing the 
skills required for 
existing jobs and 
creating demand for 
entirely new roles. This 
impacts workforce 
planning, potentially 
creating skill gaps or 
surpluses. AM systems 
used for hiring and task 
assignment directly 
shape the workforce 
composition. 

Working conditions:  
This includes the 
practical aspects of 
work, such as working 
hours, work 
organisation, training, 
and work-life balance 

AM systems directly 
influence daily work 
through automated 
scheduling, task 
allocation, and 
continuous monitoring. 
This can affect work 
intensity, autonomy, 
and the boundary 
between work and 
personal life. 

being dismissed based on metrics generated by an 
opaque AM system without understanding the 
criteria used. 

Skill set transformation: The deployment of AM, 
including broadly capable models like GPAI, can 
automate routine tasks, reducing the demand for 
specific manual or administrative skills. 
Simultaneously, it increases the need for skills 
related to managing, maintaining, and interacting 
with AM systems. A 'case' might involve a company 
rapidly adopting AM tools, leading to a significant 
portion of its workforce lacking the necessary 
digital skills, creating an internal 'skill shortage'. 
This highlights the need for substantial investment 
in training and reskilling programs. 

Changes in job demand: Certain jobs or tasks may 
become obsolete due to AM, while new roles (e.g., 
AI trainers, data curators) emerge. A 'case' could 
involve a logistics company implementing AM for 
route optimisation and scheduling, reducing the 
need for human dispatchers but requiring new 
roles for monitoring and managing the AM system. 
The concept of 'high-impact systems' affecting 
employment decisions is relevant here, as these 
systems can reshape entire segments 

Algorithmic scheduling and task allocation: AI-
powered systems can automate scheduling, 
assigning tasks to workers based on algorithms 
optimising for efficiency. This is common in 
platform work or logistics. A case could involve a 
delivery driver whose schedule is constantly 
altered by an algorithm based on real-time 
demand, leading to unpredictable working hours 
and difficulty planning personal life (impacting 
work-life balance). This automation can also lead 
to increased work intensity as algorithms push for 
maximum output. 

Performance monitoring and surveillance: AM 
systems can track keystrokes, location, speed, or 
interactions to monitor worker performance. A 
'case' might involve customer service 
representatives whose conversation patterns are 
analysed by AM to evaluate empathy or adherence 
to scripts, leading to stress or a feeling of being 
constantly watched. Transparency about what data 
is collected and how it is used for evaluation is 

19 

EPRS | European Parliamentary Research Service 

DIMENSION 

IMPACT DESCRIPTION 

KEY EXAMPLES 

Health and safety:  
This dimension covers 
risks to workers' 
physical and mental 
wellbeing, including 
work intensity, mental 
health, and physical 
safety. 

AM can introduce new 
psychosocial hazards 
like increased work 
intensity, stress from 
surveillance, or mental 
health problems. 
Conversely, AM could 
potentially be used for 
safety monitoring. 
Addressing these 
impacts often involves 
liability frameworks and 
specific safety 
regulations. 

critical, with tools like model cards potentially 
offering some level of clarity. 

Physical injuries: AM-controlled machinery or 
robots in industrial settings can cause physical 
harm if they malfunction. A classic 'case' involves a 
worker injured by a robotic arm that did not stop 
as expected. Establishing liability can be complex 
due to the AM system's complexity and the 
difficulty in proving a 'defect' or causal link. 

Mental health and psychosocial risks: Continuous 
algorithmic surveillance, pressure to perform 
based on automated metrics, and lack of human 
oversight can lead to increased stress, burnout, 
and mental health issues. The prohibition of 
emotion recognition systems in the workplace, 
except under strict conditions, is a direct 
regulatory response to the potential psychosocial 
harms of AM surveillance. A case could involve a 
worker experiencing severe anxiety or depression 
attributed to constant monitoring and performance 
pressure from an AM system. 

Work intensity and fatigue: AM-driven 
optimisation of tasks and schedules can lead to 
excessive work intensity without adequate rest, 
increasing the risk of fatigue-related accidents or 
long-term health problems. This is an inherent risk 
of AM focused purely on efficiency. The need to 
adapt safety regulations implies addressing issues 
like work-rest cycles and monitoring intensity 
under algorithmic control. 

Source: Authors' elaboration. 

Estimating the impact of AM 

Using the EWCS survey, it is possible to analyse the potential impacts of AM, though with cross-
sectional data, it is difficult to establish causality. However, by comparing workers with and without 
AM  exposure  within  the  same  country,  sector  and  occupation,  and  controlling  for  age,  sex, 
education, migrant background, firm size, contract type and part-time status, we approximate the 
association between AM and outcomes 'all else equal'. The coefficients can be read as percentage-
point differences relative to otherwise similar workers (Table 4). 

20 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Table 4 – Estimated effects of AM on selected outcomes (EU, EWCS2024) 

MEASURE 

INDICATION 

PREVALENCE 
2024 (%) 

Stress 

Negative 

23.9 

Autonomy at 
work 

Good work-life 
balance 

Able to get 
hour off 

Sufficient rest 
between 
workdays 

Work bad for 
health 

Positive 

Positive 

Positive 

Positive 

Negative 

49.6 

84.1 

67.7 

82.6 

26.9 

IMPACT OF 
AM ON 
MEASURE 
 (p.p.) 

ABOVE 
STATISTICAL 
UNCERTAINTY 
('STATISTICALLY 
SIGNIFICANT') 

SIGNIFICANT 
IMPROVEMEN
T OF HAVING 
'PROCEDURE' 

6.2 

-2.0 

-2.4 

-6.3 

-2.3 

4.8 

Yes 

No 

Yes 

Yes 

Yes 

Yes 

No 

No 

No 

Yes 

No 

No 

Source: Authors' estimations based on EWCS2024. 

The  estimates  indicate  that  AM  exposure  is  associated  with  higher  rates  of  stress  (+6.2 p.p. 
compared  to  the  population  prevalence  of  23.9 %) and  a greater  share  reporting  work  is  bad  for 
health (+4.8 p.p.), both estimates beyond statistical uncertainty. On dimensions linked to time and 
flexibility, AM is associated with a lower likelihood of good work–life balance (–2.4 p.p.), reduced 
ability  to  get  an  hour  off  when  needed  (–6.3 p.p.),  and  less  frequent  sufficient  rest  between 
workdays  (–2.3 p.p.);  all  three  gaps  are  beyond  statistical  uncertainty.  By  contrast,  perceived 
autonomy at work shows a small, non-significant (within the statistical uncertainty range) difference 
(–2.0 p.p.).  The  EWCS2024  survey  asks  workers  subjected  to  AM  if  their  employer  has  a  'formal 
procedure' to contest or review AM decisions. The last column of Table 4 indicates whether it makes 
a difference for the outcomes (e.g. stress, autonomy at work) if the worker/respondent has access 
to  a  formal  complaint  procedure  as  indicated  in  the  EWCS  survey.  The  only  clear  improvement 
appears for the ability to get an hour off, where the negative association disappear in firms with such 
a procedure; for the other outcomes, the presence of a procedure does not materially change the 
estimated effects. 

2.3. AM  and  interviews  results,  validation  of  the  approach  and 
future trends 

This  summary  draws  on  the  interviews  conducted  with  representatives  from  five  different 
stakeholders: Confédération Européenne des Cadres (CEC) representing EU managers, Wipro a 
company providing digital transformation services, industriAll Europe a global Union federation, 
the European Economic and Social Committee (EESC), and New York University (NYU), with the 
contribution of a researcher dealing with algorithmic management and digital transformation. The 

21 

EPRS | European Parliamentary Research Service 

goal  was  to  assess  the  real-world  implications  of  AM  in  the  workplace.  The  following  synthesis 
identifies  cross-cutting  themes,  shared  concerns,  and  noteworthy  divergences,  supported  by 
institutional references. 

General perceptions and validation of desk review 

All  respondents  broadly  validated  the  desk  review's  key  findings  regarding  the  impact  of  AM  on 
work.  A  common  theme  was  the  intensification  of  pre-existing  dynamics,  such  as  productivity 
tracking,  decision  opacity,  and  blurred  managerial  responsibilities  (CEC,  Wipro,  EESC).  Several 
highlighted  the  cumulative  effect  of  algorithmic  exposure  over  time,  with  constant  surveillance 
becoming  more  intrusive  and  less  transparent  (industriAll  Europe,  Wipro).  Some  organisations 
acknowledged benefits such as increased safety or reduced repetitive tasks (Wipro, NYU), while all 
agreed  that  such  advantages  are  conditional  on  worker  agency,  transparent  design,  and  human 
oversight (industriAll Europe, CEC). 

In  addition,  one  of  the  stakeholders  (industriAll)  shared  the  result  of  a  recent  study  on  the 
deployment  of  AI  in  manufacturing  and  energy  sectors  confirming  that  AI  is  already  widely 
integrated  across  value  chains  from  R&D  to  after-sales.  The  research highlights  that AI  adoption 
tends  to  be  more  positive  when  workers  are  involved  through  their  representatives  and  receive 
appropriate training. 

Emerging tools and real-world applications 

Interviewees referenced various new AM tools, including wearables for posture tracking and tools 
claiming  to  detect  emotional  states  or  'micro-stress'  (Wipro,  industriAll  Europe).  While  these 
technologies  may  offer  health  and  safety  benefits,  stakeholders  noted  concerns  about  their 
contribution  to  a  culture  of  emotional  surveillance  and  data  misuse.  Respondents  observed  that 
compared to earlier generations of automation, these tools do not merely replace physical labour 
but actively govern behaviours and decisions, creating new power asymmetries (CEC, Wipro). In 
this  context,  one  respondent  (industriAll  Europe)  emphasised  that  algorithmic  systems  must  be 
auditable  and  contestable  throughout  their  lifecycle,  not  just  at  initial  deployment,  to  ensure 
accountability and transparency. 

Employment structures and industrial relations 

There  was  strong  alignment  across  interviews  that  AM  is  reshaping  employment  relationships, 
especially  in  platform  work  and  hybrid  settings.  Respondents  warned  of  the  growing  mismatch 
between  legal  status  and  managerial  control,  with  some  workers  managed  like  employees  but 
lacking corresponding rights (EESC, Wipro). All respondents criticised the absence or erosion of 
social dialogue, with AI tools introduced unilaterally and outside traditional consultation frameworks 
(industriAll  Europe,  EESC).  New  bargaining  demands  are  emerging,  but  institutional  responses 
remain underdeveloped (Wipro, CEC). 

Work organisation and working time 

Most  respondents  observed  that  AM  is  transforming  work  organisation,  making  decisionmaking 
more  top-down  and  diminishing  the  role  of  middle  managers  (Wipro,  NYU).  Scheduling  and 
performance  monitoring  tools  are  disrupting  work-life  balance,  with  constant  alerts  and 
unpredictable shifts (industriAll Europe, CEC). There was consensus that workers are experiencing 

22 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

increased  mental  stress  due  to  continuous  monitoring  and  vague  performance  metrics.  This 
contributes to surveillance anxiety and reduced informal interaction, which is essential to workplace 
wellbeing (NYU, Wipro). 

Given  these  evolving  workplace  dynamics,  one  stakeholder  suggested  that  information  and 
consultation mechanisms should  be  systematically updated  to  reflect  evolving  systems  and  uses 
(industriAll Europe). 

Skills, training and workforce adaptation 

All  interviewees  pointed  to  a  widening  digital  literacy  gap,  both  among  workers  and  managers. 
Beyond technical upskilling, there is a need for critical awareness of how AI systems function and 
can  be  challenged  (Wipro,  NYU,  EESC).  This  was  seen  as  a  prerequisite  for  fairness  and 
accountability  in  digital  workplaces.  In  line  with  this  training  and  early  consultation  are  key  to 
mitigating  risks  and  ensuring  positive  impact  from  AI  technologies  in  workplaces  (industriAll 
Europe).  

Economic impacts and fundamental rights 

Respondents largely agreed that productivity gains from AM are unequally distributed, with most 
benefits accruing to capital while workers face intensified demands and reduced autonomy (CEC, 
Wipro). Several mentioned a lack of rights-based design, particularly concerning privacy, bias, and 
human dignity (Wipro, EESC). Bias in recruitment and evaluation was cited as a persistent problem. 
Emotion detection and voice analysis tools were strongly criticised for pseudoscientific claims and 
discriminatory impacts (Wipro, industriAll Europe). 

Social inclusion and unforeseen implications 

There was broad agreement that older workers, migrants, racialised groups, and those with lower 
digital skills are disproportionately affected by AM systems (Wipro, NYU). These groups are often 
underrepresented in training datasets, leading to misclassification or exclusion (EESC, Wipro). A 
few respondents warned of 'cultural colonisation' the export of Western designed tools into contexts 
that lack local regulatory safeguards (Wipro). 

Policy recommendations and future outlook 

All 
interviewees  called  for  stronger  regulatory  frameworks,  enforceable  standards,  and 
institutionalised  worker  consultation  rights.  Ethical  guidelines  alone  were  deemed  insufficient 
(industriAll Europe, NYU, CEC). These perspectives echo recent OECD findings and underline the 
urgent need to go beyond the current AI Act in ensuring that the transition to AM respects rights, 
promotes fairness, and protects the wellbeing of all workers. 

2.4. Analysis of case studies 

During the study, four case studies have been implemented to investigate more in depth the impact 
of  AM  in  the  workplaces.  The  case  studies  show  how  AM  is  used  in  different  industries  and  in 
different countries. In all cases AM systems have changed the way people work by lowering the need 
for direct human supervision and raising the need for algorithm-based tasks allocation. Employment 
structure  changes  are  evident  in  all  sectors,  with  traditional  roles  being  transformed  and  new 
technical positions emerging. However, the German automotive case shows a more organised way 

23 

EPRS | European Parliamentary Research Service 

of getting workers' input during implementation. There is a clear trend towards algorithm-driven 
task management in the workplace. This often leads to more efficient work but also less freedom for 
workers and more monitoring. There are two sides to the health and safety effects: AM can lower 
physical strain and make some safety aspects, but all cases report more mental stress and work. 
Governance  challenges  appear  universal,  with  transparency  and  explainability  remaining 
problematic  despite  varying  regulatory  frameworks.  Stronger  data  protection  rules  support  the 
French  healthcare  system,  while  the  German  automotive  case  shows  that  formal  consultation 
processes can help stakeholders get involved more actively, suggesting that regulatory context and 
implementation approach significantly influence outcomes. 

2.4.1. Case studies 

The report presents a selection of four case studies to see more in depth the impact of AM in the 
workplaces. 

Algorithmic management in the Italian logistics sector 

Description of sector main characteristics 

The logistics sector in Italy is a critical infrastructure that connects supply chains across Europe. It 
comprises a range of operators—from multinational carriers to small regional couriers—operating in 
a fragmented yet highly dynamic environment. The sector is characterised by high-volume, time-
sensitive deliveries that rely heavily on digital tools for planning, routing, and coordination. These 
characteristics make logistics a prime sector for the adoption of algorithmic management (AM)47. 

Description of most recent/innovative AI/AM methodologies adopted 

AM in Italian logistics typically involves platforms like Kronos, Hubstaff, and bespoke AI systems 
developed in-house. These tools automate decisions concerning delivery scheduling, task allocation, 
and  route  optimisation.  Companies  have  implemented  real-time  dashboards  to  visualise  driver 
location,  monitor  performance  metrics,  and  adjust  delivery  workflows  on  the  fly.  AI  applications 
often include machine learning models that predict delays and reallocate resources proactively48. 

Description of changes on: 

a) Employment relations: 

Employment relations have evolved under AM. Workers receive instructions directly from mobile 
apps and algorithms, bypassing traditional supervisors. Human interactions have been replaced by 
digital  commands  and  automated  feedback  loops.  The  resulting  disintermediation  weakens 
opportunities for negotiation and introduces uncertainty about how performance is judged49. ns 

b) Employment structure: 

In terms of employment structure, the rise of AM has led to job polarisation. While there is demand 
for IT specialists, data analysts, and platform technicians, low-skilled delivery roles have become 

47  

48  

ILO, 2024. 

ILO, 2024. 

49   Baiocco and Fernández-Macías, 2022. 

24 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

more precarious, and are often outsourced. AM facilitates platform-based work models, leading to 
a shift from standard employment to gig-like arrangements, even within traditional firms50. 

c) Working conditions 

Working conditions have become  increasingly  algorithm  driven.  Task pacing  is  tightly controlled, 
with little room for worker discretion. Algorithms assign routes that maximise efficiency but often 
overlook  human  factors  such  as  breaks,  traffic  stress,  or  urban  navigation  challenges.  This 
intensification  of  labour  is  compounded  by  constant  performance  monitoring  and  disciplinary 
triggers for low scores51. 

d) Health and safety 

Health and safety are impacted in both physical and psychological terms. On one hand, optimised 
routes can reduce fuel consumption and driver fatigue. On the other, rigid task execution and digital 
surveillance create anxiety, stress, and mental overload. Companies seldom include AM risks in their 
safety assessments, and workers report insufficient training on system usage52. 

Governance frameworks vary. Although many firms report having ethics guidelines, internal audits, 
and consultation protocols, workers often lack clarity on algorithmic processes. The opacity of AM 
systems  limits  accountability,  and  collective  bargaining  frameworks  struggle  to  adapt.  Legal 
instruments regulating transparency and fairness in AM are still under development 53. 

Algorithmic management in the French healthcare sector 

Description of sector main characteristics 

independent 
France's  healthcare  system  comprises  public  hospitals,  private  clinics,  and 
practitioners. The sector is heavily regulated and operates under strict professional codes and data 
protection  norms  (e.g.  GDPR).  Following  the  COVID-19  pandemic,  digital  health  tools  saw  rapid 
expansion.  Electronic  health  records,  digital  shift  systems,  and  triage  algorithms  have  become 
commonplace in clinical settings, creating fertile ground for AM54. 

Description of most recent/innovative AI/AM methodologies adopted 

Hospitals  increasingly  use  AM  platforms  for  staffing,  task  distribution,  and  operational  triage. 
Software  such  as  Kronos  supports  automated  scheduling  and  performance  dashboards.  In 
emergency departments, real-time allocation of medical staff to cases is done via AI-based triage. 
AM is also employed to track treatment compliance, patient throughput, and administrative KPIs55. 

50  

ILO, 2024. 

51   Baiocco and Fernández-Macías, 2022. 

52   Baiocco and Fernández-Macías, 2022. 

53   Urzì Brancati et al., 2022. 

54  

ILO, 2024. 

55   Baiocco and Fernández-Macías, 2022. 

25 

 
EPRS | European Parliamentary Research Service 

Description of changes on: 

a) Employment relations 

Employment relations are shaped by the logic of AM. Task assignments, shift rotations, and even 
leave  approvals  are  determined  algorithmically.  Staff  members  often  lack  insight  into  how  these 
decisions are made. Union representatives have flagged AM systems for undermining collaborative 
scheduling practices and introducing opacity in workload distribution56. 

b) Employment structure 

Employment structure has evolved to include new roles like digital operations officers, data privacy 
managers,  and  AI  system  supervisors.  Medical  professionals  are  also  expected  to  adapt  to 
dashboards,  alerts,  and  digital 
input  mechanisms.  This  shift  requires  reskilling  and  new 
competencies in data interpretation and platform usage57. 

c) Working conditions 

Working conditions are influenced by real-time alerts, performance indicators, and system-based 
time management. While such systems improve scheduling efficiency and transparency, they reduce 
the  worker's  ability  to  pace  tasks  or  make  discretionary  decisions.  Staff  often  report  feeling 
micromanaged and unable to engage with patients in a holistic way58. 

d) Health and safety 

Health and safety are affected in multiple ways. Improved task allocation can enhance emergency 
responsiveness and reduce overlapping duties. However, the cognitive load from managing both 
patients  and  digital  expectations  leads  to  burnout.  Alarm  fatigue,  screen  overuse,  and  constant 
monitoring contribute to mental stress, particularly among nurses and junior staff59. 

Governance  mechanisms  in  French  hospitals  include  staff  councils,  internal  audits,  and  ethical 
boards.  These  are  supported  by  national  regulations  on  digital  tools.  Nevertheless,  many  AM 
systems  operate  as  'black  boxes',  with  limited  explainability.  Transparency,  explainability,  and 
worker input into system design remain central challenges60. 

Algorithmic management in the German automotive sector 

Description of sector main characteristics 

The automotive sector in Germany is a critical pillar of the country's industrial landscape and export 
economy, with approximately 779 700 reported workers employed in the factories of manufacturers 
of motor vehicles and motor vehicle parts in 202361. Given the numerous challenges confronting the 
car industry, particularly the rapid technological advancements, the progression of digitalisation in 

56   Baiocco and Fernández-Macías, 2022. 

57  

ILO, 2024. 

58   Baiocco and Fernández-Macías, 2022. 

59   Baiocco and Fernández-Macías, 2022. 

60   Baiocco and Fernández-Macías, 2022. 

61   Verband der Automobilindustrie, 2024. 

26 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

this sector is inevitable. The transition towards electrification and autonomous vehicles has led to a 
significant increase in the use of digital technologies. Although the application of AI is a more recent 
development, with only around 9 % of German automotive companies declaring their use of AI in 
202362, it has many promising potential applications in the sector. Some German companies, such 
as Volkswagen, Mercedes-Benz as well as the multinational Stellantis, have explored the potential 
of AI in various segments of the value chain, including customer experience, research and innovation, 
and,  most  notably,  the  manufacturing  process63.  This  gradual  introduction  of  AI  necessitates  the 
adaptation of workplaces. While AI can increase productivity and efficiency, reduce workload and 
improve working conditions, it can also pose risks, such as reduced work autonomy, changes to the 
required skillset of workers and transparency challenges64. 

Description of most recent/innovative AI/AM methodologies adopted 

Audi has implemented an AI-based quality control system for spot welding during the automated 
body-in-white  assembly  stage  at  its  Neckarsulm  plant  in  Germany.  The  transition  from  manual 
ultrasound checks of approximately 5 000 welds per vehicle to automated inspection of around 1.5 
million welds over 300 vehicles each shift represents a significant enhancement in efficiency. The AI 
system  swiftly  identifies  anomalies,  allowing  human  operators  to  focus  on  more  complex  tasks, 
rather than conducting extensive manual checks65. The data generated through use of the AI can be 
used to optimise other processes in the future, for example, as a basis for predictive maintenance 
down the road. Even though there are currently no standards or certifications for AI applications, 
the process was designed to be audit- and certification-proof through coordination with the German 
Association  for  Quality  (DGQ),  the  Fraunhofer  Institute  for  Industrial  Engineering  (IAO)  and  the 
Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)66. 

Description of changes on: 

a) Employment relations 

In order to enable workers to be involved in the process when using AI in production in the future, 
Audi commissioned the Fraunhofer Institutes IPA and IAO to carry out an accompanying study with 
recommendations  for  action.  The  IAO  experts  interviewed  various  Audi  staff  –  specialists  in 
production, quality assurance, IT and managers about the potential and wishes they associate with 
the  use  of  AI.  External  certification  bodies  supplemented  the  internal  Audi  interviews.  The 
functionality and transparency of the safety of the AI system were seen as particularly important67. 

b) Employment structure 

Before AI was introduced, production staff would use ultrasound to manually monitor the quality of 
spot  welds  based  on  random  sampling.  Now,  AI  software  detects  anomalies  in  real  time  and 
engineers can monitor the weld data on a dashboard. Manual checks are only performed if a fault is 

62   Bundesministerium für Wirtschaft und Klimaschutz & Bundesministerium für Digitales, 2024. 

63   Ait Larbi et al., 2024. 

64   European Commission, 2025. 

65   Wirtschaftskraft, 2023. 

66   Metrology.news, 2023. 

67   Fraunhofer IPA, 2023. 

27 

 
EPRS | European Parliamentary Research Service 

detected. This may indicate a shift in the automotive sector, as automation is expected to reduce 
employment  in  production  while  creating  opportunities  in  monitoring,  programming,  and  data 
analysis. This could lead to a significant change in the required skill set, also for qualified workers, 
with  IT  and  electronics  knowledge  becoming  more  important  than  traditional  automotive 
qualifications,  consequently  leading  to  'deskilling'  or  job  cuts.  Although  some  tasks  may  become 
simpler with digital interfaces, many roles will become more complex and diverse, with AI potentially 
replacing human expertise in quality control and evaluation68.  

c) Working conditions 

Quality  control,  which  used  to  be  manually  supervised  by  a  specialist,  was  considered  a  tedious 
process.  In  future,  AI  will  support  this  process  by  evaluating  the  quality  of  spot  welds  based  on 
certain  process  parameters,  suggesting  a  preselection  of  conspicuous  spots  for  ultrasonic 
inspection. This should eliminate the time-consuming ergonomic assessment process and save a 
considerable amount of time69.  

d) Health and safety 

Using  AI  to  automate  manual  processes  can  free  up  workers  from  repetitive  and  sometimes 
physically  demanding  tasks  that  affect  their  physical  and  mental  wellbeing.  As  AI  works  with 
gathered data and conducts analyses that pinpoint specific issues, it can simplify the workload and 
free  up  time  for  tasks  that  require  innovative  thinking.  In  this  context,  the  wider  introduction  of 
automation  in  manufacturing  processes  promises  to  reduce  health  and  safety  risks  for  factory 
workers. For instance, Audi has also introduced 5G wireless technology into its production process, 
as part of a pilot project  involving the use of 5G in a safety network for robotic assembly. 5G is 
particularly well-suited to connecting sensors, machines, and devices operated by people, because 
it is both quick and reliable. Therefore, 5G can now perform tasks that could pose a risk to workers 
wirelessly, thus eliminating the danger. However, it should be noted that the digitalisation of the 
manufacturing  processes  may  create  new  risks  for  workers.  These  include  work  intensification, 
reduced worker autonomy, and constant connectivity pressures. These factors can lead to elevated 
stress levels and psychosocial concerns. Additionally, constant screen exposure may result in digital 
fatigue. This would require new OSH strategies to be implemented in workplaces70. 

Algorithmic management in the Danish telecom sector 

Description of sector main characteristics 

The Danish telecommunications sector is a highly developed and competitive industry, comprised 
mainly of four large operators, alongside international firms like Telia, Telenor, and Three71. This high 
level of digitalisation means telecom companies are at the forefront of adopting new technologies72. 
In  this  context,  AM  is  increasingly  being  introduced  to  optimise  processes.  The  Danish  telecom 

68   Meil, 2020. 

69   Ait Larbi et al., 2024. 

70   EU-OSHA and JRC, 2021. 

71   WIK consult, 2019. 

72  

IFS News, 2024. 

28 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

sector faces a well-organised labour force and a policy environment that emphasises collaboration 
between employers and unions in managing change. 

Description of most recent/innovative AI/AM methodologies adopted 

Telecom  firms  in  Denmark  have  begun  implementing  a  range  of  AI  and  algorithmic management 
tools  to  improve  efficiency  and  service  quality.  One  notable  innovation  is  in  field  service 
management  to  optimise  scheduling  of  field  technicians73.  The  system  automatically  assigns  the 
best-suited technicians to jobs and dynamically routes them based on real-time data like location 
and  traffic.  In  customer  service  and  call  centres,  companies  are  using  advanced  software  for 
scheduling, monitoring, and assisting staff. For example, Telia deployed an automated Workforce 
Management system integrated with its contact centre platform, enabling smarter shift scheduling 
and intraday adjustments74. Danish telecom operators are also embracing AI for quality control: calls 
to  customer  support  may  be  recorded  and  transcribed  by  machine-learning  speech  analytics 
systems. For instance, TDC's policy notes that customer calls can be automatically transcribed to 
analyse conversations, identify root causes of problems, and improve service quality75.  

Description of changes on: 

a) Employment relations 

The rise of algorithmic management is altering employment relations in the Danish telecom sector, 
prompting  both  concerns  and  collaborative  responses.  One  major  change  is  a  shift  in  trust  and 
transparency between workers and management76. Many telecom customer service workers report 
feeling  that  decisions  are  made  by  opaque  computer  systems  rather  than  through  dialogue77.  In 
response, Danish social partners have started negotiating guardrails for AI's use. Across the Danish 
labour market, trade unions and employers included discussions on AM/AI in the 2023 collective 
bargaining round78. Principles of transparency and co-determination are likely to be adopted.  

b) Employment structure 

AI  and  algorithmic  management  are  gradually  reshaping  job  roles  and  skill  needs  in  Denmark's 
telecom  sector.  For  example,  Telenor's  virtual  assistant  now  resolves  many  incoming  customer 
service requests79. As entry-level service tasks are digitised, companies are shifting focus toward 
higher-skilled positions in data analytics, AI development, and IT. While there are no explicit reports 
in  the  Danish  context,  Telia  Sweden  is  upskilling  its  workforce  to  meet  these  new  demands80. 
Algorithmic  management  is  introducing  a  more  data-driven  hierarchy  in  telecom  organisations. 
Danish  unions  are  vigilant  about  this  trend.  A  2023  union-led  conference,  'Når  din  chef  er  en 

73  

IFS News, 2024. 

74   Calabrio, 2025. 

75   TDC, 2025. 

76   FEPS, 2024. 

77   FEPS, 2024. 

78   Larsen et al., 2024, Ilsøe et al., 2024. 

79   Vux.world, 2025. 

80   Telecoms, 2022. 

29 

 
EPRS | European Parliamentary Research Service 

algoritme'81, highlighted the need for oversight: labour leaders discussed ensuring that algorithms 
serve as support tools rather than autonomous bosses82. 

c) Working conditions 

Algorithmic management is affecting day-to-day working conditions for Danish telecom employees. 
A  2024  survey  of  Danish  customer  service  workers  (including  telecom  support  staff)  found  that 
many report that this digital oversight has eroded job quality – citing lower trust, less autonomy at 
work,  and  higher  stress  levels  under  algorithmic  control83.  Likewise,  40 %  of  Danish  union 
representatives  (representing  a  share  of  Telecom  workers)  say  that  electronic  monitoring  or 
algorithmic management is negatively affecting their colleagues' work environment, and one in five 
reps have handled employee complaints about on-the-job surveillance84. These findings indicate a 
change in working conditions as AI-driven systems become embedded in telecom operations. 

d) Health and safety 

While  there  is no  explicit  documentation  of  changes  in  health  and safety  in  the  Danish  telecoms 
sector,  a  Nordic  survey  found  76 %  of  workers  in  customer  service  and  telemarketing  roles  (call 
centres), including in the telecoms sector, experience algorithmic management and report heavier 
workloads, higher stress, and lower autonomy85. 

81  

'When your boss is an algorithm'. 

82   FHO, 2023. 

83   csr.dk, 2024. 

84   HK, 2024. 

85   FEPS, 2024. 

30 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

3. Task 3 – Regulation of AI and AM  

The use of AM tools in the workplace bears significant potential to support workers' productivity, 
improve  their  working  conditions  and  physical  safety  and  enhance  health  risks  management. 
However,  as  highlighted  above,  these  systems  may also  pose  numerous  challenges  and  risks  for 
employment relations.  

From  a  legislative  perspective,  these  opposing  interests  call  for  a  balanced  approach  between 
limiting  regulatory  interventions  to  encourage  innovation  and  economic  growth,  in  line  with  the 
objectives  of  the  European  Competitiveness  Compass  and  ensuring  a  higher  level  of  workers' 
protection  to  safeguard  their  fundamental  rights,  wellbeing  and  participation  in  an  increasingly 
digitalised workspace. 

The EU 2018 AI Strategy86 and the Platform Work Directive87 (PWD) set out two objectives in relation 
to the deployment and use of AI/AM: increasing the European market's competitiveness through 
new  technologies  while  upholding  fundamental  rights  and  EU  values  through  trustworthy 
technologies that are safe and reliable for individuals. 

In the 2020 White Paper on Artificial Intelligence88, the Commission endorsed the seven principles 
for the regulation of trustworthy AI outlined by the High-Level Expert Group89: technical robustness 
and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; 
societal  and  environmental  wellbeing;  and  accountability.  A  feedback  process  on  the  High-Level 
Expert Group's draft Guidelines on Trustworthy AI organised by the same indicated that, while many 
these  principles  are  reflected  in  existing  legal  or  regulatory  regimes,  others  are  not  specifically 
covered in many economic sectors90. Consequently, the AI Act91 gives legal effect to many of these 
principles. 

This section analyses the existing regulatory framework for the use of AI/AM technologies in the 
workplace, with the aim to assess whether existing legal acts and social partner agreements provide 
sufficient protective mechanisms and safeguards for workers, and where gaps may exist. Section 3.1 
provides an overview of relevant EU legislation and ongoing legislative initiatives for regulating AI 
and AM technologies in the workplace. Starting with the digital acquis (Section 3.1.1.), the section 
also maps the existing EU labour and occupational safety and health (OSH) acquis (Section 3.1.2.), 
before  concluding  by  focusing  on  the  Platform  Work  Directive  -  the  only  existing  piece  of  EU 
legislation to bridge the two legal frameworks (Section 3.1.3.). Section 3.2. provides an analysis of 
these mapped legal instruments to show how they address specific impacts and risks connected to 
the  deployment  of  AM  technologies  in  the  workplace.  Section  3.2.  also  explores  the  regulatory 
framework  and  case-law  at  national  level  in  the  six  selected  Member  States  for  which  country-

86   Communication on Artificial Intelligence for Europe, COM/2018/237 final, European Commission, April 2018. 

87   Directive (EU) 2024/2831 of 23 October 2024 on improving conditions in platform work. 

88   European Commission, 2020, On Artificial Intelligence.  

89   EC High-Level Expert Group on AI, Ethics Guidelines for trustworthy AI, 2019.  

90   European Commission, 2020, On Artificial Intelligence. 

91   Regulation (EU) 2024/1689.  

31 

 
EPRS | European Parliamentary Research Service 

specific  research  has  been  carried  out  in  the  context  of  this  study:  France,  Germany,  the 
Netherlands, Poland, Spain and Sweden. 

3.1. Regulatory  framework  applicable  to  AI  and  AM 
workplace 

in  the 

3.1.1. EU digital acquis 

Background to the EU digital acquis  

The EU digital acquis is primarily marker oriented, regulating the removal of barriers to cross-border 
digital trade and services, often combined with the need to protect specific fundamental rights. The 
following  paragraphs  present  a  selection  of  legislation  from  the  EU  digital  acquis  setting  out 
protective  mechanisms  (see  Section  3.2),  in  line  with  some  of  the  above-mentioned  guiding 
principles, that are relevant, due to their general scope, to the use of AM systems in the workplace. 
These instruments are:  

First, the General Data Protection Regulation (GDPR)92,93 and the e-privacy Directive94 which set out 
safeguards for the protection of privacy and personal data in line with Article 16 of the Treaty on the 
Functioning of the European Union (TFEU). 

Second, the EU AI Act, providing for a general and uniform legal framework for the development, 
the circulation, and the use of AI systems within the internal market, including in workplaces95. The 
Commission has announced the withdrawal of its proposal for an AI liability directive96, which aimed 
to harmonise the legal framework for civil liability claims relating to AI97.  

In  the  same  vein,  the  recent  Framework  Convention  on  Artificial  Intelligence  of  the  Council  of 
Europe98 aligns with the EU regulation's risk-based approach to AI and its seven guiding principles.  

The box below outlines the provisions in the Treaty that grant the EU the authority to adopt the 
listed legal documents (i.e. the legal basis). 

92   Regulation (EU) 2016/679. 

93   As part of the fourth Simplification Omnibus package, the Commission has proposed exempting small mid-caps 

(SMCs) and organisations with fewer than 750 employees from GDPR record-keeping obligations, with the aim of 
streamlining rules and reducing administrative burdens. See: Simplification measures to save EU businesses €400 
million annually.  

94   Directive 2002/58/EC. 

95   Recital 1 of the AI Act. 

96   European Commission, COM/2022/496 final. 

97   See: AI liability directive.  

98   Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of 

Law, 2024.  

32 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Legal basis for EU digital acquis  

There is no legal basis establishing a special EU competence to regulate digital tools as such in the Treaties. 
However, as the influx of technologies has the potential to affect all aspects of the EU economy, including 
forms  of  employment  and  work  relationships,  several  existing  legal  bases  can  support  the  adoption  of 
specific or sectoral legislation affected by those technologies.  

Based on the selected legislation constituting the leading instruments of the EU digital acquis described 
below,  two  legal  bases  particularly  stand  out:  Article  16  TFEU,  on  the  protection  of  personal  data,  and 
Article 114 TFEU on the approximation of laws for the fulfilment of the internal market. Both these bases 
are  grounded  on  the  ordinary  legislative  procedure  (Articles  289  and  294,  TFEU)  involving  both  co-
legislators. 

Some instruments of the EU digital acquis contain provisions suggesting the possibility for the EU 
to further regulate the deployment of AM through tertiary legislation or to adopt more detailed acts 
and guidelines. Finally, although the GDPR and the AI Act are generally directly applicable in Member 
States,  national  legislators  have  limited  discretion  to  specify  additional  rules  and  mechanisms, 
including in work-related matters. 

The box below lists a few of the provisions that give the EU or the Member States further regulatory 
options.  

33 

 
EPRS | European Parliamentary Research Service 

Regulatory choice provisions in the GDPR 

•  Article 88(1) of the GDPR allows Member States to adopt, by law or collective agreements, more 
specific rules on a non-comprehensive list of employment matters to secure employees' rights 
and freedoms when their personal data are processed in a working context.  This list  expressly 
includes  the  processing  of  personal  data  for  the  purpose  of  management,  planning  and 
organisation of work, the performance of the employment contract, health and safety at work, 
and the termination of the employment relationship. 

Regulatory choice provisions in the AI Act 

•  Article 2(11) of the AI Act establishes a no-prejudice clause for current or future EU or national legislation or 
collective agreements that are more favourable to workers and workers’ rights in relation to the of use of 
AI systems by employers. 

• 

• 

In  the  light  of  the  AI  Act  risk-based  approach,  Article  6(2)  qualifies  certain  AI  systems  as  'high-risk'  by 
reference to Annex III. This Annex lists the different AI systems considered as high risks for safety, health 
and  fundamental  rights.  Point  4  of  this  Annex  identifies  certain  AI  systems  in  the  field  of  employment, 
workers management and access to self-employment as high-risk AI systems. 

 Articles 7(1) and 97 of the AI Act give the Commission a delegated competence to amend Annex III to add 
or modify the list of use-cases of high-risk AI systems if (a) the AI systems are intended to be used in any of 
the areas listed in Annex III, including employment, workers management and access to self-employment; 
and (b) the AI systems pose a risk of harm to health and safety, or an adverse impact on fundamental rights, 
and that risk is equivalent to, or greater than, the risk of harm or of adverse impact posed by the high-risk AI 
systems already referred to in Annex III.  

•  Article 6(7) and 97 of the AI Act also give the Commission a delegated competence to amend and delete any 
of  the  conditions  laid  down  in  Article  6(3)  regulating  the  derogation  to  the  qualification  of  high-risk  AI 
system listed in Annex III. 

• 

Articles 6(5) and 96 AI Act allow the Commission to adopt guidelines on various practical aspects of the AI 
Act’s implementation. Article 6(5) particularly focuses on high-risks AI systems by requiring the Commission, 
after consulting  the  European  Artificial  Intelligence  Board,  and  no  later  than  2  February  2026,  to  adopt 
guidelines on the practical implementation of the conditions under which AI systems listed in Annex III can 
be  exceptionally  considered  non-high-risk.  Recital  53  further  clarifies  that  these  guidelines  should  be 
completed by a comprehensive list of practical examples of high-risks and non-high risks use cases. 

Scope and definitions 

Personal scope: As previously mentioned, this selected set of legal acquis establishes frameworks 
tailored to digital markets or the use of digital components in economic activities. Although some of 
these acts contain references to workers or employment aspects, none of the measures apply to, or 
define workers. However, as employers and their service providers are business operators, they can 
be subject to the obligations laid down in these legal acts under their specific terminology. Similarly, 
workers may benefit from protective mechanisms or be affected by the regulated operations. 

The table below outlines the variations in terminology that can affect workers and employers when 
AM tools are used in a working environment. 

34 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Table 5 – Overview of the terminology relevant to determine the scope 

SELECTED 
LEGISLATION 

WORKERS 

EMPLOYERS 

OTHER 
INTERMEDIARIES 

SOCIAL 
PARTNERS 

AI Act 

European 
Framework 
Convention on AI 

The Regulation 
does not define 
the terms of 
'workers' or 
'affected persons'. 
However, all 
categories of 
workers, including 
solo self-
employed can be 
considered 
'affected persons' 
(Article 1) in the 
context of an 
employment 
relationship where 
the employer uses 
AM systems.  

The Framework 
Convention 
broadly applies to 
'the activities 
within the lifecycle 
of artificial 
intelligence 
systems that have 
the potential to 
interfere with 
human rights, 
democracy and 
the rule of law'.  

It does not define 
or specifically 
apply to workers. 
However, it 
requires its Parties 
to set out 
provisions that 
could affect 
workers, given 
that the use of AM 

All employers in 
both the public 
and private 
sectors using AI 
systems for the 
management of 
workers can be 
qualified as 
'deployers of AI 
systems' (Article 
3(4)). 

Employers may 
also qualify as 
'providers' if they 
develop their own 
AI systems or a 
General-Purpose 
AI Model (GPAI 
model) and put it 
into service 
(Article 3(3) and 
(11)). 

The Framework 
Convention 
broadly applies to 
'the activities 
within the lifecycle 
of artificial 
intelligence 
systems that have 
the potential to 
interfere with 
human rights, 
democracy and 
the rule of law'.  

It does not define 
or specifically 
apply to 
employers. 
However, it 
requires its Parties 
to set out 
provisions which 
can generate 
obligations for 

Depending on 
their activities, 
employers' service 
providers can 
qualify as any of 
the 'operators' 
listed in the 
regulation, other 
than deployers 
(Article 3(8)). 
These include: 
provider, product 
manufacturer, 
authorised 
representative, 
importer, and 
distributor of AI 
systems.  

The Framework 
Convention 
broadly applies to 
'the activities 
within the lifecycle 
of artificial 
intelligence 
systems that have 
the potential to 
interfere with 
human rights, 
democracy and 
the rule of law'.  

It does not define 
or apply 
specifically to 
intermediaries. 
However, it 
requires its Parties 
to set out 
provisions that 
could generate 
obligations for 

While the AI Act 
insists on workers' 
rights to 
information and 
consultation 
(Recital 92), it 
does not define 
'social partners', 
'trade unions' or 
'workers' 
representatives' as 
such in its 
personal scope 
and does not set 
out direct 
obligations 
applicable to 
social partners. 

The Framework 
Convention does 
not directly define 
or apply to 'social 
partners', 'workers' 
representatives' or 
'trade unions'. 

However, it 
requires its Parties 
to set out 
provisions on 
multistakeholder 
public consultation 
(Article 19). 

35 

 
 
EPRS | European Parliamentary Research Service 

SELECTED 
LEGISLATION 

WORKERS 

EMPLOYERS 

OTHER 
INTERMEDIARIES 

SOCIAL 
PARTNERS 

has the potential 
to interfere with 
their rights. 

employers, given 
that the use of AM 
has the potential 
to interfere with 
employees' rights. 

GDPR 

The Regulation 
does not define 
the term 'worker'. 
However, workers, 
including solo self-
employed, qualify 
as 'data subjects' 
when their 
employer 
processes their 
personal data in 
the context of 
their employment 
relationship.  

All employers, in 
both the public 
and private 
sectors, can 
qualify as 
'controllers'99 of 
their workers' 
personal data 
(Article 4(7)).  

intermediaries, 
given that the use 
of AM has the 
potential to 
interfere with 
users' and 
employees' rights. 

Depending on the 
relationship 
between 
employers and 
their service 
providers, the 
latter may qualify 
as 'processors' if 
they process data 
on behalf of the 
employer (Article 
4(8))100 or even 
'joint controllers' 
(Article 26)101, if 
they would 
participate in the 
determination of 
the purposes (and 
means) of the 
processing of 
workers' personal 
data102. Joint 
controllership can 
also exist if the AI 
providers would 
have their own 
economic interest 
in providing 
specific AM 

The Regulation 
does not directly 
define or refer to 
'social partners', 
'workers' 
representatives' or 
'trade unions'. 

However, the 
GDPR gives 
Member States 
the possibility to 
develop more 
specific rules for 
personal data 
processing in the 
employment 
context through 
social dialogues 
and collective 
agreements 
(Recital 155 and 
Article 88). 

99   A controller is a natural or a legal person, public authority, agency or other body who alone or jointly with others 

determines the purposes and means of the processing of personal data (Article 4(7) GDPR). 

100   A processor is a natural or legal person, public authority, agency or other body which processes personal data on 

behalf of the controller (Article 4(8) GDPR). 

101   For the explanation of these concepts please see EDPB Guidelines 07/2020 on the concepts of controller and 

processor in the GDPR. 

102   See, for example, Judgment of the Court (Grand Chamber), 5 June 2018, Wirtschaftsakademie Schleswig-Holstein, 

Case C-210/16, ECLI: EU:C:2018:388. 

36 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

SELECTED 
LEGISLATION 

WORKERS 

EMPLOYERS 

OTHER 
INTERMEDIARIES 

SOCIAL 
PARTNERS 

tools 103 and even 
regardless of any 
formal agreements 
or direct access to 
data 104.  

Source: Authors' elaboration. 

Digital technologies covered: As market legislation, the selected legislation listed above covers a 
wide range of technologies, including AI tools and AM systems. 

The AI Act is the primary legal framework for regulating AI. It broadly applies to all types of AI105 and 
general-purpose  artificial  intelligence  (GPAI)106  systems,  including  those  used  in  employment 
matters. The definitions laid down in Article 3 of the AI Act ensure a broad coverage of all types of 
AI technologies that can be deployed in workplaces, as described in Sections 1 and 2. Moreover, 
neither the AI Act nor the PWD envisage direct overlap between each other as none of these legal 
instruments contain a specific non-prejudice clause in favour of the other. However, the PWD sets 
out that it shall be without prejudice to any other rights conferred on persons performing platform 
work by other legal acts of the Union107. This suggests that the safeguards laid down in the AI Act 
will also apply in cases where platform work involves AM systems. It follows that the AI Act leaves 
very few gaps in its material scope as it is broad enough to cover all sorts of AM tools used in a 
working context. 

The  AI  Act  is  rooted  in  a  risk-based  approach  laying  down  different  sets  of  rules  for  prohibited 
practices (Chapter II), high-risk AI systems108 (Chapter III) and AI systems presenting more limited 
risks for safety health and fundamental rights (Chapter IV). It also contains rules on GPAI (Chapter 
V). In this taxonomy, AM systems used in the context of employment, workers management and 
access  to  self-employment  are  explicitly  considered  as  high-risk  AI  systems109,  subject  to  more 
stringent  rules  and  oversight  when  they  are  used  for  the  recruitment,  the  selection  and  the 
evaluation  of  candidates  for  a  position,  to  analyse  and  filter  job  applications,  to  make  decisions 

103   See, for example, Judgment of the Court (Second Chamber), 29 July 2019, Fashion ID, Case C-40/17, ECLI: 

EU:C:2019:629. 

104   See, for example, Judgment of 7 March 2024, IAB Europe, Case C-604/22, ECLI:EU:C:2024:214. 

105   Broadly defined under Article 3(1) of the AI Act as machine-based systems, designed to operate with varying levels 
of autonomy and adaptiveness, and that, for explicit or implicit objectives, infers, from the input it receives, how to 
generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual 
environments. 

106   Defined under Article 3(6) of the AI Act as AI models, including those that are trained with a large amount of data 
using self-supervision at scale, that displays significant generality and can perform a wide range of distinct tasks 
regardless of the way the model is placed on the market. Those tools can, by definition, be integrated into a variety 
of downstream systems or applications. 

107   Article 26(3) of the PWD. 

108   Note that the AI Act refers to the term high-risk AI systems (in short, AIS). For legal accuracy, this term is therefore 

used in this chapter when directly referring to the provisions of the AI Act.  

109   See Article 6(2) and Annex III, point 4 of the AI Act.  

37 

 
EPRS | European Parliamentary Research Service 

affecting terms of work-related relationships, for the promotion or termination of an contractual 
employment  relationship,  to  allocate  tasks  based  on  individual  behaviour  or  personal  traits  or 
characteristics  or  to  monitor  and  evaluate  the  performance  and  behaviour  of  persons  in  such 
relationships. The scope of the AI Act also closes potential gaps by covering AI systems used in high-
risk areas, even when released under free or open-source licenses110. Although this category of AI 
systems is, in principle, excluded from the scope of the regulation, its deployment in high-risk areas 
such as AM systems at work means it falls within the scope of the regulation111.  

In the same vein, the European Framework Convention on AI aligns its definition of AI with the EU 
AI Act112 and broadly covers 'the activities within the lifecycle of artificial intelligence systems that 
have the potential to interfere with human rights, democracy and the rule of law'113. This Framework 
Convention  has  been  signed  by  sixteen  contracting  parties,  including  the  European  Union114. 
However, it has not entered into force yet. As a framework convention, it lays down key obligations 
and  principles  that  contracting  parties  are  required  to  implement  effectively  through  legislative, 
administrative  or  practical  measures.  These  obligations  aim  to  safeguard  fundamental  rights  and 
democratic processes throughout the conception and deployment of AI systems. It also encourages 
contracting  parties  to  take  a  'graduated  and  differentiated'  approach  based  on  the  severity  and 
probability of adverse human rights impacts arising from AI systems115. 

Aside  from  these  AI-centred  legal  frameworks,  the  other  selected  regulations  do  not  focus  on 
specific AI systems or AI-powered technologies per se, remaining technologically neutral. Instead, 
they rather provide for additional safeguards and mechanisms in the context of the provision of AI-
powered services or AM systems provided to employers for their deployment in the workplace. For 
instance, the GDPR applies broadly to all processing operations, whether by automated means or 
not (Article 4(2)). However, it only applies to operations performed on personal data116, and not to 
anonymous  data117  such  as  aggregated  data  and  statistics,  which  are  usually  used  for  model 
learning118,119. Non-personal data are not governed by the GDPR, but are instead regulated by several 
EU instruments, such as Regulation (EU) 2018/1807 on a framework for the free flow of non-personal 
data, the Data Governance Act and the Data Act, among others. The aim of these instruments is not 
to protect fundamental rights, but rather to ensure that non-personal data can circulate freely, be 
shared safely, and fuel innovation. 

110   Article 2(12) of the AI Act.  

111   See Article 2(12) of the AI Act.  

112   See Article 2 of the Framework Convention. 

113   Article 3(1) of the Framework Convention. 

114   For a complete list of signatories see: Council of Europe, The Framework Convention on Artificial Intelligence. 

115   Article 1(2) of the Framework Convention. 

116   Article 4(1) GDPR defines personal data as information relating to an identified or identifiable natural person. 

117   Anonymous data is any information relating to a natural person where the person can no longer be identified. 

118   European Economic and Social Committee, A guide to Artificial Intelligence at the workplace, 2022, p. 8.  

119   The EDPB states that AI models can be trained using personal and non-personal data. However, when AI models are 

trained using personal data, they cannot always be considered anonymous. See EDPB, Opinion 28/2024. 

38 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

In conclusion, while the EU digital acquis applies to the deployment and use of AI and AM systems 
in the EU, as internal market instruments, they do not focus on regulating employment contexts. 
While  the  AI  Act  classifies  AM  systems  as  high-risk  AI  systems  and  the  GDPR  provides  privacy 
safeguards, neither instrument defines workers nor establishes direct labour law protections. Also, 
social partners are not recognised as actors under these legal instruments.  

3.1.2. EU labour and OSH acquis 

While  the  use  of  digital  technologies  in  the  workplace  leads  to  the  automation  of  employers' 
functions,  this  change  may  not  necessarily  require  legal  amendments,  provided  that  existing 
protective mechanisms can be interpreted to encompass this change. This section explores to what 
extent the existing EU labour law acquis could be used in situation of AM in the workplace. 

Background to the EU labour and OSH acquis 

The  European  Union's  competence  in  the  field  of  employment  policy  finds  it  legal  foundation  in 
Article  3(3)  of  the  Treaty  on  European  Union  (TEU),  establishing  the  Union's  objective  to  work 
towards full employment and social progress. 

Legal basis for EU labour and OSH acquis  

Complementing Article 3 of the TEU, Article 9 of the TFEU introduces a horizontal clause requiring that the 
definition and implementation of EU policies must aim to achieve high levels of employment. In line with 
Article 145 TFEU, both the EU and the Member States are called upon to pursue a coordinated employment 
strategy, with a particular focus on fostering a skilled, trained, and adaptable workforce, and on developing 
labour  markets  that  can  respond  effectively  to  economic  change.  In  addition,  Articles  151  to  153  TFEU 
provide the legal basis for EU action in supporting and complementing Member States’ efforts on social 
rights,  dialogue  with  social  partners,  living  and  working  conditions,  protection  of  workers’  health  and 
safety,  equality  between  men  and  women  in  employment  and  others,  thereby  reinforcing  the  Union’s 
commitment to promoting inclusive and sustainable labour markets. Finally, Article 157 TFEU enables EU 
to guarantee equality between men and women at work and in pay. 

Core labour rights forming the basis of the Union’s social and employment policy are also contained in the 
Charter of Fundamental Rights (Charter). Key provisions include the prohibition of discrimination (Article 
21), the equality between women and men (Article 23), the right of workers to be informed and consulted 
within the undertaking (Article 27), the right to collective bargaining and action (Article 28), and protection 
in the event of unjustified dismissal (Article 30). The Charter also affirms the right to fair and just working 
conditions (Article 31), reinforcing the EU’s commitment to promoting dignity, equality, and protection for 
all workers across Member States. 

The  EU  labour  acquis  comprehends  a  number  of  EU  legal  instruments  that  lay  down  common 
minimum standards for employment across the EU. In that sense, it acts as the basic legal framework 
that national labour law provisions shall meet while Member States have the possibility to adopt 
rules  that  provide  a  higher  level  of  protection  to  workers.  It  mainly  consists  of  directives  and 
regulates aspects such as working conditions, health and safety at work, information, consultation 
and employee participation, and non-discrimination in relation to employment.  

A first cluster of EU labour law directives sets out minimum rights in relation to, for instance, working 
time,  mandatory  rest  periods,  leave  and contractual  arrangements.  It  also  includes provisions  on 

39 

 
EPRS | European Parliamentary Research Service 

transparency and minimum wage. A second set of directives regulates information, consultation and 
the  participation  of  employees,  e.g.  in  European  Works  Councils  or  in  relation  to  collective 
redundancies. In relation to occupational safety and health (OSH) the OSH Framework Directive120, 
adopted in 1989, remains the cornerstone of EU law in this area as it establishes EU minimum OSH 
requirements across different sectors. Under Article 16(1) of the OSH Framework Directive, a series 
of 'daughter' directives focusing on specific aspects of health and safety (e.g., protective measures 
such as personal equipment), or risks (e.g., exposure to noise or vibrations), or targeting sectors 
(e.g., constructions sites or mineral extraction) have been subsequently enacted. OSH directives are 
centred on the principle of prevention of risks121, forming the basis of the employers' obligations to 
take  measures  necessary  to  anticipate  and  prevent  risks,  provide  information  and  training  to 
workers122, as well as conducting regular risks assessment and take tailored protective measures as 
needed123.  

Finally, the labour acquis also integrates legislation on equality and non-discrimination in relation to 
employment, which prohibits discrimination on grounds of religion, race or ethnicity, disability, age 
or sexual orientation. For the overview of the most relevant labour and OSH instruments in relation 
to AM at the workplace, see the table below. 

Table 6 – Overview of selected EU labour instruments 

EMPLOYMENT 
RELATIONSHIP 

EMPLOYMENT 
STRUCTURE 

WORKING 
CONDITIONS 

HEALTH AND SAFETY 
AT WORK (OSH) 

•  Working Time 
Directive 
(2003/88/EC)  

•  Directive on 
Collective 
Redundancies 
(98/59/EC) 

•  European Works 
Council Directive 
(2009/38/EC) 

• 

Information and 
Consultation 
Directive 
(2002/14/EC) 

•  No specific EU 
instruments 124  

•  Directive (EU) 
2019/1152 on 
Transparent and 
Predictable 
Working Conditions 

•  Work-Life Balance 
Directive (EU) 
2019/1158 

•  Pay Transparency 
Directive (EU) 
2023/970 
(transposition 
deadline 7 June 
2026) 

•  Platform Work 
Directive (EU) 

•  OSH Framework 

Directive 
89/391/EEC  

•  Workplace 
Directive 
(89/654/EEC)  

•  Display Screen 
Directive 
(90/270/EEC) 

•  Work Equipment 

Directive 
(2009/104/EC) 

120   Council Directive 89/391 EC. 

121   Article 6 of the OSH Framework Directive. 

122   See note 38, page 34. 

123   Article 10. 

124   Though it is important to note that several policy initiatives exist in relation to workforce skill evolution and other 

aspects of employment structure, such as the European Skills Agenda and the recent 2025 'Union of Skills' package, 
focused on developing skills adapted to future needs, lifelong learning and skills portability, among others. For more 
information, see, for instance, Union of skills - Investing in people for a competitive European Union.  

40 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

•  Platform Work 
Directive (EU) 
2024/2831 
(transposition 
deadline 2 
December 2026) 

2024/2831 
(transposition 
deadline 2 
December 2026) 

EQUAL TREATMENT AND ANTI-DISCRIMINATION 

•  Employment Equality Directive (2000/78/EC) 

•  Gender equality in employment and occupation Directive (2006/54/EC) 

Source: Authors' elaboration. 

These legal acts are complemented by social partners' initiatives such as the collective agreement 
on  digitalisation125,  improving  workers'  collective  bargaining  strengths  in  line  with  their  rights  to 
consultation and participation as well as by non-binding instruments126. Finally, effective guaranties 
and safeguards to protect workers rights could also be put in place under national and international 
law.  

Scope and definitions 

Personal  scope.  The  EU  legislation  on  working  conditions  typically  applies  to  'workers'.  The 
definition  of  a  worker  is  largely  left  to  Member  States'  legal  labour  traditions.  As  a  result,  the 
concepts  of  'employee',  'employment  relationship'  and  'employment  contract'  can  differ  across 
jurisdictions.  This  can  result  in  certain  types  of  workers  outside  of  standard  employment 
relationships being excluded from the scope of EU directives on working conditions127.  

The CJEU has significantly contributed to determining what constitutes a 'worker' under EU law, 
determining, for instance, that the defining characteristics of that relationship are that 'for a certain 
period of time, a person performs services for and under the direction of another person in return 
for  remuneration'128.  Based  on  the  case  law  of  the  CJEU,  a  person  is  a  worker  if  all  three  of  the 
following  elements  are  present:  (i)  the  performance  of  services;  (ii)  subordination;  and  (iii) 
remuneration. 

More recent legal instruments, such as the Work-Life Balance Directive129 and the Pay Transparency 
Directive130, reflect the effort to enlarge the scope of protection provided by the EU legislation on 
working  conditions.  These  legal  instruments  cover  'all  workers,  men  and  women,  who  have  an 

125   BusinessEurope, SMEunited, CEE, ETUC (and the liaison committee EUROCADRES/ CEC), European social partners 
framework agreement on digitalisation, 2020, European social partners framework agreement on digitalisation | 
BusinessEurope. 

126   For example, the 2023 ILO Digital Employment Diagnostic Guidelines provide a roadmap for policymakers, 

researchers, statisticians and practitioners to gather accurate and reliable data, measure the impact of digitalisation 
on employment and develop evidence-based policies that can effectively address emerging issues and ensure 
decent working conditions for all workers. 

127   Szpejna and Boudalaoui-Buresi, 2020. 

128   CJEU C-66/85, Lawrie-Blum v Land Baden-Württemberg, para. 17.  

129   Directive (EU) 2019/1158 .  

130   Directive (EU) 2023/970 .  

41 

 
EPRS | European Parliamentary Research Service 

employment contract or employment relationship as defined by the law, collective agreements or 
practice in force in each Member State, taking into account the case-law of the Court of Justice'131. 
Moreover, the Platform Work Directive132 provides a rebuttable presumption of a labour relationship 
with  the  person  providing  work  through  a  digital  platform,  even  where  the  relationship  is  not 
designated  as  such  by  the  parties  involved133.  Still,  it  is  important  to  note  that  genuinely  self-
employed persons are not covered by the EU labour acquis. Provided that the above three criteria 
are fulfilled, (solo) self-employed could fall within the scope of the relevant directives. 

Conversely, OSH directives set out a definition of 'workers' as 'any person employed by an employer, 
including trainees and apprentices but excluding domestic servants'134. This term is a broader one, 
encompassing a wide range of employment situations and it is aimed at covering a wider group of 
individuals in the workplace. Still, genuinely self-employed persons are in principle not covered. 

Digital technologies covered. As for their material scope, the EU labour and OSH directives do not 
regulate digital technologies nor AM systems135. This is logical, given that the core EU directives were 
adopted before digital technologies such as AI became widespread in the workplace. 

While these directives do not explicitly regulate the use of technological tools to automate tasks 
traditionally carried out by human managers, they do contain several provisions or interpretations 
that can be applied to digital or electronic monitoring and communication. For example, Recital 24 
of the Directive on Transparent and Predictable Working Conditions136 recognises the increasing use 
of  digital  communication  tools  and  confirms  that  when  information  should  be  communicated  in 
writing, this would also include communication by electronic means. Likewise, the OSH Framework 
Directive requires the employer to draw up a risk assessment, systematically assessing potential 
OSH risks in the workplace and to keep such assessments up to date137. This includes an assessment 
of the consequences on workers' health and safety resulting from the planning and introduction of 
new technologies, as required by Article 6(3) OSH Framework Directive138. 

3.1.3. Platform Work Directive (PWD) 

The  distinct  challenges  of  the  use  of  AM  in  the  workplace  have  led  to  calls  for  sector-specific 
regulation, which led in 2024 to the adoption of the Directive (EU) 2024/2831 on improving working 
conditions in platform work (also commonly referred to as the Platform Work Directive or the PWD).  

131   For example, see Article 2 of the Work-Life Balance Directive.  

132   Directive (EU) 2024/2831. 

133   Article 5 of the Platform Work Directive. 

134   Article 3(a) of the OSH Framework Directive.  

135   Except for the Platform Work Directive, see below section. 

136   Directive (EU) 2019/1152. 

137   Article 6 of the OSH Framework Directive.  

138   The EU Strategic Framework on Health and Safety at work 2021-2027 explicitly recognises robotisation and AI as 

providing great opportunities for OSH implementation, but also presenting a number of risks. 

42 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Background  to  the  PWD  and  specific  issues  related  to  AM  in  the  context  of 
platform work 

The Platform Work Directive regulates working conditions for a specific group of workers performing 
platform work through a digital labour platform (i.e. persons performing platform work), with a view 
to address some of the challenges they face as a consequence of the digital transition. The group of 
workers that work through digital labour platforms are increasingly rapidly and were, already at the 
time of the adoption of the Directive proposal, estimated to reach 43 million in 2025139. 

The PWD aimed to tackle a number of issues, such as the employment status of different types of 
persons  performing  platform  work,  in  particular  under  which  circumstances  they  could  be 
considered self-employed or rather in an employment relationship, but also aspects related to AM. 
According  to  Recital  5  of  the  PWD,  '[b]y  means  of  the  algorithms,  the  digital  labour  platforms 
organise, to a lesser or greater extent depending on their business model, the performance of the 
work, the remuneration for the work and the relationship between their customers and the persons 
performing  the  work'.  Among  others,  PWD  intended  to  address  the  insufficient  transparency 
regarding the use of such AM systems by digital platforms as well as little access to remedies against 
the decisions taken by such systems.  

Legal basis for PWD 

The Platform Work Directive was adopted on the basis of Article 153(1)(b) TFEU which allows the EU to 
establish minimum standards with the objective of improving working conditions and Article 16(2) TFEU 
on data protection. 

Scope and definitions 

Personal scope. The PWD applies specifically to people providing work through platforms. Platform 
work is defined as work organised in a triangular relationship 'through a digital labour platform on 
the basis of a contractual relationship between the platform or an intermediary, the individual and a 
recipient of the service'140. The relationship between the individual performing the work (often a solo 
self-employed)  and  the  intermediary  can  be  identified  as  a  labour  relationship  when  certain 
elements are present, irrespectively of the nature of the contractual relationship or how the parties 
designate the relationship between them. Despite widespread deployment of AM at the workplace, 
the personal scope of the PWD remains limited to the digital platform economy. 

Digital technologies covered  

According to the PWD, AM systems are systems powered by algorithms, which increasingly replace 
managerial  functions  by  taking  or  supporting  relevant  decisions, such  as  allocating  tasks,  pricing 
individual  assignments,  determining  working  schedules,  giving  instructions,  evaluating  the  work 
performed,  providing  incentives  or  applying  adverse  treatment141.  These  systems  customarily 

139   Proposal for a Directive on improving working conditions in platform work, COM(2021)762 final. 

140   Article 2(1)(b) of the PWD. 

141   Recital 8 of the PWD. 

43 

 
 
EPRS | European Parliamentary Research Service 

process  personal  data  of  persons  performing  platform  work.  The  PWD  sets  out  a  number  of 
limitations and requirements regarding AM, defined in the context of the Directive as automated 
monitoring and decisionmaking systems. To that end, it establishes a number of specific protection 
mechanisms  and  enforcement  provisions,  including  remedies,  aiming  to  protect  the  working 
conditions of persons working through a platform and protecting their personal data.  

3.2. Key provisions and mechanisms to protect workers related to 
AM in the workplace 

3.2.1. Information provisions and transparency  

Workers subject to AM often do not have access to information on how such digital technologies 
work, which personal data and other information are used or how their behaviour affects decisions 
taken by such tools. Further, such information is very often not available to workers' representatives, 
labour inspectorates or other competent authorities. Mechanisms that increase the transparency in 
the use of AM at workplace, such as the right to information and the right to explanation help to 
mitigate this risk. 

Labour law and OSH obligations  

The general framework for workers' right to information is regulated in the following instruments: 

The Information and Consultation Directive142 focuses on the collective right to information (and 
consultation)  that  should  in  principle  be  given  to  employees'  representatives143  in  undertakings 
employing at least 50 employees, or to establishments employing at least 20 employees. Employees 
should  be  informed  on  significant  developments  affecting  (i)  the  undertaking's  activity  and 
economic situation, (ii) situation, structure and changed of employment, in particular where there is 
a threat to employment, (iii) decisions likely to lead to substantial changes in work organisation or 
contractual relations144.  

The Directive on Transparent and Predictable Working Conditions, on the other hand, focuses on 
the individual right to information about the essential aspects of the employment relationship145. Its 
broad scope covers all workers, who have an employment contract or employment relationship, as 
defined  by  the  law,  collective  agreements  or  the  practices  in  force  in  each  Member  State, 

142   Directive 2002/14/EC.  

143   Article 2(e) gives no specific definition of the term 'employees' representatives' but refers to definitions provided by 

national laws and/or practices. Such representative body can take form of trade union representation or the works 
council model. See: Commission staff working document accompanying the Communication from the Commission 
to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the 
Regions on the review of the application of Directive 2002/14/EC in the EU {COM (2008) 146 final}. In Member 
States with a unionised single-channel tradition, information and consultation provided directly to employees should 
be considered an ultima ratio. See: Studies on the implementation of Labour Law Directives in the enlarged 
European Union - Directive 2002/14/EC establishing a general framework for informing and consulting employees in 
the European Community. Synthesis report, p. 9.  

144   Article 4 of the Information and Consultation Directive.  

145   Article 4(2) lists the minimum information that should be provided to all workers, while Article 7 lists information 

that should be provided to workers sent to another EU Member State or to a third country.  

44 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

considering the case law of the CJEU146. This could include domestic workers, on-demand workers, 
intermittent workers, voucher-based workers, platform workers, trainees and apprentices, provided 
they fulfil the CJEU's criteria for being classed as a worker. Article 4(2) contains a non-exhaustive 
list of the aspects of the employment relationship about which the employers are required to provide 
written information to the worker147.  

An important occasion where it is essential for workers to receive information is when the employer 
decides  to  carry  out  collective  redundancies,  as  laid  down  in  the  Directive  on  Collective 
Redundancies148. In such cases, all relevant information must be communicated to workers and their 
representatives in a timely manner, as this is crucial for enabling them to put forward constructive 
proposals.  

Information  obligations  are  also  important  cornerstone  of  the  OSH  legislation149.  For  example, 
pursuant to the OSH Framework Directive, workers and their representatives have the right to be 
informed about all safety and health risks and protective and preventive measures adopted by the 
employer150.  The  duty  of  information  extends  also  to  workers  from  any  outside  undertakings 
engaged in work in the employer's undertaking151. Another crucial right guaranteed under the OSH 
Framework Directive is the right to adequate training152, which should be provided, among other 
situations, when new work equipment or new technology is introduced. 

Although the legislation does not explicitly stipulate that workers or their representatives must be 
informed about the deployment of AM systems, it could be argued that they should receive this 
information  on  a  case-by-case  basis.  For  instance,  although  the  Directive  on  Transparent  and 
Predictable Working Conditions does not cover the use of digital tools, one could argue that the use 
of  AM,  particularly  when  it  affects  workers'  rights,  constitutes  an  essential  element  of  the 
employment relationship. In the Lange case153, for instance, the CJEU ruled that employers must 
inform workers of the obligation to work overtime, even if this was not stipulated in the directive, 
since this aspect constitutes an essential element that affects working conditions154. Furthermore, 
under Article 4(2)(c) of the Information and Consultation Directive, any decision likely to lead to 
substantial changes in work organisation must be the subject of prior information and consultation. 
Recent  reports  by  Eurofound  and  EU-OSHA  argue  that  this  obligation  should  be  understood  as 
including the introduction of significant changes in work organisation through AM systems.155 Still, 

146   Article 1(2) of the Directive on Transparent and Predictable Working Conditions. 

147  

In certain circumstances other information may be deemed essential. De Wispelaere, F., Pacolet, J. and De Smedt, 
L., 2021.  

148   Council Directive 98/59/EC.  

149   See, for example, Article 6 of the Workplace Directive and of the Display Screen Directive (see below for more 

details).  

150   Article 10 of the OSH Framework Directive.  

151   See note 68, page 39. 

152   Article 12(1) of the OSH Framework Directive.  

153   CJEU C-350/99, Wolfgang Lange v Georg Schünemann GmbH. 

154   This ruling however refers to the Written Statement Directive (91/533/EEC), which was repealed by the Directive 

on Transparent and Predictable Working Conditions. 

155   Eurofound, 2024; EU-OSHA, 2022. 

45 

 
EPRS | European Parliamentary Research Service 

some legal authors highlight the need to explicitly regulate information and consultation obligations 
in  the  context  of  AM  systems  and  emphasise  certain  obligations  that  are  already  implicit  in  the 
current wording of the Information and Consultation Directive.156 In this context, it is also important 
to note that the Information and Consultation Directive applies in undertakings with more than 50 
employees or in establishments with more than 20 employees, unless Member States have adopted 
a lower threshold. As such, the obligations would not set out similar information rights for workers 
in smaller undertakings.  

Similarly, the OSH Framework Directive guarantees relevant measures to address risks emerging 
from the application or use of AM systems, such as the right to information and consultation and the 
right  to  adequate  training157.  Finally,  these  directives  are  minimum  harmonisation  directives, 
meaning that they do not prevent Member States from applying higher standards. 

Digital law 

EU  digital  acquis,  such  as  the  GDPR,  the  European Framework  Convention  on  AI  and  the  AI  Act 
provide for a more comprehensive right to information provision and transparency. 

The principle of fairness and transparency is a long-established principle of EU data protection law 
that applies irrespective of the legal basis for processing and throughout the life cycle of processing 
158.  Within  the  context  of  the  GDPR,  transparency  is  an  overarching  obligation  applying  to  three 
central areas: (i) the provision of information to data subjects about the processing of their personal 
data as an expression of fairness of that processing159; (ii) how data controllers communicate with 
data subjects in relation to their data protection rights; and (iii) how data controllers facilitate the 
exercise by data subjects of their rights160. In line with Articles 13 and 14 of the GDPR, a controller 
needs to provide the data subjects with several information, including the existence of automated 
decisionmaking161. In line with the principle of fairness and accountability the controller should be 
able to explain to data subjects the likely impact of such processing on their rights and should not 
deceive the reasonable expectations of data subjects. The GDPR also introduced the right to data 
portability (Article 20 GDPR), which enables data subjects to receive the personal data concerning 
them  in  a  structured,  commonly  used,  machine-readable  format  and  to  transmit  these  data  to 
another  controller  without  hindrance  from  the  controller  to  which  the  personal  data  have  been 
provided162. 

156   Cefaliello, Moore and Donoghue, 2023; Adams-Prassl et al., 2023. 

157   European Commission, 2023. 

158   Article 5(1)(a) of the GDPR. 

159   The rules concerning the controller's information obligations are set out in Articles 12–14 of the GDPR. Article 12 

sets out the general rules on providing information to data subjects, which apply where data are obtained from the 
data subject (Article 13) or from other sources (Article 14). 

160   Article 29, Data Protection Working Party, Guidelines on transparency under Regulation 2016/679 (WP260 rev.01), 

revised and adopted on 11 April 2018.  

161   Article 13(2)(f) and Article 14(2(g) of the GDPR. 

162   Article 29, Data Protection Working Party, Guidelines on the right to 'data portability' (WP242rev.01), revised and 

adopted on 5 April 2017.  

46 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

This  principle  of  transparency  is  also  emphasised  in the  legislation  regulating AI. Article  8  of  the 
European Framework Convention on AI sets out a general principle of transparency and oversight 
which must be tailored to the context and risks induced by the use of a given AI system. Article 
14(2)(a) of the Convention further requires the Parties to adopt or maintain adequate measures to 
ensure  that  AI  systems  that  have  the  potential  to  significantly  affect  human  rights  are  properly 
documented. This information must also be made available to affected individuals, enabling them to 
exercise their right to seek a remedy in the event of a violation.  

In  this  respect,  the  AI  Act  explicitly  acknowledges  the  necessity  to  compensate  any  gap  in 
employees' right to information and participation in the context of planned deployment of high-risk 
AI  systems  in  the  workplace.  Recital  92  indeed  states  that  'it  remains  necessary  to  ensure 
information  of  workers  and  their  representatives  on  the  planned  deployment  of  high-risk  AI 
systems  at  the  workplace  where  the  conditions  for  those  information  […]  and  consultation 
obligations in other legal instruments are not fulfilled'. The AI Act addresses this absence of specific 
obligation  in  the  EU  employment  acquis  through  several  layers  of  transparency  and  disclosure 
obligations. First, in the context of high-risks systems, it establishes a principle of 'transparency by 
design'  for  deployers,  such  as  employers,  to  be  sufficiently  informed  by  their  providers  of  AI 
solutions  on  the  system's  characteristics  to  fulfil  their  own  transparency  obligations.  Article  13 
indeed requires a set of instructions to be disclosed to the deployer on the AI system' ownership, its 
appropriate uses and characteristics. These instructions shall include, inter alia, information on the 
AI  systems'  characteristics,  capabilities  and  limitations; their  level  of  accuracy,  robustness  and 
cybersecurity; details on the human oversight measures developed by the provider, including the 
technical measures in place to facilitate the interpretation of AI system' outputs by the deployer; as 
well  as  information  on  any  known  or  foreseeable  circumstance  related  to  the  use  or  reasonably 
foreseeable misuse of the high-risk AI system which may lead to risks to the health and safety or 
fundamental rights163. 

A second layer of transparency applies specifically to the deployment of AI in the workplace. In line 
with Recital 92, Article 26(7) indeed requires employers that intend to deploy a high-risk AI system 
to inform both affected workers and workers' representatives, before putting such AI system into 
service  or  using  them.  This  obligation  applies  in  accordance  with  the  national  and  EU  rules  and 
practices on workers' rights to information164. It can also be mentioned that certain AI systems are 
also subject to specific transparency requirement towards their users in order to ensure an adequate 
level of literacy and informed use of such tools (Article 50).  

Platform Work Directive (PWD)  

Lastly, the PWD bridges the gap between the general transparency rules of the digital acquis and 
the  labour  law  specifics  of  providing  information  to  workers.  The  PWD  introduces  more detailed 
requirements regarding transparency on the use of AM systems and provision of information for 
persons working through digital labour platforms. These more detailed information requirements 
are limited in terms of their personal scope to persons performing platform work, and do not apply 
to  AM  systems  used  in  other  types  of  undertakings.  Nevertheless,  they  provide  specific  legal 

163   See Article 13(3) of the AI Act. 

164   See Article 26(7), 2nd sentence. 

47 

 
EPRS | European Parliamentary Research Service 

protection for the use of AM systems at work, ensuring that workers are adequately informed about 
the use of such systems, which may be extended to other workers in future legislative initiatives. 
For instance, Article 9 of the PWD contains a legal obligation for Member States to determine in 
their  legislation  that  digital  labour  platforms must  inform  persons  performing  platform  work  and 
national  authorities  about  the  use  of  automated  monitoring  and  decisionmaking  systems,  in 
accordance with Directive 2002/14/EC on information and consultation. Specifically, such platforms 
need  to  disclose  whether  such  systems  are  in  use  or  being  introduced  and  details  about  their 
operation.  

For  automated  monitoring  systems,  platforms  are  required  to  provide  information  about  the 
introduction of such systems, the types of data being monitored (such as categories of data), the 
objectives of the monitoring, and how it is conducted. They must also clarify who the recipients of 
this data are, including any data transfers within corporate groups. For automated decisionmaking 
systems, platforms must inform workers about their existence, the types of decisions supported or 
made by these systems (such as suspensions, terminations, or payment decisions), the categories 
of data and parameters used, including how personal data and worker behaviour influence these 
decisions,  and  the  grounds  for  decisions  that  adversely  affect  workers'  contractual  status  or 
compensation. 

The PWD also contains formal requirements about how this information is to be provided to the 
persons performing platform work, namely in a written, accessible, and plain language format, on 
the first day of work and before any significant changes affecting work conditions or monitoring 
methods are implemented. Workers can also request more detailed information at any time. Similar 
information  obligations  also  apply  to  selection  and  recruitment  processes.  Importantly,  workers 
have  the  right  to  data  portability,  meaning  they  can  receive  a  copy  of  personal  data  generated 
through these systems, including reviews and ratings, in a way that protects their rights under EU 
data protection laws. Platforms must facilitate this process by making tools available to this end, 
free of charge, and, upon request, transmit the data directly to third parties. 

Finally,  Articles  13  and  14  contains  specific  obligations  for  platforms  to  provide  information  to 
persons performing platform work, in line with the obligations set out under EU law, tailored to them.  

48 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Generally,  EU  Member  States  regulate  information  and  consultation  obligations  in  line  with  the  above-
mentioned Directives. Works councils in the Netherlands  have the right to be consulted and to provide 
advice to the employers on the introduction of mayor technological innovations (Article 25 of the Works 
Council Act). A specific mention to be informed about the use of AM systems is contained in the French 
Labour  Code.  Art.  L.  2312-38  requires  employers  to  inform  work  councils  of  the  introductions  of  such 
automated systems and of any methods or techniques supporting recruitment procedures.  

Spain is the first EU Member State to set out information obligations on AI/AM in a dedicated law. The 
Rider’s Law (2021) specifically recognises the right of the work council to be informed by the company of 
parameters,  rules  and  instructions  of  the  algorithms  of  artificial  intelligence  systems  that  affect  labour 
decisionmaking and may have an impact on working conditions, access to and maintenance of employment.  

A draft amendment bill to the Trade Union Act is currently in the process of adoption in Poland. The draft 
bill regulates the obligation of the employer to provide information, upon request from a company trade 
union, on the parameters underlying the algorithms used int eh workplace to make decisions concerning 
employees.  

To  enhance  transparency,  the  Dutch  government  introduced  an  algorithm  register  in  2022  where 
government agencies can, on a voluntary basis, enter the information about the algorisms they use. The 
register is accessible to everyone and signals the government’s commitment to promote accountability in 
the use of AM systems. Although it is not complete yet, as of 2025 the publication of high-impact algorithm 
has become mandatory, setting a strong transparency precedent.  

Another example of soft law tool which encourage a transparent and informative use of AM systems in the 
workplace is the Spanish Ministry of Labor and Social Economy’s guide on algorithmic information in the 
workplace. The guide illustrates and provides practical guidance on the obligation of businesses to provide 
information on the use of algorithms in the workplace.  

In France, a think tank called LaborIA, supported by the Ministry of Employment has been established. The 
latter issued guidelines on the deployment of AI at work for microenterprises and SMEs. Those guidelines 
not only insist on employees’ involvement and training in AI/AM systems deployment process at work but 
also on how to approach workers’ role and interactions with AI tools when working. This guide provides a 
detailed  process  to  accompany  AI/AM  systems  deployment  as  well  as  a  list  of  key  legal  provisions 
applicable to workers’ protection at work in this context. However, the guide does not particularly insist on 
OSH risks and prevention. 

No other examples or initiatives were identified in the other selected Member States.  

3.2.2. Specific safeguards for AM (e.g. human oversight, right to explanation, 
ex-ante assessment and continuous monitoring) 

As mentioned above, none of the labour or OSH instruments provide specific mechanisms for the 
use of AM. Such mechanisms are however present in the European Framework Convention on AI, 
the AI Act, the GDPR and the Platform Work Directive. 

Human oversight and the right to explanation 

Both the European Framework Convention on AI and the EU AI Act lay down detailed rules on human 
oversight, both for the use of AI systems, AM in general and AM in particular. In line with Article 8 of 
the Framework Convention on AI, Articles 14 and 26 of the AI Act lay down rules on the application 
of an adequate level of human oversight applicable to AI-generated decisions affecting individuals. 
Although the regulation does not state it explicitly, it has been argued that this provision may allow 

49 

 
EPRS | European Parliamentary Research Service 

workers'  representative  bodies  to  play  a  role  in  this  supervision165.  Pursuant  to  Article  14(2),  the 
purpose of human oversight is to prevent, mitigate and minimise the risks caused by the use, or 
reasonably foreseeable misuse, of AI systems on individuals' safety, health and fundamental rights. 
Such obligation mainly falls on AI system providers, with deployers' support. High-risk AI system 
provider must develop two types of measures to ensure its AI system is subject to human oversight: 
measures built into the AI system itself; and measures identified by the provider to be implemented 
by the deployer166. Providers shall also ensure that the deployer (i.e. the employer) or the person 
appointed  to  carry  out  such  oversight  are  adequately  informed  of  the  risks,  capabilities  and 
limitations of the AI system; are aware of the risk to automatically rely or over-rely on the high-risk 
AI  system'  outputs  (so-called  'automation  bias')  as  well  as  their  possibility  to  neutralise  the  AI 
system by interpreting its output correctly, set aside its proposed solution or disactivate it when 
necessary167.  The  AI  Act  also  requires  high-risk  AI  system,  such  as  AM  tools,  to  be  designed  to 
achieve an appropriate level of accuracy, robustness, resilience and cybersecurity to prevent errors 
throughout  their  lifecycle168.  Deployers  of  AI  systems  are  required  to  assign  human  oversight 
responsibilities  to  natural  persons  who  are  competent,  trained  and  have  sufficient  authority  and 
support to exercise their task effectively169. They shall also inform their AI system providers when 
the operation of a high-risk AI system in accordance with the providers' instructions may lead to 
risks for persons' health and safety or fundamental rights170.  

These due diligence obligations must also be accompanied by an appropriate level of transparency 
and  information  for  the  persons  subject  to  automated  decisions.  Article  26(11)  of  the  AI  Act 
specifically provides that a person concerned by a decision, taken or assisted by a high-risk AI tool 
such as those deployed in a working environment in line with point 4 of Annex III of the AI Act, shall 
be informed of such use. In this context, the European Social Partners Framework Agreement on 
Digitalisation (the Framework Agreement on Digitalisation)171 outlines that any AI system used at 
work must be adequately transparent and explainable to ensure effective oversight and checks to 
prevent erroneous AI output172. 

The AI Act also guarantees the right of individuals, subject to a decision supported or resulting from 
a high-risk AI system adversely affecting their safety, their health or their fundamental rights, to 
request clear and meaningful explanations from the deployer (i.e. the employer) (Article 86). Such 
right to explanation covers both the role of the AI system in the decisionmaking process and the 
main elements taken into consideration to reach said decision. 

165  

industriALL, 2024. 

166   Article 14(3) of the AI Act. 

167   Article 14(4) of the AI Act. 

168   Article 15. 

169   Article 26(2) of the AI Act.  

170   Article 26(5) of the AI Act. 

171   European Trade Union Confederation (ETUC), European Centre of Employers and Enterprises providing Public 

Services (CEEP), SMEunited, and BusinessEurope, Framework Agreement on Digitalisation, 22 June 2020, available 
at ETUC, accessed August 2025.  

172   See European Framework Agreement on Digitalisation, Point 3 – Artificial Intelligence (AI) and guaranteeing the 

human in control principle.  

50 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Although the GDPR does not refer to any particular AM tools, it provides rules that are adaptable to 
future digital challenges. In this vein, it provides data subjects (i.e. workers) with the right not to be 
subject to a decision based solely on automated processing, including profiling, that produces legal 
effects  for  them  or  significantly  affects  them  (Article  22(1)  GDPR).  However,  this  right  is  not 
absolute and does not apply if: (a) the decision is necessary for entering into or performing of a 
contract; (b) the data subject has given explicit consent; or (c) the decision is authorised by EU or 
Member State law (Article 22(2) GDPR). In cases referred to in points (a) and (c), where automated 
decisionmaking is permitted, the data subjects should have the right to obtain human intervention, 
express their own point of view, and contest the decision (Article 22(3))173. In addition, data subjects 
should  also  be  informed  of  the  existence  of  automated  decisionmaking  and  provided  with 
meaningful  information  about  the  decisionmaking  logic  involved  (algorithmic  transparency)174,  as 
well as the significance and the envisaged consequences of such processing (Article 15(1)(h))175. 

Problems surrounding the notions of 'solely automated' and 'meaningful human intervention', as well 
as the obligation to provide explanations under Articles 15 and 22 of the GDPR, however present 
interpretive  challenges,  as  demonstrated  by  the  case  law  of  the  CJEU  and  the  national 
administrative  and  judicial  decision176.  In  parallel  with  the  obligations  relating  to  provision  of 
information, Article 10 of the PWD establishes a requirement to ensure human oversight over AM 
systems  used  by  platform  providers.  It  requires  platform  operators  to  implement  measures  that 
guarantee  human  intervention  where  necessary,  particularly  in  decisionmaking  processes  that 
significantly affect workers' rights and working conditions. The article aims to prevent automated 
systems from making critical decisions without human review, thereby safeguarding workers' rights 
and ensuring transparency, fairness, and accountability in the use of AM systems. More generally, it 
mandates  platform  companies  to  maintain  sufficient  human  check  and  oversight  concerning  AM 
algorithms,  to  ensure  these  mechanisms  do  not  infringe  on  workers'  rights  or  result  in  unfair 
treatment. 

Article 11 of the PWD sets out safeguards for human review of automated decisions, in particular in 
decisionmaking  processes  that  significantly  affect  workers'  rights  or  working  conditions.  This 
includes  the  possibility  to  discuss  and  clarify  any  facts  that  have  led  to  a  decision  taken  by  an 
automated system with a contact person designated by the platform, and the obligation to rectify 
any decisions that have infringed the rights of the person working through the platform.  

173   Recital 71 goes even further and requests that such safeguards should also include the right to obtain an explanation 

of the decision reached and the right to challenge the decision. 

174   This includes 'a right to an explanation of the procedure and principles actually applied in order to use, by 

automated means, the personal data of the data subject with a view to obtaining a specific result'. Such 'relevant 
information' must be 'concise, transparent, intelligible and easily accessible'. See CJEU C-230/22, Dun & Bradstreet 
Austria. 

175   For more information, see Article 29 Data Protection Working Party, Guidelines on Automated individual decision-
making and Profiling for the purposes of Regulation 2016/679 (WP251rev.01), revised and adopted on 6 February 
2018. 

176   Hießl, C., Case Law on Algorithmic Management at the Workplace: Cross-European Comparative Analysis and 

Tentative Conclusions, 2025.  

51 

 
EPRS | European Parliamentary Research Service 

While the PWD covers a wide range of AM tools, including semi-automated decisions, the GDPR 
only protects fully automated and high-impact outcomes. Another difference between the GDPR's 
and the PWD's safeguards is that the GDPR provides an ex-post individual remedy, whereas the 
PWD  imposes  ex-ante  organisational  duties.  Compared  to  the  PWD,  the  AI  Act  only  requires 
deployers  to  inform  subjects  of  automated-  or  AI-assisted  decisions  without  requiring  certain 
decisions to be taken by humans only177. This creates a distortion between the rights of persons 
performing platform work subject to the PWD and other workers where AM systems are deployed. 

In its National Collective Agreement of May 2023, Spain established that the deployment of AI systems in 
companies  must  follow  the  principle  of  human  control,  security,  and  transparency.  Similarly,  although 
limited to a single sector, the Spanish 24th Collective agreement of the banking sector of 2021 protects 
employees from decisions based solely on automated decision and against bias and discrimination.  

In the Netherlands, on occasion of court proceedings in the case Uber B.V. and Uber Technologies Inc., the 
Amsterdam Court of Appeal ruled on grounds of national data protection law that platforms hiring workers 
shall  provide  information  about  the  functioning  of  their  algorithms  and  ensure  meaningful  human 
intervention in decisions based on automated processing (See also Section 3.2.4 below).  

In France, a dual regime applies differently to fully automated individual decisions taken by private and 
public entities. Pursuant to Article 47 of the French data protection law, private entities, such as private 
employers,  are  prohibited  to  take  any  individual  decision  based  on  a  fully-automated  data  processing 
system whereas, public authorities, including public employers, are allowed to do so under the following 
conditions: The decision shall not process sensitive data; it explicitly informs the person concerned that it 
is grounded on algorithmic processing and that information on the characteristics and rules applicable to 
that algorithmic processing can be requested (transparency) and, that the data processor exercises human 
oversight on the algorithmic processing and its evolutions and is able to explain that processing, in details 
and  understandably,  to  the  person  concerned.  Although  this  regime  is  laid  down  in  the  general  data 
protection law, it applies to  employment settings and creates a distinction between  public and private 
workers.  

No other examples or initiatives were identified in the other selected Member States.  

Ex-ante assessment and continuous monitoring 

AI Act, GDPR and PWD all require ex-ante impact assessment before rolling out an AM tool, which 
might need to be performed cumulatively, depending on circumstances of the case. 

The AI Act provides for several types of risk assessment throughout the market chain. These include 
those conducted by certain AI deployers (i.e. employers using the AM tools), who are obliged to 
conduct a fundamental rights impact assessment (FRIA)178. However, the scope of such a FRIA is 
limited to the deployment of high-risk AI systems, such as AM tools, by public entities and private 
entities providing public services179. Therefore, excluding private employers from the scope of this 
obligation. At providers' level, a continuous risk management system (RMS) shall be established to 

177   See Article 10 of the PWD and Article 26(11) of the AI Act. 

178   Article 27 of the AI Act. 

179   Article 27(1) of the AI Act. See also, Opinion of the European Economic and Social Committee – Pro-workers AI: 

levers for harnessing the potential and mitigating the risks of AI in connection with employment and labour market 
policies (own initiative opinion). 

52 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

identify,  estimate,  evaluate  and  address  risks  throughout  the  entire  lifecycle  of  high-risks  AI 
systems180.  These  RMSs  shall  be  developed,  implemented  and  maintained  through  a  continuous 
iterative  process  including  regular  systematic  reviews  and  updates  as  necessary181.  While  this 
suggests a possibility for social partners to be regularly informed of the risks182, no participation to 
the evaluation process can be foreseen by workers' representatives or employers (i.e. deployers) as 
this exercise only falls on AI system providers. 

Meanwhile, employers deploying AM tools may also be required to carry out a data protection impact 
assessment (DPIA). The AI Act explicitly states that its FRIA 'shall complement'183 the DPIA, which 
usually involves a more detailed analysis of these processing activities in order to identify, evaluate 
and  mitigate  the  risks  associated  with  the  processing  of  such  data184.  Whether  or  not  a  DPIA  is 
required for the use of AM depends on a case-by-case assessment185. In principle, Article 35 of the 
GDPR requires a controller to carry out such an assessment when processing data is likely to result 
in  a  high  risk  to  the  rights  and  freedoms  of  data  subjects.  This  is  the  case,  for  example,  when 
controllers (i.e. employers) use new technologies to track workers' location or behaviour, or when 
data  processing  is  used  to  make  automated  decisions  about  workers  that  could  have  legal  (or 
similarly significant) effects. The EDPB guidelines aim to clarify when the preparation of a DPIA is 
necessary  by  providing  criteria  for  national  lists  of  processing  operations  requiring  such  an 
assessment186. Article 8 of the PWD ensures that any processing of personal data by a digital labour 
platform  using  such  automated  systems  is  considered  high-risk  processing  under  the  GDPR, 
requiring a DPIA. Moreover, Article 12 PWD requires an assessment of physical, psychosocial and 
ergonomic risks resulting from an AM tool to be carried out prior to the deployment and whenever 
circumstances change. As mentioned earlier, the personal scope of the PWD is, however, limited to 
persons performing platform work and would not extend to workers in other workplaces. In addition, 
ex-post monitoring of the AM deployment is required by the AI Act and, in the context of persons 
performing platform work by the PWD but no such safeguard exists in the GDPR.  

Article 26(5) of the AI Act requires AI deployers to monitor the operation of the high-risk AI system 
on the basis of the instructions for use and, where relevant, inform providers in accordance where 
they  have  reason  to  consider  that  the  use  of  the  high-risk  AI  system  in  accordance  with  the 
instructions  may  result  in  that  AI  system  presenting  a  risk  and  the  relevant  market  surveillance 
authority,  and  shall  suspend  the  use  of  that  system.  Where  deployers  have  identified  a  serious 
incident, they shall also immediately inform first the provider, and then the importer or distributor 
and  the  relevant  market  surveillance  authorities  of  that  incident.  Article  10(1)  PWD  requires  a 

180   Article 9 of the AI Act. 

181   Article 9(1) and 9(2) of the AI Act. 

182  

industriALL, 2024. 

183   Article 27(4) of the AI Act. 

184   Article 35(1) GDPR. 

185  

186  

In line with the risk-based approach in the GDPR, carrying out a DPIA is not mandatory for every processing 
operation. 

In line with Article 35(4) GDPR, national data protection authorities must establish a list of processing operations for 
which a DPIA should be prepared, while Article 35(5) GDPR gives provides an option to also establish a list of 
activities for which no DPIA is required. 

53 

 
EPRS | European Parliamentary Research Service 

periodic  follow-up  to  take  place  of  the  risk  assessments  related  to  OSH  required  by  Article  12. 
Regularly and in any event every two years, digital platforms are required to carry out an evaluation 
of  the  impact  of  individual  decisions  taken  or  supported  by  automated  systems  on  persons 
performing  platform  work,  including,  where  applicable  on  their  working  conditions  and  equal 
treatment at work.  

In the Netherlands, a non-binding supporting document has been developed to support  responsible AI 
systems  deployment.  The  AI  Impact  Assessment  (AIIA)  is  soft  law  instrument  designed  to  help 
organisations  identify  and  mitigate  ethical  and  societal  risks  associated  with  AI  deployment.  While  not 
specifically focusing on AI/AM deployment in workplaces, it facilitates FRIA including non-discrimination 
principles  where  algorithmic  management  tools  are  used,  particularly  within  public  institutions.  It  also 
provides a structured reflection on transparency, human oversight, and potential societal harms. 

No other examples or initiatives were identified in the other selected Member States.  

3.2.3. Organisation of working time 

AM systems may be used to better organise working time, leave and other work arrangements or 
schedules across employees, in a number of ways. For instance, AM systems can be used to create 
optimal  work  schedules  based  on  availability  of  employees,  demand  forecasts  and  regulatory  or 
individual requirements, such as daily working hours, minimum rest periods, and specific working 
arrangements  of  an  employee.  AM  systems  can  also  be  implemented  to  monitor  working  time, 
including  for  reporting  to  national  authorities.  Data  gathered  by  AM  systems  could  be  used  by 
undertakings to optimise productivity or planning.  

A number of legal instruments have been adopted over the years in relation to the organisation of 
working time, including at EU level. In particular, the Working Time Directive187, and more recently 
the  Directive  on  Transparent  and  Predictable  Working  Conditions188  and  the  Work-Life  Balance 
Directive189,  regulate  aspects  of  working  time  and  working  conditions  that  could  potentially  be 
impacted by the use of AM systems.  

For instance, rules on daily working hours, weekly rest periods, maximum weekly working time, are 
set out in detailed terms in the Working Time Directive and need to be respected both in traditional 
employment  relationships  and  in  those  employing  AM  tools  as  they  are  generally  applicable  to 
workers. The CJEU had the occasion to deal with the use of a digital tool recording working time in 
CCOO case and has clarified that the failure to ensure compliance with the right to a limitation on 
maximum  working  time  and  minimum  rest  period  is  incompatible  with  the  objective  of  the 
Directive190.  These  elements  are  essential  for  the  health  and  safety  of  workers,  and  given  the 
fundamental  nature  of  these  rights,  they  should  not  be  subordinated  to  purely  economic 
considerations191. The CJEU also considered, in Rudy Matzak, that 'stand-by' time, during which the 

187   Directive 2003/88/EC.  

188   Directive (EU) 2019/1152. 

189   Directive (EU) 2019/1158. 

190   CJEU, C-55/18, Federación de Servicios de Comisiones Obreras (CCOO) v Deutsche Bank.  

191   See note 103, page 48.  

54 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

worker's opportunities to carry out other activities are significantly restricted, is to be regarded as 
working time192. 

The Directive on Transparent and Predictable Working Conditions sets out legal requirements for 
the  provision  of  information  to  workers  about  employment  conditions  and  predictability  of  work 
schedules. The Directive, for instance, requires workers to be provided with their work schedules 
well in advance, provides specific protection to workers on atypical or highly flexible contracts (such 
on-call  work  contracts  or  zero-hour  contracts)  and  allows  workers  to  request  changes  to  work 
schedules.  The  requirements  of  the  Directive  apply  to  a  broad  range  of  workers  across  the  EU, 
including those on more flexible work contracts. The Directive does, however, not contain specific 
provisions regarding the use of AM systems.  

Further  limits  to  extended  workload  are  offered  by  the  Work-Life  Balance  Directive,  aiming  to 
facilitate  work-life  balance  for  parents  and  carers  and  flexible  working  arrangements.  Protective 
mechanisms  for  workers  include  the  obligation  for  the  employer  to  consider  and  respond  to  the 
request of flexible working arrangements, and to justify any refusals or postponements (Article 9), 
protection from dismissal (Article 12) and from any less favourable treatment resulting from their 
request  (Article  14).  These  provisions  support  work-life  balance  and  encourage  adaptable  work 
practices and must be respected also in situations where AM systems are used193. 

AM  systems  at  the  work  floor  present  opportunities  for  increased  efficiency  and  enhanced 
compliance, including with the legal requirements set out in the labour law Directives. For instance, 
AM  systems  may  be  used  to  monitor  and  automatically  report  working  time  to  enforcement 
authorities, including in sectors where access to the 'work floor' for inspections is more challenging. 
However, the use of AM systems also creates specific regulatory needs in relation to transparency 
and wellbeing at work which are not currently addressed in the working time acquis.  

Pursuant to Article 6(2) and Point 4(b) of Annex III of the AI Act, AI systems used for the purpose of 
workers'  management  leading  to  decisions  affecting  the  terms  of  their  working  relationship  or 
allocating tasks based on workers' behaviour and personal traits are expressly qualified as high-risks 
AI systems due to their potential impact on the safety, health and fundamental rights of the workers 
concerned. 

The Platform Work Directive sets out specific protection mechanisms for persons working through 
digital  platforms,  in  particular  regarding  transparency  and  fairness  of  work  arrangements.  These 
range from the information and transparency requirements set out above in section 3.2.1 to concrete 
limitations for algorithmic monitoring and profiling of workers at the work floor. Similar protection 
mechanisms  are  considered  necessary  to  enhance  the  protection  of  all  workers  exposed  to  AM 
systems in the workplace  

192   CJEU, C-518/15, Ville de Nivelles v Rudy Matzak. 

193   European Commission, Study exploring the context, challenges, opportunities and trends in algorithmic 

management, 2023. 

55 

 
EPRS | European Parliamentary Research Service 

No  example  or  initiative  on  how  automated  systems  for  time  management  or  organisation  adhere  to 
working time limits was identified in the selected Member States. 

According to the JRC study on the platformisation of work and an OECD artificial intelligence paper on 
algorithmic management in the workplace, Spanish and German employees’ worktime is heavily monitored 
through digital tools in general (50 % of employees in Germany for 46 % in Spain). A larger difference is 
noted between the use of AM systems which is broadly used in Spain compared to Germany. According to 
the JRC study, AM systems used for the automated allocation of work are the most widespread form of 
AM systems identified in Spain and Germany, where 11 % of German workers and 19 % of Spanish workers 
are automatically allocated their shifts or working time via digital device.  

3.2.4. Data protection and privacy 

The  use  of  modern  digital  technologies  has  added  a  new  layer  of  complexity  in  monitoring  and 
surveillance  in  the  workplace,  through  many  different  devices  such  as  smartphones,  desktops, 
tablets, vehicles and wearables194. This has been compounded by the COVID-19 pandemic that gave 
rise to the full automatization of the managerial functions. This section explores to what extent the 
data protection legislation addresses the use of AM at workplace.  

The right to data protection, recognised in Article 8 of the Charter and also enshrined in Article 16 
TFEU is seen as an essential component of respect for private and family life and is part of the rights 
protected by Article 8 of the European Convention on Human Rights (ECHR)195. The GDPR provides 
a general legal framework for the protection of personal data, which applies to private and public 
actors processing personal data at national level, including in the workplace. Considering the need 
of  important  datasets  for  the  improvement,  actualisation  and  well-functioning  of  AI,  both  the 
European Framework Convention on AI and the EU AI Act also lay down standards on personal data 
protection and data governance. Article 11 of the European Framework Convention on AI particularly 
insists on the need to uphold the right to privacy and the protection of personal data throughout AI 
system' lifecycle. It also commands that effective guaranties and safeguards are put in place under 
national  and  international law  to  protect  the  rights  of  individuals.  In  relation  to  the  use  of  AI  for 
managerial  and  employment  purposes,  Recital  57  of  the  AI  Act  expressly  acknowledges  that  AI 
systems  used  to  monitor  the  performance  and  behaviour  of  such  persons  may  undermine  their 
fundamental rights to data protection and privacy. Nevertheless, the Regulation does not insist on 
personal data  protection  and  rather  establishes  rules  on data  governance.  Such  a  lack  of  special 
provisions in the AI Act is explained by the use of consistency mechanisms in relation to the GDPR. 
Indeed,  the AI Act heavily  relies  on  the  already-existing  GDPR,  through cross-references  and no 
prejudice clauses to safeguard users' data protection196. Conversely, Article 10 of the AI Act rather 

194   Article 29 Data Protection Working Party, Opinion 2/2017 on data processing at work, Adopted on 8 June 2017, p. 9. 

195   The concept of 'private life' has been broadly interpreted in the case law of the European Court of Human Rights to 
include respect for one's physical, psychological or moral identity, identity and autonomy, image and reputation. As 
such, it includes intimate situations, sensitive or confidential information, information that could affect the public's 
perception of an individual, and even aspects of one's family and professional life and public behaviour. 

196   See for instance Articles 2(7), 10(5) or 26(9) and Recitals 10 and 53 of the AI Act. 

56 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

focuses on data governance, setting out standards to ensure that high-risk AI systems rely on high-
quality datasets to train AI models, hence avoiding errors and biases.  

While  the  GDPR  generally  ensures  that  workers  receive  adequate  information  on  the  scope  and 
nature  of  the  monitoring  and  surveillance197,  and  that  employers  are  required  to  justify  the 
measures198  and  minimise199  their  impact  by  deploying  the  least  intrusive  methods,  this  omnibus 
approach has several deficiencies: 

Imbalance  of  power  between  the  parties:  Although  labour  law  provides  special  protection  for 
workers to counterbalance the subordinate nature of employment relationships, the GDPR does not 
treat workers differently to other data subjects. The EDPB has also affirmed that workers are not in 
an  equal  position  of  power,  advising  against  relying  on  consent  as  a  lawful  legal  basis  given  the 
dependency resulting from the employment relationship, as it is unlikely that such consent could be 
freely given200; 

Lack  of  collective  dimension:  Although  the  GDPR  permits  the  representation  of  data  subjects 
(Article 80), it does not cover important collective aspects of labour law. These include the role of 
workers'  representatives,  the  consultation  of  workers,  and  the  role  of  labour  inspectorates  in 
enforcing labour rights201. The GDPR safeguards the individual right of workers to have their data 
protected. 

Several  attempts  to  introduce  an  EU-level  data  protection  framework  targeting  exclusively 
workplace  relations  failed.  The  lack  of  political  agreement  resulted  in  the  'opening  clause'  under 
Article 88(1) of the GDPR, which allows Member States to pass their own legislation on employee 
data protection202. Some scholars argue that, on the one hand, this compromise promoted diverse 
and innovative regulatory approaches at the level of the Member States, while on the other hand it 
created further fragmentation, legal uncertainty and inconsistent enforcement203. The CJEU recently 
ruled204 that three conditions must be met for the use of Article 88: (i) national law must be specific 
to employment and distinct from general GDPR rules; (ii) the purpose must be to protect the rights 
and freedoms of workers; and (iii) suitable and specific measures must be in place in accordance 
with Article 88(2). Although they are not defined, a set of substantive requirements can be derived 

197  

In line with Articles 12 and 13, employers in their role as controllers need to provide transparency to workers (i.e. 
data subjects), meaning that they should provide them with the following information: (i) the identity and the 
contact details of the controller, (ii) contact details of the DPO, (iii) purpose and legal basis, (iv) categories of 
personal data, (v) recipients, (vi) transfers, (vii) retention periods, (viii) legitimate interests, (ix) exercising rights, (x) 
lodging complaints with DPA, (xi) source, (xii) automated decisionmaking. 

198   Principle of lawfulness (Article 5(1)(a) GDPR) requests that data are processed lawfully, meaning that the controller 
must be able to demonstrate one of the lawful legal bases for the processing of personal data specified in Article 6 
or even in Article 9, in the case of special categories of data. 

199   The principle of data minimisation requires that personal data are adequate, relevant and limited to what is 

necessary in relation to the purpose shall be processed (Article 5(1)(c) GDPR). 

200   Article 29 Working Party, 2018, Guidelines on Consent under Regulation 2016/679.  

201   Proposal for a Directive on improving working conditions in platform work, COM(2021)762 final.  

202   See also Recital 155. 

203   Abraha, 2022. 

204   CJEU C-34/21, Hauptpersonalrat der Lehrerinnen und Lehrer.  

57 

 
EPRS | European Parliamentary Research Service 

from  non-binding  instruments205  regulating  data  protection  in  the  workplace,  including  the 
principles  of  finality,  necessity  and  proportionality,  transparency,  and 
information  and 
consultation206.  

At  the  EU  level,  the  interplay  between  data  protection  and  employment  legislation  has  been 
addressed  in  the  Platform  Work  Directive.  Article  7  of  the  PWD  establishes  limitations  on  the 
processing  of  personal  data  by  means  of  automated  monitoring  or  decisionmaking  systems, 
including a clear prohibition to use such systems for the processing of personal data relating to, for 
instance, emotional or psychological wellbeing, private conversations, offline data or data relating 
to specific criteria, e.g. ethnicity, migratory status, political or religious opinions, diseases, etc. The 
limitations  on  processing  of  personal  data  extend  from  the  start  of  the  recruitment  or  selection 
procedure.  

In the absence of a specific  regulatory framework, national authorities  have leveraged the  GDPR rules, 
concepts and prerogatives to protect workers’ fundamental rights against intrusive AM systems deployed 
in the workplace.  

In April 2023 in the Netherlands, the Amsterdam Court of Appeal fined Uber B.V. and Uber Technologies 
Inc. to a EUR. 584.000 fine for failure to uphold platform workers’ right to personal data protection in a 
dismissal  process.  In  this  case,  drivers  argued  that  their  platform  accounts  had  been  automatically 
disactivated, being, de facto, dismissed from their possibility to use that intermediary platform to work. 
On grounds of data subject’s right to access information (Article 15(1) GDPR), the Court ruled that the 
employer  must  provide  information  about  the  functioning  of  its  algorithms.  It  also  found  a  breach  of 
safeguards against automated individual decisions making and profiling (Article 22 GDPR) and concluded 
that companies shall ensure meaningful human intervention in decisions based on automated processing.  

Similarly in France, by decision of 27 December 2023, the data protection authority fined Amazon France 
Logistique a EUR. 32 million administrative sanction for an overly intrusive AI automated monitoring and 
evaluating system and the use of video surveillance systems without workers’ prior information. Regarding 
the use of automated tools, the authority sanctioned the deployment of a highly precise tool measuring 
employees’ activities interruption as well as the speed of their product scanning frequency to minimise 
stock supply errors and supervise employees’ work planification and evaluations. The authority found that 
Amazon  had  breached  the  principle  of  data  minimisation  for  its  too  precise  pause  monitoring  system 
(Article 5(1)(c), GDPR) as well as the principles of lawful processing and transparency obligations for not 
informing employees of the establishment of an intrusive evaluation system. 

An example of soft-law tool has been found in France. The national data protection authority (CNIL) has 
developed a series of practical recommendations on the application of the GDPR to the development and 
deployment of AI systems. A specific case study illustrates how SMEs can use generative-AI to draft job 
offers and the key elements to be considered to comply with data protection rules. Although relevant to 
the employment context, these recommendations do not specifically address risks and challenges arising 
from the use of AM in the workplace.  

No other examples or initiatives were identified in the other selected Member States.  

205   For example, the Council of Europe Recommendation No R (89) on the Protection of Personal Data Used for 

Employment Purposes 1989 (revised in 2015), the ILO Code of practice on the protection of workers' personal data 
1997, and the Article 29 Working Party, Opinion 8/2001 on the processing of personal data in the employment 
context, 5062/01/EN/Final WP 48. 

206   Abraha, 2022. 

58 

 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

3.2.5. Bias and discrimination  

Workers are protected against discrimination in a number of ways also through EU primary207 and 
secondary law, e.g., the Race Equality Directive208 prohibits discrimination on the grounds of race or 
ethnic  group,  the  Employment  Equality  Directive209  on  the  grounds  of  age,  religion  or  belief, 
disability, or sexual orientation, and the Gender equality in employment and occupation Directive210 
on the grounds of sex. For example, Article 2 of the Employment Equality Directive forbids both 
direct and indirect discrimination. Workers are afforded protection through a reversal of burden of 
proof  in  legal  proceedings  (Article  11),  while  additional  safeguards  include  protection  against 
dismissal or other adverse treatment resulting from the complaint (Article 12). More recently, the 
Pay Transparency Directive reinforces the principle of equal pay between men and women for work 
of equal value.  

Like humans, algorithms can be vulnerable to biases that can result in unfair and/or discriminatory 
predictions. Not all biases however result in unlawful discrimination211. When this is the case, the EU 
equal treatment and non-discrimination legislation also applies to the use of AM systems.  

The principle of equal treatment, as an overarching principle of EU law and enshrined in Article 21 of 
the  Charter  of  Fundamental  Rights  (Charter),  has  already  been  recognised  as  applicable  to 
algorithms in the Deliveroo case212. In this case, the Italian Court found that the algorithm used by 
Deliveroo  was  discriminatory  as  it  allocated  timeslots  on  the  basis  of  riders'  ability  to  offer  and 
accept  work,  with  no  consideration  of  personal  reasons  (e.g.,  family  issues  or  childcare)  for  late 
cancellation or non-participation in peak shifts213. In addition, the CJEU has previously ruled that the 
anti-discrimination  rules  are  directly  applicable  in  disputes  between  private  parties,  including 
employment relationships, even though the directives are generally addressed to Member States214. 

However, the current boundaries of EU non-discrimination law are being challenged by algorithmic 
discrimination for a number of reasons: 

Distinction  between  direct  and  indirect  discrimination215:  While  direct  discrimination  does  not,  in 
principle, require justification, the proportionality test is applied in cases of indirect discrimination. 
Presenting evidence to support workers' claims may therefore be especially difficult for reasons such 
as trade secrets and IP protection. 

207   See in particular Articles 19 and 157 TFEU and Articles 21 and 23 of the Charter. 

208   Council Directive 2000/43/EC.  

209   Council Directive 2000/78/EC.  

210   Directive 2006/54/EC.  

211   For more insights into addressing bias in AI and algorithms, see ELA, Artificial intelligence and algorithms in risk 

assessment. A Handbook, July 2024.  

212   Ruling of the Court of Bologna of 31 December 2020, http://www.soluzionilavoro.it/2021/01/07/giurisprudenza-

tribunale-di-bologna-ordinanza-31-dicembre-2020/.  

213   See An Italian lesson for Deliveroo: Computer programmes do not always think of everything!  

214   See e.g. CJEU C-414/16, Vera Egenberger v Evangelisches Werk für Diakonie und Entwicklung e.V. 

215   For the discussion on whereas discriminatory AI/AM systems should be argued under direct or indirect 

discrimination see, for example, Adams‐Prassl, J., Binns, R. and Kelly‐Lyth, A., Directly Discriminatory Algorithms.  

59 

 
EPRS | European Parliamentary Research Service 

Lack of transparency ('black box' problem): AM decisionmaking is often opaque, making it difficult 
for individuals or courts to understand, trace, or prove discriminatory treatment. 

Intersectional discrimination: The Gender Shades study216 showed that facial recognition software 
of  large  commercial  platforms  was  biased  against  several  groups,  but  especially  against  dark-
skinned women (so called intersectional discrimination to race and gender). However, the CJEU217 
so far does not fully recognise discrimination resulting from the combination of more grounds. 

Limited number of protected grounds: The list of protected grounds in EU directives is closed and 
exhaustive,  limiting  protection  for  emerging  risks  like  discrimination  based  on  social  origin  or 
socioeconomic status218. 

From the digital acquis perspective, the protection of fundamental rights is the primary objective of 
the European Framework Convention on AI and the AI Act and the GDPR. Recital 71 of the GDPR AI 
and data processing used for profiling must not be discriminatory and that potential biases should 
be  anticipated  and  prevented.  Recital  57  of  the  AI  Act  acknowledges  that  AI  systems  used  for 
recruitment  processes,  evaluation,  promotion,  or  retention  of  persons  in  a  working  position  may 
perpetuate  historical  patterns  of  discrimination  against  inter  alia  women,  certain  age  groups, 
persons with disabilities, or persons of certain racial or ethnic origins or sexual orientation. This is 
the reason why such AI systems are expressly qualified as high-risk AI systems and their technical 
documentation  shall  contain  detailed  information  on  the  potential  discriminatory  impacts  it  may 
have219.  

Article 10 of the European Framework Convention on AI sets out two distinct obligations on equality 
and non-discrimination. First, the Parties to the Convention shall adopt rules to uphold the right to 
non-discrimination  and  equality,  including  explicitly  gender  equality.  Second,  Parties  are  also 
required  to  establish  rules  aimed  at  overcoming  any  inequality  issue  arising  from  the  use  or 
functioning of AI systems throughout their lifecycle. The AI Act establishes mechanisms to ensure 
high-risks AI systems' harmful potential are addressed. For instance, Article 15(4) sets out specific 
obligations in relation to high-risk AI systems' accuracy and resilience against errors, inconsistencies, 
faults and biases. Operators shall adopt technical and operational measures in that regard such as 
fail-plans and backups. This provision also requires such AI systems to be developed in a way that 
eliminates  biased  outputs,  taking  into  consideration  these  AI  systems'  environment  and  their 
interactions with natural persons.  

The PWD sets out a number of specific safeguards for non-discrimination in relation to AM by digital 
platforms. For instance, Article 7(1) PWD prohibits platforms from processing data that reveal race 
or ethnic origin, disability, health, trade union membership, religion, etc. when deploying automated 
monitoring or decisionmaking systems. Moreover, Article 10, which sets up the requirement to carry 
out regular risk assessments of AM associated risks contains a specific obligation for the employer 

216   Buolamwini and Gebru, 2018. 

217   CJEU C-443/15, David L. Parris v Trinity College Dublin and Others. 

218   See, for example, CJEU, C-354/13, Fag og Arbejde (FOA) v Kommunernes Landsforening (KL). 

219   Article 11(1) and Annex IV of the AI Act. 

60 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

to modify or switch off any systems that entail a high risk of discrimination. As noted by Eurofound, 
the PWD thus sets up a 'bias-monitoring' duty set in relation to platform work220. 

In the Netherlands, the Equal Opportunities in Recruitment and Selection Act has been introduced very 
recently  (2025).  It  aims  at  combating  discrimination  in  the  recruitment  and  selection  process  including 
when  algorithms  are  used.  It  requires  employers  with  more  than  25  employees  to  develop  a  publicly 
accessible procedure to explain how discrimination is prevented.  

No other examples or initiatives were identified in the other selected Member States.  

3.2.6. Health and safety  

Risk assessment 

The general rules on the prevention and protection of workers in the workplace are set out in the 
OSH  Framework  Directive.  A  core  element  of  workplace  safety  is  the  risk  assessment221.  This 
assessment involves identifying potential hazards, analysing the risks they create and implementing 
measures to mitigate or eliminate them. Another key feature of the OSH Framework Directive is the 
emphasis  on  a  proactive,  risk-based  approach:  employers  have  the  duty  to  keep  themselves 
informed of the last advanced technologies and scientific findings222, and to adapt the work to the 
individuals  and  to  the  'choice  of  working  and  production  methods',  as  well  as  to  'technical 
progress'223. This implies that risk prevention measures need to be modified if circumstances evolve, 
including technological developments or working conditions. To this end, the EU-OSHA and the EU 
sectoral social dialogue partners have developed a new Online interactive Risk Assessment (OiRA) 
tool to help micro and small enterprises (MSEs) assess occupational safety and health risks related 
to task automation224. 

Without prejudice to general OSH rules, Article 12 of the PWD sets out specific obligations for the 
health  and  safety  of  persons  performing  platform  work.  Platforms  have  to  evaluate  the  risks 
associated with the use of automated monitoring and decisionmaking systems, focusing for instance 
on psychosocial issues resulting from the use of such systems. The adequacy of safeguards already 
in  place  have  to  be  assessed  ensuring  that  they  are  still  appropriate  for  the  risks  identified. 
Preventive and protective measures have to be implemented to mitigate such risks.  

Specific risks linked to AM optimisation 

Pursuant to the OSH Framework Directive, employers are obliged to ensure safety and health of 
workers 'in every aspect related to the work'225, which encompasses workers' mental health. One of 
the potential risks associated with the use of AM systems is the increase of psychological stress and 

220   Eurofound, 2024. 

221   Article 9(1) of the OSH Framework Directive. 

222   Preamble of the OSH Framework Directive.  

223   Article 6(2), points (d) and (e) of the OSH Framework Directive. 

224   See: OSHA, Safe automation of work with new Online interactive Risk Assessment tool, 2025.  

225   Article 5 of the OSH Framework Directive.  

61 

 
 
EPRS | European Parliamentary Research Service 

anxiety226 due to constant monitoring or the pressure of the need to optimise work processes. The 
broad formulation of the principles and obligations set out in the Directive may serve as a protective 
framework for mental health as well. Moreover, the Directive explicitly highlights the importance for 
employers to alleviate 'monotonous work and work at predetermined work-rate'227 and to consider 
their effect on mental health. 

Other specific risks relate, for example, to the continuous use of computers or laptops, specific tools 
and to the workplace physical environment. The Display Screen Directive228, for example requires 
employers to assess workstations, evaluating the safety and health conditions with particular regard 
to  risks  to  eyesight,  physical  problems  and  problems  of  mental  health229  arising  from  the  use  of 
display screen equipment230. The Directive also contains requirements for software which shall be 
easy to use and adaptable to the worker's level of knowledge and experiences. Moreover, there shall 
be no 'quantitative or qualitative checking facility' without workers' being aware of and the principles 
of  software  ergonomics  must  be  applied,  in  particular  to  human  data  processing231.  While  the 
Directive was originally designed for traditional IT systems, its ergonomic principles provide a critical 
foundation  for  ensuring  that  AM  systems  remain  human-centred,  transparent,  and  supportive  of 
workers' wellbeing. 

Additional  requirements  for  the  workplace  or  work  equipment  are  contained  in  the  Workplace 
Directive232 and in the Work Equipment Directive233. Employers must consider a number of factors 
with the view to safeguarding health and safety of the workplace, such as light, ventilation, doors, 
emergency  routes,  but  also  technical  devices  and  electrical  installations234.  The  latter  is  broadly 
defined in the Directive, as the place where workstations is as well as any other place within the area 
of the undertaking to which the workers have access in the course of their employment235. Work 
equipment,  i.e.,  'any  machine,  apparatus,  tool  or  installation  used  at  work'236  must  also  be  safe, 
suitable  for  the  work  to  be  carried  out  and  must  be  used  without  impairing  workers'  safety  and 
health237.  

226   OSHA, 2024. 

227   European Commission, 2023. 

228   Council Directive 90/270/EEC, consolidated text.  

229   Article 3(1) of the Display Screen Directive. 

230   Display screen equipment is defined as: 'an alphanumeric or graphic display screen, regardless of the display 

process employed' (Article 2).  

231   Point 3 of the Annex.  

232   Council Directive 89/654/EEC. 

233   Directive 2009/104/EC. 

234   Article 6 of the Workplace Directive. 

235   Article 2 of the Workplace Directive.  

236   Article 2(a) of the Work Equipment Directive.  

237   Article 3 of the Work Equipment Directive.  

62 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

More specific rules are in place for persons performing platform work as the PWD contains a precise 
obligation to avoid deploying automated systems in a way that exerts undue pressure on workers or 
endangers their physical or mental wellbeing. The scope of this obligation applies not only to fully 
automated decisionmaking systems but also to any automated systems that support or influence 
decisions  impacting  workers  in  any  aspect,  including  their  safety,  working  conditions,  or  rights. 
Finally,  Member  States  are  tasked  with  ensuring  that  digital  labour  platforms  take  preventive 
measures to protect workers from violence, harassment, and other hazards, including establishing 
effective reporting channels so workers can report safety concerns or incidents. 

EU  Member  States  have  general  health  and  safety  regulations  implementing  the  EU  OSH  regulatory 
framework. The Working Conditions Act in the Netherlands, for example, requires employers to draw up a 
risk inventory to identify and evaluate all hazards and risks to employees in the workplace. Such risks can 
cover potential hazards arising from the use of AM systems. Other Member States have similar regulatory 
frameworks, setting out broad and general obligations which, however, may cover but do not explicitly 
address AM systems in the workplace.  

No other examples or initiatives were identified in the other selected Member States. 

3.2.7. Social dialogue (e.g. collective bargaining, social partners agreements)  

While  AM  tools  can  improve  productivity  and  planning,  they  also  risk  undermining  traditional 
mechanisms of workplace democracy and collective representation. Social dialogue is a fundamental 
component of the European social model238. The EU fosters social dialogue through negotiations, 
consultations  or  exchange  of  information  in  either  bipartite  (e.g.  collective  bargaining  between 
social partners) or tripartite relation (e.g. dialogue between public authorities and social partners).  

The importance of social dialogue is recognised in EU primary law, especially in Articles 151 – 156 of 
the TFEU as well as in Article 27 of the Charter that protects workers' right to consultation within 
the employer and Article 28 that protect workers' rights to collective bargaining and action. 

As  illustrated  above  (see  section  3.2.1),  the  importance  of  consultation  in  the  workplace  is 
recognised  also  by  EU  labour  and  OSH  legislation.  The  Information  and  Consultation  Directive 
recognises the need to strengthen dialogue and information and consultation on situations which 
will  impact  employment  situations,  or  on  the  measures  taken  to  anticipate  potential  risks239. 
Consultations  are  needed  for  any  decisions  of  the  employers'  that  may  lead  to  a  'threat  to 
employment' or 'to substantial changes in work organisation or in contractual relations'240. Recent 
Eurofound and EU-OSHA reports highlight that such existing legal obligations should be understood 
as including decisions on the deployment and use of AM systems in the workplace. Nevertheless, 
legal scholars argue there is a need to make such obligations more explicit.  

Procedural  rules  on  when  and  how consultations shall  take  place  are  also  set  out  in  EU  law.  The 
reorganisation  of  work  or  changes  in  situations  resulting  from  the  adoption  of  AM  systems  may 
require  employers  to  consult  with  their  employees  and  their  representatives.  However,  this 

238   Principle 8 of the European Pillar of Social Rights. 

239   Preamble of the Information and Consultation Directive.  

240   Article 4(2) of the Information and Consultation Directive.  

63 

 
 
 
  
EPRS | European Parliamentary Research Service 

obligation is not evident in the legislation. The European Works Council Directive241 further enhances 
consultation obligations in the context of undertakings or groups of undertakings established across 
different Member States242. The question of consultation on the topic of deployment of AM in the 
workplace is not addressed. 

It seems that, at least for the time being, social dialogue regarding AI topics is not particularly well 
developed243. However, in recent years, EU social partners have concluded a series of autonomous 
agreements  concerning,  for  example,  aspects  such  as  telework244,  work-related  stress245  and 
digitalisation 246. These framework agreements reflect the social partners' efforts to modernise work 
organisation, adapt to evolving workplace realities, and promote the integration of new technologies 
in a way that minimises risks to workers. Measures targeted the use of AM tools in the workplace are 
contained  in  the  Framework  Agreement  on  Digitalisation.  This  Framework  Agreement  has  been 
concluded by the European cross-sectoral social partners and therefore applies to the whole EU 
territory as well as the territories of the European Economic Area (EEA). It covers all workers and 
employers of both the public and private sector notwithstanding their form or activities, including 
economic  activities  using  online  platforms.  The  Framework  Agreements  applies  to  all  forms  of 
working relationship as defined under national law247. According to this agreement, the deployment 
of AI systems in the world of work should: (i) follow the human-control principle, (ii) undergo risk 
assessment to prevent any harm (physical and psychological), (iii) be fair and ensure avoidance of 
bias and discrimination, (iv) transparent and explicable with effective oversight. If AM is employed 
in HR procedures (e.g., recruitment, performance analysis), its use must be transparent and subject 
to human intervention if requested by the worker, who is always allowed to contest the decision. 
Information on the relevant agreements covering AM provisions at Member States level is available 
in the UNI Europa database248, which will be further explored with regard to the six selected Member 
States. 

The digital instruments do not really regulate social dialogue. The exception is Article 26(7) of the AI 
Act, which requires the employers, who are deployers of high-risk AI systems at the workplace, to 
also inform workers' representatives and the affected workers that they will be subject to the use of 
the  high-risk  AI  system.  This  obligation  is  limited  to  the  provision  of  information  and  does  not 
necessitate consultations with workers' representatives. Additionally, the AI Act does not preclude 
the adoption or preservation of more favourable national legislation and to particularly encourage or 
allow the adoption of collective agreements249. Recital 9 further details that the AI Act should not 

241   Directive 2009/38/EC. 

242   Member States are to provide for the right to establish European Works Councils in companies or groups of 

companies with at least 1000 employees in the EU and the other countries of the European Economic Area, when 
there are at least 150 employees in each of two Member States. 

243  

industriALL, 2022. 

244   Framework agreement on telework, 2002. 

245   Framework agreement on work-related stress, 2004. 

246   Framework agreement on digitalisation, 2020.  

247   European Framework Agreement on Digitalisation, Scope.  

248   UNI Europa, Database of AI and AM in collective bargaining agreements.  

249   Recital 9 and Article 2(11) of the AI Act. 

64 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

affect the exercise of workers' fundamental rights recognised at the EU and national levels, including 
the right to negotiate, to conclude and enforce collective agreements.  

On the other hand, PWD ensures that workers' representatives are provided with comprehensive 
details about AM systems in advance of their deployment and whenever relevant changes occur. 
Also, authorities can request this information at any time. 

65 

EPRS | European Parliamentary Research Service 

At national level, social partners play a more important role in the protection of workers’ interests in AI 
matters. In the Netherlands and Spain collective bargaining agreements and social dialogue are advanced. 
In the Netherlands, several collective labour agreements now address the use of AI in the workplace. For 
instance,  the  provisional  Labour  agreement  for  the  insurance  industry  creates  an  obligation  to  inform 
employees on AI impacts on their work. It also includes guidelines for the adequate regulation of AI use 
between employers and employees. In Spain, alongside several sectoral collective agreements containing 
protective provisions on AI, the National Collective Agreement of May 2023 (V AENC) establishes similar 
provisions that apply to the entire territory. 

In France, collective bargaining agreements remain limited to company agreements, mostly concluded in 
the communication, financial, and industrial manufacturing sectors. However, national authorities strongly 
encourage  the  use  of  social  dialogue  at  all  stages  of  AM  systems  deployment  through  supporting 
documents.  Recently,  the  Dial-IA  initiative  involved  50  representatives  of  trade  union  and  employers’ 
representative organisations  from the public and private  sectors, academics and national authorities to 
reflect on how to strengthen social dialogue relating to AI was launched. This initiative led to the adoption 
of  a  manifesto  and  a  comprehensive  toolbox  to  guide  collective  bargaining  and  implement  the  2020 
European Social Partner Framework Agreement. This toolbox includes inter alia, a basic knowledge sheet 
on AI, a stake mapping, a list of arguments in favour of social dialogue and a mapping of leverages available 
to social partners to effectively engage in social dialogue on AI. 

Some  Member  States  also  secured  workers’  rights  to  information  and  consultation  through  their 
representatives. In Sweden, the Co-determination Act creates a procedural obligation for employers to 
negotiate  before  introducing  significant  technological  or  structural  changes.  Similarly,  in  France,  the 
Labour  Code  explicitly  requires  employers  to  consult  work  councils  before  the  introduction  of  new 
technologies that can affect employees’ OSH or working conditions. To issue an informed opinion the work 
council  can  request  an  expert’s  opinion.  In  the  Netherlands,  the  Works  Councils  Act  (WOR)  requires 
companies’ work councils to be consulted on the introduction of important technological innovations and 
to be asked for their consent when such instruments may affect employees’ evaluation or personal data 
processing. Pursuant to Article 27(k) of the WOR, work councils have the right to express their consent to 
introduction of a system that collects, stores and uses personal data (e.g. appointment policy, sick leave, 
etc.). However, those obligations only apply to medium to larger companies subject to the obligation to 
have an internal work council.  

National  case-law  however  reveals  discrepancies  and  uncertainties  regarding  the  scope  of  employees’ 
right to information, consultation or participation. In both France and Germany, national courts have been 
asked to clarify whether work councils and workers’ right to co-determination extended to the deployment 
of AM systems in the workplace. In France, the Judicial Tribunal of Nanterre departed from the Court of 
Cassation’s past case-law and found that work councils had to be consulted on the future deployment of 
AM systems prior to any deployment or training on such tools. In Germany, the Hamburg Court found that 
the mere authorisation for employees to use generative AI tools to perform their tasks required workers’ 
consultation but did not justify the application of co-determination procedures.  

In Poland, trade unions call for further involvement in discussions relating to AM in the workplace. On 4 
December 2024, the National Commission Presidium of NSZZ 'Solidarność' adopted a Position on artificial 
intelligence in the workplace emphasising the need for a responsible implementation of AI technologies 
that  minimises  the  potential  negative  effects.  Solidarność  further  urges  regulators  to  ensure  that 
employees are duly consulted prior to the implementation of AI systems and that their right to privacy is 
upheld. 

No other examples or initiatives were identified in the other selected Member States.  

66 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

3.2.8. Redress (individual and collective) and enforcement  

EU law provides access to justice in relation to infringement of EU primary and secondary legislation, 
which can be invoked in court, including in the area of labour law. There is not one specific EU legal 
instrument setting up a mechanism for collective redress, though specific legal instruments provide 
legal standing to workers representatives, such as trade unions, or NGOs to bring claims on behalf 
of  workers.  This  is,  for  instance,  the  case  in  equal  treatment  legislation  and  more  recent  labour 
legislation, such as the Directive on minimum wages.  

In addition to the human oversight requirements set up by the PWD in relation to digital platform 
work (including human review and the right to ask for corrections, see above), Article 18 of the PWD 
provides persons providing work through digital platforms with a general right to redress. The PWD 
extends  such  rights  to  persons  whose  employment  or  contractual  relationship  has  ended  and 
requires  dispute  resolution  to  be  timely,  effective  and  impartial.  Moreover,  the  PWD  requires 
Member  States  to  guarantee  adequate  compensation  for  any  damaged  sustained  resulting  from 
infringements of the PWD. Additional procedural guarantees are included in the PWD, such as the 
possibility for courts and competent authorities to order platforms to disclose evidence (Article 21 
PWD)  and  protection  against  adverse  treatment  and  dismissal  (Articles  22  and  23  PWD).  Legal 
standing is provided to expressly provided worker representatives and NGOs.  

The European Framework Convention on AI sets out obligations for the establishment of effective 
remedies to challenge AI practices and outputs that breach fundamental rights or the rule of law. 
Effective  and  accessible  remedies  shall  be  put  in  place  to  allow  victims  of  an  AI  system  activity 
breaching  their  fundamental  rights250.  In  that  context,  relevant  information  must  additionally  be 
shared with the affected persons and the relevant national competent authorities to enable them to 
exercise  their  control  and  seek  redress.  The  Convention  also  provides  for  the  establishment  of 
effective procedural guarantees to allow individuals who are victims of a wrongful AI system that 
have  a  significant  adverse  impact  on  the  enjoyment  of  their  right  to  act  against  it251.  Article  26 
commands Parties to establish effective oversight bodies to monitor the correct application of the 
obligations  resulting  from  the  Convention.  This  provision  also  foresees  an  obligation  of  effective 
administrative cooperation with human rights institutions and other authorities having overlapping 
competences.  

The  AI  Act  contains  similar  mechanisms,  although  it  organises  a  more  complex  administrative 
oversighting structure focusing on market surveillance. These mechanisms cover all aspects of the 
economic  lifecycle  of  AI systems  and models,  from their  placement  on  the market252  to  ordinary 
market surveillance functions253. Similarly to the European Framework Convention on AI, Article 77 
of the AI Act requires operators to share appropriate documents with national authorities competent 
to protect fundamental rights to facilitate their protective mandate. On that ground, fundamental 
rights  bodies  are  entitled  to  access  to  documents  and  information  to  investigate  on  AI  systems 
activities likely to have a damaging effect on fundamental rights, within the remit of their jurisdiction 

250   Article 14(1) of the Framework Convention. 

251   Article 15 of the Framework Convention. 

252   Article 72 of the AI Act. 

253   Article 74 to 84 of the AI Act. 

67 

 
EPRS | European Parliamentary Research Service 

and  competence.  They  are  also  mandated  to  inform  and  cooperate  with  market  surveillance 
authorities where necessary254. AI system providers are also mandated to report incidents caused 
by AI systems to the competent market authority in a timely manner. The AI Act also establishes a 
set of remedies for users and subject of a wrongful decision grounded on an AI output to exercise 
their rights. Individuals are indeed allowed to lodge a complaint to market surveillance authorities255, 
make use of the protection granted to whistleblowers256. The AI Act does not contain any provision 
foreseeing  collective  redresses.  As  a  market-oriented  legislation  it  primarily  focuses  on  market 
surveillance and cooperation between AI system providers and deployers. Nonetheless, the general 
clause laid down in Article 2(11) of the AI Act leave Member States an open-ended regulatory choice 
to maintain or introduce more favourable rules to enhance workers' ability to exercise their rights in 
an  employment  context,  including  by  allowing  the  application  of  more  favourable  collective 
agreements257. 

The GDPR specifies remedies for workers with respect to the processing of their personal data. The 
Regulation  sets  out  the  rights  of  data  subjects,  with  the  aim  of  giving  them  control  over  their 
personal data. In the context of automated processing by AM tools, especially the right to access to 
the personal data and related information258, the right to object to259 or restrict the processing260, 
the right to data portability261 and the rights connected with automated individual decisionmaking262 
are of relevance. In addition, one or more independent data protection authorities (DPOs) ensure 
the effective application of data protection rules across all sectors263. These authorities can handle 
complaints lodged by data subjects and, where appropriate, investigate the subject matter of the 
complaint264.  The  DPO  can  issue  warnings  to  ensure  compliance,  impose  temporary  or  definitive 
bans,  and  issue  administrative  fines265.  When  it  comes  to  employment  matters,  however,  many 
national DPAs often lack the legitimacy and interest to do so. Although fines are not often imposed 
in the employment sector266, the average amount is higher than in other sectors267. 

The above-listed remedies and enforcement actions are linked to the individual rights of workers. 
According  to  Article  80  of  the  GDPR,  trade  unions  and  workers'  representatives  can  assist 
employees exercise their rights, but the GDPR does not give them any legal standing. Whether such 
bodies  can  launch  collective  actions  and  enforce  data  subjects'  rights  depends  on  national  legal 

254   Article 77(1) of the AI Act. 

255   Article 85 of the AI Act. 

256   Article 87 of the AI Act. 

257   See Article 2(11) and Recital 9 of the AI Act. 

258   Article 15(1) of the GDPR. 

259   Article 21 of the GDPR. 

260   Article 18 of the GDPR. 

261   Article 20 of the GDPR. 

262   Article 22 of the GDPR. 

263   Article 51(1) of the GDPR. 

264   Article 57 of the GDPR. 

265   Article 58 of the GDPR. 

266   Employment is eighth in the list of the 11 sectors, ranked by the number of fines. See: GDPR Enforcement Tracker.  

267   Employment is the third sector based on the sum of fines. See: GDPR Enforcement Tracker.  

68 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

systems. The main drawback of the current system is thus the lack of collective rights relating to 
transparency, information, human oversight, DPIA and the right to an explanation, as workers are 
very unlikely to take individual action due to a lack of knowledge, motivation and fear of retaliation 
by the employer. Additionally, AM tools also use data that is outside the scope of the GDPR, such as 
statistics  and  anonymised  data,  or  data  related  to  other  individuals,  for  which  there  are  limited 
possibilities for individual action.268 

The distribution of powers relating to the use of AM systems at work is heterogenous. In all Member States, 
the use of AM systems in the workplace falls within the ordinary competence of labour inspectorates. There 
is no example of specific units, methods or tools available to labour inspectors to monitor the effects of 
AM  systems  on  workers.  Representatives  of  the  French  labour  inspectorates  suggested  that  further 
training would be necessary to adequately understand and address such new matters.  

At  the  same  time,  in  most  Member  States,  data  protection  authorities  acted  through  their  GDPR 
prerogatives  to  oversight  the  use  and  challenges  of  AM  systems,  including  in  employment  matters.  As 
illustrated  in  Section  3.2.4,  the  French  and  Dutch  data  protection  authorities  used  their  administrative 
punitive powers to fine companies for their use of intrusive AM system and automated decisionmaking 
processes at work. 

In  the  Netherlands,  since  2023,  the  data  protection  authority  has  been  appointed  as  the  coordinating 
supervisory authority for algorithms and AI that threaten fundamental rights. For the implementation of 
the AI Act, it specifically created the Coordination of Algorithmic Oversight Department (DCA), a separate 
organisational  unit  which  supervises  the  use  of  algorithms  and  AI.  This  unit  provides  overarching  risk 
analysis, strengthens cooperation between supervisory authorities and contributes to the development of 
new legal frameworks. Similarly, the national AI authorities have responsibilities to enforce AI safety rules. 
For instance, Spain established a new AI Supervisory Agency (AESIA) competent to implement the AI Act 
and exercise technical and legal oversight to ensure the safe and ethical use of AI at the national level. 

Beyond ordinary judicial actions and remedies to the data protection authorities, national laws do not have 
specific collective redress mechanisms addressing breaches of workers’ rights by AM systems. No other 
examples or initiatives were identified in the other selected Member States.  

3.3. Conclusion 

This study carried out a mapping of key provisions and mechanisms relevant to the protection of 
workers during the deployment of AI and AM in the workplace. This exercise reveals that, while the 
EU digital acquis establishes a comprehensive regulatory framework for the use of AM across all 
sectors,  and  certain  provisions  within  EU  labour  and  OSH  legislation  could  be  construed  as 
addressing issues related to AM systems in the workplace, the unique characteristics of workplace 
organisation give rise to new challenges.  

The  regulatory  mapping  shows  that  the  existing  EU  legal  framework  already  provides  several 
important safeguards for workers in relation to AM, even though it was not designed primarily with 
this purpose in mind. Instruments such as the GDPR guarantee strong individual rights over personal 

268   See presentation of the Tobias Müllensiefen, European Commission / EUI, Worker Protection Between the GDPR 
the AI Act, at the EUI seminar AI and algorithms at the workplace: Socio economic implications and regulatory 
challenges on 5 May 2025. 

69 

  
 
EPRS | European Parliamentary Research Service 

data and establish some limitations to automated decisionmaking. The AI Act explicitly designates 
AM tools used in employment as high-risk AI systems, triggering obligations on transparency, human 
oversight and conformity assessments. The OSH framework imposes binding duties on employers 
to ensure safe and healthy working environments, which can extend to psychosocial risks linked to 
monitoring  and  algorithmic  scheduling.  Together,  these  measures  provide  certain  general 
safeguards. To address the specific challenges of platform work, the PWD introduces targeted rules 
on transparency, fairness, human oversight, safety and accountability  in one of the sectors most 
affected by AM. At the same time, the current framework continues to display gaps and limitations.  

The regulatory analysis above is structured around the main protection mechanisms, set out in EU 
legislation,  and  features  needed  for  the  sound  deployment  of  AM  in  the  workplace,  taking  into 
consideration the Guidelines from the High-Level Expert Group. It also considered the use cases of 
AM in the workplace highlighted in the previous sections of this report. Based on this analysis, the 
main regulatory gaps at EU level have been identified as follows. 

The  EU  legislation  on  working  conditions  typically  applies  to  'workers',  the  definition  of  which  is 
largely left to Member States. As a result, certain individuals, such as persons performing platform 
work, workers in non-standard forms of employment (e.g. seasonal workers, on-call workers) or the 
(solo)  self-employed269,  may  not  be  covered  by  the  full  set  of  labour  law  guarantees.  The  PWD 
extends  the  specific  protection  mechanisms  for  the  use  of  AM  systems  to  persons  performing 
platform  work,  including  those  in  non-traditional  employment  relationships  and  (solo)  self-
employed.  However,  its  protection  mechanisms  specifically  designed  for  the  deployment  of  AM 
apply only to the limited category of persons performing platform work, and not to other sectors of 
the economy. By contrast, digital acquis instruments such as the AI Act and the GDPR have a broader 
scope of application and do not offer specific protection mechanisms just for workers. As a result, 
workplace AM is only regulated indirectly through the obligations imposed on employers as business 
operators  or  as  data  controllers  or  processors,  unless  national  rules  specifically  fill  this  gap.  This 
leads  to  fragmentation  and  uneven coverage  across  employment  statuses  and  between Member 
States. 

In  relation  to  information  and  transparency  obligations,  existing  labour  and  OSH  rules  contain 
general duties to inform and consult workers, but they do not explicitly require disclosure about the 
use of AM systems. Moreover, the Information and Consultation Directive does not apply to smaller 
undertakings or establishments with less than 50 or 20 workers, respectfully. The AI Act introduces 
transparency duties, such as notifying workers when they are subject to a high-risk AI system and 
requires  'transparency  by  design'  for  AI  developers.  Such  information  requirements  specifically 
cover information provision to workers, though they are less specific than those set out in the PWD 
for persons performing platform work.  

Some of the legal instruments analysed require specific safeguards relevant for the deployment of 
AM systems, such as human oversight, the right to explanation, and impact assessments. However, 
the  analysis  has  shown  some  limitations  to  some  of  these  safeguards  when  it  comes  to  the 
protection  of  workers'  rights.  For  instance,  the  AI  Act  requires  AI  system  providers  to  share 

269   To be considered a worker under EU labour law, a self-employed person must meet three distinct criteria. See 

Section 3.1.2 for more information.  

70 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

information  that  allows  human  oversight  to  be  carried  out  by  deployers  and  establishes  an 
information  obligation  on  the  use  of  AI.  The  GDPR  contains  an  obligation  to  provide  'meaningful 
information' about automated decisions and restricts the use of fully automated decisionmaking. 
The PWD contains more specific requirements in relation to human oversight (e.g. right to override 
AM systems' decisions) as well as obligations for employers to appoint trained humans with authority 
and  resources,  as  well  as  a  right  to  human  review  and  a  detailed  right  to  explanation.  The  PWD 
applies  such  obligations  not  only  to  fully  automated  monitoring  systems  and  automated 
decisionmaking  systems  (which  are  subject  to  more  stringent  limitations),  but  also  to  partially 
automated AM systems. Such specific safeguards are limited to persons performing platform work 
and do not extend to other workers. Ex-ante assessments are also subject to limitations in scope: 
the AI Act foresees Fundamental Rights Impact Assessments (FRIAs), which do not apply to private 
employers  unless  they  are  providing  public  services.  The  GDPR  requires  Data  Protection  Impact 
Assessments (DPIAs), but only when specific risk thresholds are met, which may leave out several 
deployments of AM270.  

Working-time  rules,  such  as  maximum  weekly  hours,  daily  and  weekly  rest  periods,  and 
predictability of schedules, apply across the board. However, the acquis lacks specific safeguards to 
deal  with  algorithmic  scheduling,  algorithm-driven  pace  setting,  or  the  intensification  of  work 
through automated monitoring. Targeted protections appear in the PWD but apply only to persons 
performing platform work. 

The  GDPR  provides  strong  safeguards  for  individuals  in  relation  to  personal  data  processing. 
However,  it  does  not  take  into  account  the  structural  power  imbalance  between  employers  and 
workers. This makes reliance on consent problematic, as it cannot be considered 'freely given' in 
most  employment  situations.  Moreover,  the  GDPR  provides  limited  collective  enforcement 
mechanisms, focusing instead on individual rights. Article 88 of the GDPR allows Member States to 
introduce  more  specific  rules  for  employee  data  processing,  but  this  has  resulted  in  divergent 
regimes  across  the  EU.  The  AI  Act  complements  the  GDPR  by  imposing  governance  and 
transparency duties on high-risk AI systems, but it does not create workplace-specific privacy rights 
for workers, unlike those that have been developed for persons performing platform work in the 
PWD. 

EU equality legislation applies to AM insofar as it affects protected grounds such as sex, age or 
ethnicity.  However,  in  practice,  proving  discrimination  is  difficult  because  of  the  lack  of 
transparency and complexity of algorithmic systems. Furthermore, EU equality law is based on a 
closed list of protected grounds, which does not cover other risks associated with AM such as bias 
linked to employment type, socioeconomic status, or personality traits. The AI Act recognises the 
risk of discriminatory outcomes and classifies many AM tools as high-risk AI systems, but it does not 
impose robust, ongoing bias detection or mitigation obligations on employers who deploy them. The 
PWD  requires  algorithmic  bias  monitoring  and  even  the  obligation  to  modify  the  automated 
decisionmaking  system  or  the  discontinuation  of  its  use,  in  order  to  avoid  such  decisions  in  the 
future. However, this obligation is limited to platform work. 

270   EDPB, Guidelines on Data Protection Impact Assessment (DPIA) and determining whether processing is 'likely to 

result in a high risk' for the purposes of Regulation 2016/679, wp248rev.01.  

71 

 
EPRS | European Parliamentary Research Service 

The  EU  OSH  framework  is  technology-neutral,  which  means  it  can  apply  to  psychosocial  risks 
created  by  AM,  such  as  stress,  pressure  from  constant  monitoring,  or  unpredictable  schedules. 
However,  the  directives  do  not  contain  any  algorithmic  management-specific  duties,  such  as 
obligations to address AI-driven pace-setting, performance surveillance, or ergonomic risks linked 
to  digital  interfaces.  The  only  EU  instrument  to  impose  targeted  requirements  on  employers  to 
address such risks is the PWD. Furthermore, EU OSH legislation generally requires employers to 
involve workers in risk assessment, but there is no explicit EU-wide requirement to cover AM-related 
risk assessment and monitoring. 

EU  law  requires  information  and  consultation  in  cases  of  major  organisational  changes.  The 
deployment of AM systems is not explicitly included as such as trigger, leaving uncertainty as to the 
application of the consultation obligations. The AI Act contains a duty to inform workers when high-
risk AI systems are used but does not regulate the involvement of the social partners. Finally, small 
undertakings are excluded from the scope of participation rights under EU law. The PWD contains 
specific consultation obligations relating to the deployment of AM systems for persons performing 
platform work.  

Finally,  remedies  for  workers  affected  by  AM  are  fragmented  across  different  legal  frameworks. 
Under  the  GDPR,  workers  can  file  complaints  with  data  protection  authorities,  though  these 
procedures are focused on the use and processing of personal data. Despite the limitation in scope, 
it  is  important  to  note  that  the  enforcement  mechanisms  under  the  GDPR  have  been  used  by 
workers in several Member States in relation to the deployment of AM systems. The AI Act relies on 
market  surveillance  authorities  to  monitor  compliance  rather  than  authorities  focused  on  the 
protection  of  working  conditions  and  fundamental  rights.  Finally,  labour  and  OSH  legislation 
enforcement varies significantly between Member States. Collective redress mechanisms are scarce 
outside  the  platform  economy,  which  means  workers  often  lack  effective  collective  standing  to 
challenge the use of AM. Finally, it is important to note that the mandates of different supervisory 
authorities  are  not  aligned,  leaving  no  single  authority  with  responsibility  for  overseeing  the  full 
impact of AM in the workplace. 

The table below provides an overview of the main mechanisms for each type of legal instrument, as 
well as highlighting gaps in legal protection.

72 

Table 7 – Overview of the legal assessment 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of work 

PROTECTION 
MECHANISMS 

Scope (material and 
personal) 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

The PWD applies to 
automated monitoring and 
automated decisionmaking 
systems.  

However, its scope is limited 
to the platform economy, and 
relevant provisions only apply 
to persons performing 
platform work (this includes 
solo self-employed). 

Existing labour and OSH directives 
are technologically neutral but can 
be interpreted so that their general 
duties (information and consultation, 
risk management) also apply when 
AI/AM tools are introduced. 
Nevertheless, none of them 
explicitly address the risks 
associated with the deployment/use 
of AM systems. 

EU labour and OSH rules apply to 
'workers', a term defined by the case 
law of the CJEU. While Member 
States can implement more 
favourable definitions, genuinely 
self-employed individuals are in 
principle not covered. 

The AI Act and the 
Framework Convention 
broadly apply to all forms of 
AI systems, including AI 
systems used in 
workplaces. Most AM 
systems deployed in 
workplaces are expressly 
qualified as high-risk AI 
systems, subject to the 
more stringent provisions of 
the AI Act. 

On the other hand, the 
GDPR is technologically 
neutral. It applies 
safeguards to all processing 
operations, including AM 
systems, and distinguishes 
between personal and non-
personal data. The latter is 
regulated to ensure free 
circulation and innovation, 
rather than rights 
protection. 

Although the PWD is the 
most comprehensive piece of 
legislation on the use of AM 
tools in the workplace, its 
material and personal scope is 
limited. 

As overarching legislation, the 
scope of the EU digital acquis 
is broad enough to cover 
most AM tools as well as all 
types of workers (this 
includes solo self-employed). 
However, none of these 
instruments are specifically 
designed to address 
challenges at the workplace. 

The existing labour law and 
OSH acquis applies, but is 
limited to workers in an 
employment relationship, 
excluding workers in non-
traditional forms of work (e.g. 
(solo) self-employed workers, 
casual workers) and 
sometimes excludes small 
companies (e.g. consultation). 

73 

 
EPRS | European Parliamentary Research Service 

PROTECTION 
MECHANISMS 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

Moreover, while several legal 
instruments can be 
interpreted to provide a 
minimum level of protection 
to workers' rights (e.g. 
requirement to do a risk 
assessment, information 
requirement), there are 
important limitations as the 
protection mechanisms are 
not specifically tailored to the 
risks resulting from AM 
systems in the workplace.  

No EU instrument guarantees 
uniform AI and AM-specific 
information and consultation 
rights for persons not 
performing platform work or 
for workers in undertakings 
with fewer than 50 employees 
on in establishments with 
fewer than 20 employees.  

Existing obligations are not 
specific about the use of AI or 
AM systems, specific features 
thereof, or updates in case of 
system changes.  

Information, consultation 
and transparency 

The principle of 
transparency is emphasised 
in the AI Act and the GDPR.  

The AI Act specifically sets 
out workers' and workers' 
representatives' right to 
information prior to high-
risk AI system deployment 
in the workplace.  

Article 9 of the PWD requires 
platforms to provide persons 
performing platform work, 
and authorities with detailed 
written explanations of any 
automated monitoring or 
decisionmaking systems, and 
to update these explanations 
whenever the systems 
change. 

Workers have a general right to be 
informed and consulted before 
'substantial changes in work 
organisation', a notion that can 
encompass the deployment of AM. 
However, there is no explicit 
requirement to disclose information 
about the use of AM systems, or 
specific features thereof.  

Moreover, the Information and 
Consultation Directive applies only 
for undertakings with more than 50 
employees and for establishments 
with more than 20 employees.  

74 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of work 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

PROTECTION 
MECHANISMS 

Human oversight and the 
right to explanation 

Neither EU labour nor OSH law 
contains an explicit duty to keep 
humans 'in the loop', ensure human 
oversight or explain algorithmic 
decisions. 

The AI Act sets out 
provisions on human 
oversight.  

The AI Act mainly relies on 
AI system providers to 
share sufficient information 
allowing the performance of 
human oversight by 
deployers. At deployers' 
level, trained personnel 
shall be appointed and 
supported to effectively 
exercise human oversight 
responsibilities. 

GDPR adds an obligation to 
provide 'meaningful human 
information' about 
automated decisions and 
restricts the use of fully 
automated decisionmaking. 

Risk assessments rules are 
set out in different 
instruments of the EU 
digital acquis.  

Continuous monitoring of 
AM systems meeting 
specific thresholds is 
envisaged by the GDPR 

The PWD provides specific 
human oversight obligations 
for fully and partially 
automated decisions in the 
context of platform work (e.g. 
training ability to override, 
designated human contact, 
etc.), prohibits fully 
automated adverse decisions 
and establishes a right to 
human review and the right to 
a detailed explanation. 

The PWD is tailored to 
workers, specifically aiming to 
safeguard working 
conditions.  

While information and human 
oversight obligations are set 
out in the EU digital acquis, 
these are more limited and 
less tailored to the protection 
of workers' rights than those 
set out in the PWD. The AI 
Act permits fully automated 
or AI-assisted adverse 
decisions, but ensures that 
such persons have the right 
to human oversight and the 
right to explanation. 
Additional, more specific, 
obligations and rights 
foreseen by the PWD are 
limited to persons performing 
platform work.  

The PWD lays down rules for 
regular impact assessments, 
including of specific OSH and 
potential discriminatory 
impacts stemming from the 
deployment of automated 
decisionmaking and 
monitoring systems.  

Except for OSH risk 
assessments and DPIA, which 
would most probably apply in 
an AM setting, the broader 
impacts of AM tools on 
fundamental rights will be 
assessed in a more limited 

75 

Ex-ante assessment and 
continuous monitoring 

Although employers must carry out 
general OSH risk assessments 
before introducing new technology, 
these are not tailored to potential 
risks associated with AM. 

EPRS | European Parliamentary Research Service 

PROTECTION 
MECHANISMS 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

The PWD includes oversight 
obligations for national 
authorities and labour 
inspectorates, as well as 
obligations for platforms to 
keep records and 
documentation on AM 
systems available.  

(DPIA). The AI Act 
envisages a fundamental 
rights assessment (FRIA) 
before AI systems 
deployment, but only for 
public services.  

Ex-post monitoring is also 
subject to discrepancies. 
The AI Act is the only 
regulation requiring a 
constant iterative risk 
assessments of AI systems 
impacts on users.  

way in terms of both 
frequency and scope.  

Under the PWD, these 
impacts will only be assessed 
within the limited scope of 
platform work, albeit on a 
regular basis (both ex-ante 
and ex-post).  

By contrast, under the 
broader scope of the AI Act, 
impacts will only be assessed 
if they are deployed by public 
employers, and only prior to 
deployment. Private-sector 
employers outside the 
platform economy are not 
required to conduct AI-
specific impact assessments 
or continuously monitor 
effects. 

Organisation of working 
time 

The EU acquis and CJEU case law 
ensure that maximum working hours, 
rest periods and time recording are 
safeguarded, regardless of the 
technology used. 

Although there is no 
provision directly 
protecting workers in that 
regard, AM systems used to 
allocate tasks based on 
personal aspects or make 
decisions affecting terms of 
work-related relationships, 
such as working time are 

The specific protection 
mechanisms set up by the 
PWD extent to automated 
monitoring and 
decisionmaking systems that 
affect working time, access to 
assignments, etc.  

The current rules do not 
require employers to 
demonstrate that AI-
generated schedules adhere 
to working time limits or to 
consult with workers or social 
partners in that regard.  

76 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of work 

PROTECTION 
MECHANISMS 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

Data protection and privacy 
in the workplace 

Specific provisions regarding data 
protection are integrated in some 
labour law instruments (e.g. 
obligations relating to working time 
records under Working Time 
Directive). No specific obligations 
relating to AM systems in labour 
acquis, with exception of PWD.  

expressly qualified as high-
risk AI systems subject to 
more stringent rules.  

GDPR provides a strong 
legal framework for the 
protection of personal data, 
used in relation to the use 
of AM systems in national 
case-law.  

However, it does not 
contain specific protective 
rules for workers' nor 
considers the specificities 
of the worker-employer 
relationship. . 

The AI Act primarily focuses 
on data governance rules to 
cover both personal and 
non-personal data for AI 
systems placement on the 
market.  

Article 8 of the PWD treats all 
automated monitoring and 
decisionmaking of persons 
performing platform work as 
'high-risk' processing, 
triggering a mandatory DPIA 
and stricter safeguards. 

For instance, the PWD 
contains a prohibition on the 
processing of specific 
personal data, such as data 
on workers' private 
conversations, off-duty time, 
etc.  

The EU digital acquis provides 
rules and guarantees for data 
governance and the 
protection of personal data. 

Specific protective 
mechanisms and risks 
identified in employment 
contexts are in place for 
platform work (PWD) or have 
been set out in national 
legislation. However, such 
protective mechanisms do 
not extend to other work 
environments, where specific 
issues may arise (e.g. issue of 
free consent).  

. 

Bias and discrimination  The Employment Equality Directive 

contains protective rules against 
workers' discrimination. However, 
those rules do not address the 
specific risks stemming from the use 
of AM tools such as the need for 
nuanced assessment to distinguish 

The digital acquis 
acknowledges bias and 
discrimination risks 
stemming from AI systems. 
Moreover, Article of the 9 
GDPR establishes a 
principle prohibition 

The PWD contains specific 
provisions to prevent bias, 
including a strict prohibition 
on processing sensitive data, 
and risk assessment duties 
requiring employers to 
modify or switch off any 

The PWD is the only piece of 
legislation that contains ex-
ante and continuous bias 
mitigation duties and even 
the obligation to modify the 
automated decisionmaking 
system or the discontinuation 

77 

 
EPRS | European Parliamentary Research Service 

PROTECTION 
MECHANISMS 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

systems that pose a high risk 
of discrimination.  

of its use, in order to avoid 
such decisions in the future.  

direct and indirect discrimination; 
the lack of transparency coming 
from 'black box' effects, the risks 
resulting from software limitations or 
the limited number of protected 
grounds. 

(subject to exhaustively 
listed exceptions) for the 
processing of personal data 
revealing racial or ethnic 
origin, political opinions, 
religious or philosophical 
beliefs, or trade union 
membership, and the 
processing of genetic data, 
biometric data for the 
purpose of uniquely 
identifying a natural person, 
data concerning health or 
data concerning a natural 
person's sex life or sexual 
orientation.  

Health and safety  The OSH acquis encompasses a 

comprehensive set of general rules 
commanding employers to ensure 
workers' safety and health 'in every 
aspect related to the work'. These 
requirements often indirectly cover 
the deployment of AM systems at 
work as the instruments have been 
developed to evolve to consider 
emerging risks (e.g. risk assessment 
obligation under the OSH 
Framework Directive). However, the 

Health and safety are 
protected interests under 
the European digital acquis. 
The AI Act includes OSH 
risks in the risk 
management system that is 
part of the development of 
the AI lifecycle, as well as in 
the built-in safeguards that 
are part of the product 
development. The AI Act 
follows a product-based 

The PWD explicitly regulates 
AM with a strong emphasis on 
OSH. For instance, it 
introduces a general principle 
that AM systems used in 
platform work shall not 
adversely affect workers' 
health and safety. It also sets 
out specific requirements for 
OSH, such as the requirement 
to regulatory evaluate the 
impact of AM systems on 

78 

Outside of the PWD, there is 
a lack of horizontal 
requirements for actively 
switching off biased AM 
systems, carrying out 
continuous bias audits, and 
ensuring transparency 
regarding training data in 
employment. 

Moreover, specific risks 
associated to AM, such as 
bias linked to employment 
type or personality traits, is 
not covered by the exiting 
legal framework.  

Although identified as an area 
of vulnerability for AI 
systems, the AI Act does not 
set out any rules to address 
OSH risks specifically.  

The EU OSH acquis does, 
however, set out general rules 
that protect different aspects 
of workers' health and safety 
which could be impacted by 
the use of AM systems in the 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of work 

PROTECTION 
MECHANISMS 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

legal framework does not contain 
AM-specific duties.  

approach for regulating 
OSH risks.  

health and safety, to put in 
place preventive and 
protective measures (e.g. for 
psychosocial stress, fatigue), 
a right to human contact in 
the company, etc.  

Social dialogue and 
collective bargaining 

The Information and Consultation 
Directive and the European Works 
Council Directive create consultation 
rights, but only in certain 
undertakings or establishment (e.g. 
with more than 50 or 20 employees) 
and without AM-specific triggers. 

The EU digital acquis does 
not contain key obligations 
regarding social dialogue 
and workers' participation. 
The AI Act and the GDPR do 
not specify requirements 
for the involvement of the 
social partners (with 
exception of Article 88 
GDPR where it allows for 
the adoption of more 
stringent national 
protection measures in 
consultation with the social 
partners). 

The PWD contains specific 
information and consultation 
obligations towards workers' 
representatives on the 
introduction or substantial 
changes to AM systems in a 
specific workplace. Further 
information obligations apply 
regarding data collected and 
impacts on working 
conditions. Detailed 
requirements apply, for 
example, regarding timeliness 
of information provision and 
meaningful engagement in 
social dialogue on AM 
systems used.  

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

workplace. However, these 
mechanisms do not contain 
AM-specific obligations for 
employers and rights for 
workers, tailored to the 
specific OSH risk related to 
the deployment of AM 
systems.  

The PWD has established 
more specific protection 
measures, but is limited in its 
personal scope.  

There is no EU requirement 
securing consultation 
(beyond information) on the 
rollout of AM systems in SMEs 
or outside of platform work. 
Collective bargaining on AI 
depends on voluntary 
initiatives, mostly at Member 
State level. The EU digital 
acquis allows for more 
favourable measures to be 
determined at the national 
level, including through 
collective bargaining.  

Specific information, 
consultation and cooperation 
obligations related to the 

79 

EPRS | European Parliamentary Research Service 

PROTECTION 
MECHANISMS 

LABOUR AND OSH ACQUIS 

DIGITAL ACQUIS  
(GDPR, AI ACT) 

PWD 

Redress and enforcement  Workers may take legal action under 
general EU and national rules. Some 
sectoral directives grant trade 
unions standing, but there is no EU-
wide collective redress mechanism. 
The EU Representative Actions 
Directive, (2020/1828) is limited to 
consumer protection matters and 
does not cover labour rights or 
working conditions. 

Finally, the PWD creates a 
shared monitoring and 
cooperation obligation 
between social partners and 
national authorities for 
effective enforcement of the 
Directive.  

Article 18 of the PWD grants 
the right to timely and 
impartial redress and 
empowers the courts to order 
the disclosure of evidence. 
Workers are protected 
against retaliation, and both 
worker representatives and 
NGOs also have legal 
standing. 

Authorities are required to 
cooperate with trade unions 
for the effective enforcement 
of the Directive.  

The EU digital acquis 
focuses on market 
surveillance and 
administrative oversight, as 
well as safeguarding 
individual rights. Besides 
the possibility given to 
Member States to establish 
more favourable rules for 
workers in this context, 
there are no particular 
provisions supporting the 
right to seek collective 
redress. 

KEY GAPS / NEED FOR 
FURTHER EU ACTION 

introduction and use of AM 
systems in specific 
workplaces are currently 
limited to platform work.  

Remedies for workers 
affected by AM systems are 
fragmented across different 
legal frameworks, with 
different legal bases/aims. 
Legal redress is organised 
around individual cases, while 
collective redress are 
currently unavailable for 
potential damages resulting 
from AM systems. Finally, 
there is no coherent 
enforcement architecture 
that combines labour, OSH 
and digital regulators as the 
mandates of the different 
competent authorities are not 
aligned.  

Source: Authors' elaboration.  

80 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

4. Task 4 – Policy options and impact assessment 

4.1. Policy options 

The  increasing  use  of  algorithmic  management  (AM)  tools  across  sectors  raises  regulatory 
challenges that are only partially addressed under current EU legislation. Table 7 in the legal analysis 
in Section 3 identifies significant gaps in the protection of workers' rights, including transparency, 
human  oversight,  occupational  health  and  safety,  and  effective  (collective)  redress.  Addressing 
these gaps requires targeted policy responses. This section outlines three distinct policy options to 
regulate the use of algorithmic management that vary in legal ambition and binding effect. Together, 
they represent a spectrum from soft-law instruments to new legislative action. 

Option  1:  Recommendation.  A  non-binding  Council  of  the  European  Union  (Council) 
Recommendation addressing the risks and governance needs related to algorithmic management 
by providing guidance to Members States. 

Option  2:  Amending  existing  legislation  to  address  –  partially  or  wholly  –  risks  and  gaps  in 
legislation related to algorithmic management. 

Option 3: New directive. Introduction of a horizontal directive specifically targeting the governance 
of algorithmic management across sectors. 

Below is a more detailed description of each option. 

4.1.1. Option 1 – Recommendation and guidance to Member States 

A Recommendation accompanied by guidance to Member States could be issued by the Council, 
providing non-binding guidance to Member States and social partners on managing the risks and 
impacts of algorithmic management. A Joint Recommendation by the Council and the Commission, 
which  is  not  specifically  provided  for  in  the  TEU,  would  also  demonstrate  inter-institutional 
alignment.  However,  adopting  it  would  be  more  complex,  which  is  probably  why  this  type  of 
document is mostly used for external relations rather than matters of the internal market and social 
policy.  The  Recommendation  would  promote  a  common  EU  understanding  of  good  practice, 
encourage  early  policy  coordination,  and  set  expectations  for  future  legislative  or  institutional 
reforms. 

The content of the Council Recommendation could include sound principles for addressing issues 
related  to  transparency,  human  oversight  and  the  right  to  explanation,  monitoring  of  AM,  data 
protection, bias and discrimination, health and safety, social dialogue and collective redress related 
to AM. It may also encourage Member States to update their national occupational health and safety 
frameworks and integrate AM risks into labour inspectorate guidance, as well as provide relevant 
training.  

The  legal  basis  for  such  a  Recommendation  would  be  Article  292  TFEU,  which  allows  the  EU 
institutions, in particular the Council, to adopt Recommendations without binding force, combined 
with Article 153(1) TFEU on the competence of the EU to support and complement Member State 
activities in relation to the improvement of the working environment to protect workers' health and 
safety and working conditions, among others. 

81 

EPRS | European Parliamentary Research Service 

4.1.2. Option 2 – Amending existing directives to address legislative gaps 

Section  3  identified  the  existing  legislation  directly  or  indirectly  related  to  the  use  of  AM  and 
highlighted the gaps in the protective mechanisms for workers in relation to AM usage. This option 
looks at the possibility of closing legal gaps by amending existing legislation. It entails a partial or 
full amendment of existing legislation to ensure that the risks arising from AM usage are explicitly 
regulated. It is a package of targeted amendments to existing EU labour, OSH and digital-rights 
instruments to close the specific gaps identified in Table 7. It is described as a package in the sense 
that each piece of legislation can be amended without amending other legal instruments. Some of 
these  instruments  are  older  parts  of  the  acquis  and  require  targeted  modernisation.  These  are 
described first. Given its recent adoption and explicit reference to AM, the Platform Work Directive 
(PWD) is treated separately in the subsequent sub-section. Adapting older legislation would entail 
amendments to a significant number of directives, which are listed below in clusters: 

•  Update of the labour/OSH acquis to reflect safeguards for workers in relation to AM risks: 
o  The  OSH  Framework  Directive  89/391/EEC  (and  relevant  daughter 
directives):  The  updated  directives  would  specify  or  make  clear  that  risk 
assessments  must  cover  AM-related  psychosocial  hazards  (e.g.  work  intensity 
and  surveillance-induced  stress)  and  require  preventive  measures  when  AM  is 
introduced or changed, and worker participation in design and monitoring of AM 
systems (see also below). 

o  The Information and Consultation Directive 2002/14/EC would clarify that the 
deployment or substantial change of AM systems is a trigger for information and 
consultation duties of the employers. To ensure that all workplaces are covered, 
an  AM-specific  threshold  would  have  to  be  introduced  and  deviated  from  the 
current 'above 20 or 50 employees' threshold for AM-specific changes. 

o  The  Working  Time  Directive  2003/88/EC  would  be  updated  to  add  explicit 
safeguards  for  algorithmic  hour  rostering  (e.g.  reasonable  advance  notice, 
compatibility with daily/weekly rest), linking digital scheduling to existing time-
recording duties. 

o  The Directive on Transparent and Predictable Working Conditions 2019/1152 
would require, in its Article 4, layman-language written notices to be provided to 
the workers where automated systems are used. 

•  Clarify the connections and ensure cross-referencing with the digital acquis (GDPR 
and  AI  Act):  To  refer  to  the  definitions  of  AI/AM  tools;  to  specify  which  AM  systems 
should  be  deemed high-risk  AI  under  Annex  III  of  the  AI Act  and  to  cross-refer  to  the 
obligations of the employer in such cases; and to clarify that processing of personal data 
by means of AM systems requires a data protection impact assessment under the GDPR.  

The PWD is the only piece of EU legislation explicitly addressing AM. The PWD includes articles 
(Chapter III, Articles 7-15) that, among others, cover provisions for transparency, human monitoring, 
human review, and information and consultation for persons doing platform work in connection with 
work under algorithmic management. This directive would need to be amended/recast to expand 
the PWD's advanced safeguards, currently limited to workers performing platform work, into a set 
of  general  rights  for  all  employees  and  possibly  also  solo  self-employed  individuals  under  AM. 
However,  such  an  amendment/recast  would  be  complicated  by  the  fact  that  the  PWD  is  not 

82 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

intended as a horizontal directive. For example, Chapters I and II are focused on a particular business 
model, platform work, that the directive is designed to regulate and do not readily lend themselves 
to a horizontal approach to regulating AM. Hence, while some articles could be rephrased to include 
all workers and self-employed individuals271, others, such as the entirety of Chapter II would apply 
specifically to platform work.  

Adopting an amendment/recast of the PWD, as well as amending the older labour acquis, would 
mean that Member States would need to adopt a wide range of implementing measures.  

The legal basis for the amendments suggested under this option would follow the legal basis of the 
individual directives. 

4.1.3. Option 3 – New legislative directive 

This option creates a horizontal 'Algorithmic Management Directive' (AMD) covering all workplaces. 
It differs from option 2 by leaving the 'older' labour/OSH acquis unchanged, and in structure, it is 
not  constrained  by  the  approach  of  the  business  model-specific  PWD.  This  implies  that  scope, 
obligations, and governance choices can be derived from first principles. The directive would codify 
a  comprehensive  approach  to  AM  systems,  fully  integrating  labour,  OSH,  equality,  and  data 
governance dimensions, taking into account existing legislation in the labour/OSH acquis and the 
PWD. 

The AMD could contain all or a subset of the following elements: 

1 

2 

3 

4 

5 

Scope and definitions: Applies to all employers, as well as workers and solo self-
employed. 'Algorithmic management' is defined to include any automated system 
that  allocates  tasks,  monitors,  evaluates  or  makes  decisions  affecting  working 
conditions, referencing AI Act definitions for 'AI system'. 
Information, consultation and transparency register: The right to be informed and 
consulted extended to all workers when it relates to AM (including companies with 
less  than  50  employees  or  establishment  with  less  than  20  employees,  not  yet 
covered outside the PWD, cf. Table 7). Employers maintain a local register of AM 
systems  with  purpose,  (learning)  datasets,  metrics,  accuracy  and  limitations. 
Workers and representatives can access the register. 
Human  oversight  and  right  to  explanation:  Mandatory  human  review  before 
'significant' decision. Rights equal to those established in the PWD article 11. 
Algorithmic  impact  assessment  (AIA):  Prior  to  deployment  and  at  defined 
intervals, employers must perform an integrated assessment (fundamental rights, 
OSH—including psychosocial factors—working time, discrimination). Rights equal to 
those established under PWD Article 8 for all individuals within the scope. 
Data  protection  and  privacy:  Similar  to  the  PWD  Articles  7  and  8,  AM  can  be 
designated high-risk, implying mandatory DPIAs. Assessments are made available 
to workers' or solo self-employed representatives.  

271   E.g. Article 1 could be amended to cover 'improving working conditions in platform work and regulating algorithmic 
management in general'. Core definitions (Article 2) could be amended such that 'digital labour platform' would 
become 'algorithmic management deployer' or another neutral term (while necessarily retaining a sub-definition for 
platforms to which other chapters of the directive refer).  

83 

 
EPRS | European Parliamentary Research Service 

6 

7 

8 

9 

Bias and discrimination mitigation: Provisions similar to PWD Article 10, including 
periodic evaluation of AM tools; obligation to retrain, calibrate or suspend systems 
exhibiting disparate impact. 
Health  and  safety:  Without  prejudice  to  the  obligations  in  the  OSH  Framework 
Directive 89/391/EEC (and relevant daughter directives), explicit integration of AM 
risks into OSH management systems by a requirement to include psychosocial risk 
indicators  (stress,  work  intensity)  particularly  related  to  AM  and  to  co-design 
preventive measures with worker representatives. 
Social dialogue and participation: Right to be informed and consulted for workers 
and their representatives (see point 2). Promotion of collective bargaining clauses 
on algorithmic use (similar to PWD, Article 25). 
Redress and collective enforcement: Expansion of the rights established in Articles 
18-23 of the PWD within the existing national enforcement structures.  

A new specific Directive  would find its legal basis in Article 153(1)(a) TFEU with respect to OSH 
topics  and  in  Article  153(1)(b)  TFEU  with  respect  to  labour  topics  in  conjunction  with  Article 
153(2)(b)  TFEU,  which  allows  for  the  adoption  of  Directives  for  the  minimum  harmonisation  of 
working conditions and measures for the protection of workers' health and safety, and Article 16(2) 
TFEU on the right to privacy, similar to the legal basis used for the PWD.  

4.1.4. Pros and cons of the different policy options 

The three options present different levels of key characteristics related to the coverage of identified 
legal  gaps,  speed  of  implementation  and  implementation  costs  (e.g.,  in  the  European  legislative 
process  and  implementation  in  Member  States),  the  creation  of  a  level  playing  field,  third-party 
uncertainty  from  new  legislation  (e.g.  businesses  not  directly  meant  to  be  affected  by  the 
regulation)272,  the  risk  of  overlap  with  existing  labour  acquis  and  legal  clarity.  Table 8  provides a 
comparison  of  these  characteristics.  Option  1  relies  on  soft  coordination,  option  2  relies  on  an 
amendment  of  the  existing  legal  framework,  while  option  3  creates  a  new  binding  horizontal 
framework. Table 8 below summarises the main ex ante advantages and drawbacks. 

Regarding the coverage of identified legal gaps, a Recommendation (option 1) can signal good 
practice across all domains in Table 7, but it is not legally binding for Member States. The impact, 
therefore, depends on voluntary uptake and will likely be uneven. Amending the existing legislation 
(option 2), either by amending the labour acquis and/or by an amendment of the PWD can address 
all legal gaps identified in Table 7. A new horizontal directive (option 3) offers the most coherent 
way to cover legal gaps because it can be designed from first principles to address every gap, taking 
account of the existing labour acquis. 

Speed  of  implementation  and  implementation  costs  also  diverge.  Option  1  can  be  agreed  and 
disseminated  relatively  fast,  with  minimal  administrative  effort.  Option  2  requires  legislative 
processes/es with several actors involved, and implementation of such changes in Member States. 
The legal drafting work (in the case of amending several directives) is significant, with potentially 

272   Third-party uncertainty in this context refers to a situation where legislation (new or amended) drawn up to address 
a particular issue (in this case, AM risks) potentially affects the legal environment for businesses, not concerned with 
the issue.  

84 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

several open files to coordinate. As such, it could be that at the national level, transposition would 
take place through several legal acts. 

For option 3, a legislative process and implementation would be required, as described for option 2, 
but as a single proposal, it would be more clearly framed and better targeted to bridge gaps and 
avoid overlaps with existing legislation. 

On the level playing field and third-party uncertainty, option 1 leaves greater scope for national 
divergence and therefore weaker protection of the level playing field and increases uncertainty for 
companies with cross-border operations. At the same time, it poses no uncertainty for parties not 
directly  involved  in  AM.  Option  2  would  ensure  a  level  playing  field  by  basing  AM  regulation  in 
existing EU-wide legal instruments. It would reduce fragmentation but increase uncertainty for third 
parties if existing labour legislation is amended to account for AM. In the case where the PWD is 
amended/recast, further legislative uncertainty is generated for platform companies because of the 
risks of PWD amendments affecting their business model. Option 3 provides the strongest option 
in  terms  of  a  level  playing  field  and  third-party  certainty,  because  a  single  framework  for  AM 
regulation  applies  across  sectors.  A  new  directive  focused  on  AM  usage  should  bring  little 
uncertainty to businesses not involved in AM practices. 

There is a distinct risk of overlap with the existing labour acquis and a question of legal clarity. 
Option 1 avoids overlap by design but offers limited certainty. Option 2 must carefully handle several 
older directives that were not initially designed to address AM risks. In the case of amending the 
PWD,  the  complexity  of  transforming  a  directive  drawn  up  to  regulate  a  business  model  into  a 
horizontal directive is large, with some articles only applying to the business model (platform work) 
and  others  applying  horizontally  to  all  workers.  Option  3  maximises  legal  clarity  but  will  require 
careful drafting to avoid overlap with existing labour legislation, in particular PWD, and to align it 
with the GDPR and the AI Act. 

The three options also differ in the ability to achieve proportionality. Option 1 is proportional to its 
impact, whereas options 2 and 3 differ in the sense that proportional legislation is more complicated 
to accomplish in option 2 compared to option 3, due to option 2's reliance on existing legislation, 
which may not be amendable to ensure optimal proportionality.  

85 

 
 
EPRS | European Parliamentary Research Service 

Table 8 – Pros and cons of different policy options (summary table) 

PROS 

CONS 

Simple and quickest adoption procedure  

Non-binding 

Low implementation costs 273  

Risk of uneven uptake  

Option 1 
Recommendation 

Flexibility in national implementation  

Can encourage voluntary action across 
identified gaps. 

No direct enforcement, hence, may 
not change practices in high-
impact sectors. 

No (less) uncertainty for third parties (not 
involved in AM). 

Risk of legal fragmentation across 
countries. 

Option 2 

Amend existing 
legislation 

Builds on existing definitions (in the case of 
the PWD). 

Can cover all legal gaps identified in Table 7 
(transparency, over-sight, OSH, bias, 
monitoring, privacy). 

Level playing field (no legal fragmentation 
across EU countries). 

Legal clarity - single horizontal framework 
(can use relevant definitions from PWD) 
(though see cons below). 

Option 3 

Lower implementation costs than option 2 
(but higher than option 1).  

New algorithmic 
management 
Directive 

Can cover all gaps identified in Table 7 
(transparency, oversight, OSH, bias, 
monitoring, privacy). 

Level playing field (no legal fragmentation) 

No (less) uncertainty for third parties (not 
involved in AM). 

Source: Authors' elaboration.  

Complex legal procedure and 
implementation (several directives 
would need to be amended). 

High implementation costs 
(Member States may have to re-
transpose several directives) 

Longer adoption timeline than 
option 1. 

Risk of overlap with existing 
employment acquis (in case of 
relying on PWD). 

Increasing uncertainty for third 
parties. 

Requires additional enforcement 
capacity (relative to option 1). 

More demanding legislative 
procedure than option 1, but less 
than option 2. 

Higher implementation costs than 
option 1 (but lower than option 2). 

Risk of overlap with the PWD  

Requires additional enforcement 
capacity (relative to option 1). 

Implementation costs under option 1 depend on the number of countries that take legislative action and would not 
have done so under the baseline assumption of no EU policy intervention. This number is uncertain but likely to be 
low, given that AM regulation is discussed in several countries or is/will be regulated through collective bargaining. 
Another consideration is that option 1 will likely lower implementation costs for countries that would have legislated 
under the baseline but have not yet done so, since they can rely on guidance without including costly elements. An 
overall assessment is that implementation costs are low compared to Policy options 2 and 3.  

273  

86 

 
 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

4.2. Impact assessment 

4.2.1. Defining the baseline – no additional EU legislation  

Based on OECD-AI report and differences among EU countries (see Section 2), the use of AM is 
likely  to  increase  in  the  coming  years.  Hence,  under  the  baseline  scenario,  the  use  of  AM  in 
workplaces is expected to expand rapidly with no additional EU legislative action, while recognising 
that  some  Member  States  may  introduce  national  measures  that  slow  down  both  the  speed  of 
adoption and the intensity of use. Although some firms hesitate to adopt AM due to concerns about 
costs or workforce resistance, for instance, 79 % of non-adopting firms (in DE, FR, IT, and ES) cite 
high costs, and 56–67 % cite staff resistance as barriers (OECD-AI study). Nonetheless, as AI tools 
become cheaper and more normalised, even these barriers are likely to diminish, leading to pervasive 
AM across all sectors and firm sizes over time. Moreover, competitive forces, within countries and 
across  the  EU,  will  push  in  this  direction,  though  national  legislation  may  partly  offset  this 
development. For the same reasons, we expect that country-specific legal initiatives will emerge, 
limiting AM usage to some degree. 

Impacts on workers under the baseline are a concern. Current research and survey evidence link 
algorithmic management to a range of psychosocial and health risks (cf. section 2). Where AM tools 
are present, workers report increased stress levels and worse work-life balance. Other risks include 
inadequate breaks or rest, which can exacerbate fatigue and increase the risk of burnout. As a result, 
chronic  stress,  exhaustion,  and  mental  health  issues  such  as  burnout  are  rising  in  algorithmically 
managed workplaces. Finally, physical health and safety risks may also grow under unmanaged AM. 
In Member States that adopt national safeguards, adverse effects are expected to materialise more 
slowly, though the direction of impact remains the same. 

Under  the  baseline,  we  quantify  the  increase  in  worker  exposure  to  AM  over  the  short  term  by 
applying a simple within-industry catch-up rule to each country-by-industry cell in the EWCS. For 
every industry, we first compute a frontier benchmark as the unweighted mean of the top-three 
country  exposures.  Each  country-by-industry  pair  is  then  assumed  to  close  half  the  gap  to  the 
frontier country-by-industry benchmark. This is a normative convergence exercise to illustrate the 
potential spread of AM across the economy. The approach accounts for current country-by-industry 
differences  in  initial  uptake.  National  initiatives  can  slow  convergence  relative  to  the  absence  of 
national initiatives. 

Under the baseline, the EU-wide prevalence of AM exposure would increase from 42.3 % to 55.5 %. 
The timeframe for achieving this catch-up is highly uncertain in the absence of substantial national 
restrictions. Again, with more Member-State legislation, the realised increase would be smaller and 
more gradual. Given the growth in AM uptake and the fact that we rely on the baseline on only half 
the distance to the frontier being closed, it is expected that AM exposure would increase within a 5-
year window. Given that no account is taken for overall catch-up with the US, this estimate is judged 
to  be  conservative  and  a  lower  bound  on  the  potential  increase  in  AM  usage,  though  subject  to 
substantial uncertainty with respect to Member States' legal action. Based on the results in Section 
2, the impact on workers implies an increase in stress prevalence of 0.8 pct. point (Table 9). The 
prevalence of autonomy at work, good work-life balance, and sufficient rest between work shifts all 
decrease by 0.3 per cent. point. These effects roughly double if convergence between country-by-

87 

Autonomy 
at work 

Good work-
life balance 

Able to get 
hour off 

Sufficient 
rest 
between 
workdays 

Work bad 
for health 

EPRS | European Parliamentary Research Service 

industry pairs is assumed to be full rather than half. Member States' legislation may also affect the 
estimated impact on workers under the baseline. 

Table 1 – Baseline development in impact on workers 

BASELINE,  CLOSING  HALF 
THE GAP 

BASELINE, CLOSING THE FULL 
THE GAP 

MEASURE 

INDICATION 

PREVALENCE 
(%) 

PREVALENCE 
(%) 

DIFFERENCE 
(%-POINTS) 

PREVALENCE 
(%) 

DIFFERENCE 
(%-POINTS) 

Stress 

Negative 

Positive 

23.9 

49.6 

24.7 

49.3 

Positive 

84.1 

83.8 

Positive 

67.7 

66.9 

0.8 

-0.3 

-0.3 

-0.8 

25.5 

49.1 

83.5 

66.0 

1.6 

-0.5 

-0.6 

-1.7 

Positive 

82.6 

82.3 

-0.3 

82.0 

-0.6 

Negative 

26.9 

27.5 

0.6 

28.2 

1.3 

Source: Authors' calculation based on EWCS. 

In summary, the baseline scenario anticipates an increase in the number of workers subjected to 
algorithmic oversight and decisionmaking, accompanied by heightened risks to their stress levels, 
and work-life balance. Existing EU laws only partially mitigate these issues. Traditional labour and 
occupational safety directives do apply (e.g., general duties to protect worker health and limits on 
working hours), but they were not designed with digital management in mind and leave important 
gaps. The only dedicated instrument so far is the Platform Work Directive (PWD), which introduces 
advanced AM safeguards (transparency, human review, bias prevention, etc.) but applies only to 
workers performing platform work and only to the platform work business model. Some Member 
States will likely (partly) address these issues in national legislation. The extent to which this will 
occur is uncertain, but it is judged unlikely that all Member States will have legislation in place in the 
medium term. Thus, under the baseline, the legal gaps identified in the analysis (Table 7), such as 
the lack of transparency rights for non-platform workers, the absence of an explicit 'human-in-the-
loop' requirement, the lack of systematic algorithmic risk assessments in regular firms, and weak 
avenues for redress, will for many workers, in whole or part, remain largely unaddressed. 

Because  some  Member  States  are  likely  to  adopt  legislation  related  to  AM,  and  Member  States' 
legislation  will  not  be  subject  to  common  principles  and  guidelines,  under  the  baseline,  legal 
fragmentation  will  increase.  This  will  weaken  the  single  market  by  increasing  the  costs  of  doing 
business across multiple member states due to varying regulations on the use of AM. Since 42.3°% 

88 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

of workers (as measured by the EWCS) are subject to some form of AM, increased fragmentation is 
likely to affect a large share of companies with operations in several Member States. This burden will 
fall harder on SMEs doing business across borders due to their smaller capacity to adopt to different 
regulatory regimes. 

The impact of the policy options will be assessed, relative to the baseline, along five dimensions: 
impact on workers' AM exposure, effects of AM usage on outcome measures (e.g. stress), cost for 
companies in general, costs for SMEs, and impact on administrative costs (member states and EU 
level combined).  

There is evidence that managerial practices are important for firm-level productivity.274 The effect 
on  productivity  is  not  directly  assessed  but  is  assumed  to  be  proportional  to  the  change  in  the 
company's AM use (worker exposure to AM). While a good measure of the productivity effects of 
introducing AM does not exist and is likely to differ across sectors and contexts, productivity effects 
are a key motivation for adopting AM systems, and the promise of productivity increases is stressed 
across the literature.275 It is not expected that there would be significant productivity differences 
across  the  policy  options,  but  there  is  considerable  uncertainty  in  this  domain.  One  plausible 
downside mechanism is if regulatory design unintentionally discourages experimentation and slows 
organisational learning about how to calibrate AM and build the necessary complements in workflow, 
data quality and skills to reap the full productivity benefits. 

It is not feasible to assess these effects quantitatively; instead, we apply a semi-quantitative scale 
per dimension: None, Small / Medium / Large. A '+' and '-' sign are used to indicate the sign of the 
expected effect. E.g. 'Small-' captures minor deterioration; 'Medium' denotes a material effect, and 
'Large' indicates a substantial impact. Signs '++' / '-' are used to indicate between-categories effects 
('Medium++' denotes a larger impact than 'Medium+', but a smaller impact than 'Large+').  

4.2.2. Impact of policy option 1 

Option 1 involves a non-binding Council Recommendation that would provide guidance to Member 
States  and  employers  on  managing  the  risks  associated  with  algorithmic  management.  The 
instrument  would  promote  a  common  European  approach  and  encourage  proactive  measures 
without  creating  new  legal  obligations.  It  could  affect  legislation  by  countries  that  would  have 
adopted legislation under the baseline and may also spur some countries to pursue legislative action. 
The content of the Recommendation would likely cover key principles and best practices identified 
in the legal analysis, such as transparency of AI systems, human oversight of automated decisions, 
assessment of psychosocial risks, and inclusion of social partners in dialogue on AM (cf. Section 3). 
For example, it might urge Member States to update their national occupational safety and health 
(OSH)  regulations  to  explicitly  address  algorithmic  risks,  or  recommend  that  employers  conduct 
impact  assessments  of  their  AM  tools  and  consult with  workers  when  introducing AM  tools.  The 

274   Bloom, N., & Van Reenen, J. (2007). Measuring and explaining management practices across firms and countries. 

The quarterly journal of Economics, 122(4), 1351-1408. 

275   Milanez et al., 2025. 

89 

 
EPRS | European Parliamentary Research Service 

Recommendation could also endorse initiatives such as integrating 'right to disconnect' policies or 
AM-specific guidelines into labour inspectorates' practices to address work-life balance issues.  

Addressing legal gaps 

Due to the soft-law nature of option 1, it can only partially address the legal gaps identified in Section 
3, Table 7. In effect, it encourages Member States and companies to address the identified gaps 
through their own laws or practices, rather than mandating direct action. For instance, it could call 
for extending information and consultation rights about AM systems to all workplaces (addressing 
the current absence of AM-specific transparency rights for non-platform workers), but it would be 
up to each Member State to implement such advice. Similarly, the Recommendation may promote 
human-in-the-loop  oversight  (echoing  PWD  provisions  that  ban  fully  automated  decisions)  and 
emphasise the importance of human review before major decisions, such as dismissals. 

Overall,  option 1  would  highlight  all  the  key  issues:  urging  employers  to  conduct  algorithmic  risk 
assessments  (to  anticipate  and  monitor  risks  that  current  OSH  rules  only  generically  cover),  to 
ensure AM tools do not violate working time norms (for example, by recommending advance notice 
of  algorithmic  schedule  changes  or  adherence  to  rest  requirements),  to  prevent  invasive  data 
practices (aligning with GDPR principles), and to establish consultation mechanisms for algorithmic 
decisions. By mirroring the known gaps, the Recommendation serves as an EU-level push tool. It can 
also spur additional social partner interaction related to the use of AM at the EU level. 

The extent to which legal gaps will be closed relies on the extent to which option 1 will influence 
emerging legislation under the baseline, and the amount of additional legislation and guidance it will 
spur among Member States.276 On both points, the assessment is that the impact will be relatively 
small.277 However, there is considerable uncertainty in assessing this point.  

This means that critical gaps could remain under a purely voluntary regime. Small and medium-sized 
enterprises (SMEs), for example, may be less likely to implement non-binding recommendations due 
to their limited resources and lower awareness. Yet, these smaller workplaces currently fall outside 
requirements such as the Information and Consultation Directive (less than 20 or 50 employees), a 
gap that the Recommendation alone cannot effectively address across the EU. Thus, while option 1 
can  raise  awareness  about  extending  algorithmic  transparency  and  participation  rights  to  all 
workers,  it  cannot  guarantee  coverage.  Similarly  related  to  issues  of  bias  or  redress,  the 
Recommendation  can  encourage  measures  (e.g.,  suggesting  employers  voluntarily  audit  their 
algorithms for bias or establish internal appeal processes for AI-driven decisions), but it offers no 
new enforceable right for workers to demand these measures. 

276  

'Additional legislation' refers to legislation that would emerge under the baseline in the absence of EU policy action. 

277   This is in line with the assessment of guidance on AM in the impact assessment of the PWD. PPMI (2021). Study to 

support the impact assessment of an EU initiative to improve the working conditions in platform work. 

90 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Expected effect on AM usage 

Because  this  option  is  non-binding,  it  is  not  expected  to  alter  the  overall  use  of  algorithmic 
management in the short term. However, a Recommendation could have a direct influence on the 
legislation  that  Member  States  will  enact  under  the  baseline,  and  some  indirect  influence  on 
practices across sectors. By setting out a clear EU-endorsed standard of 'good AM governance', it 
might  spur  proactive  changes,  especially  among  larger  companies  and  in  sectors  already  under 
public  scrutiny  (e.g.  in  platform  economy,  finance,  logistics).  For  example,  multinational  firms  or 
those with strong corporate social responsibility commitments might voluntarily follow the guidance 
to  publish  information  about  their  workplace  AI  systems  or  to  introduce  human  oversight 
committees, in order to demonstrate compliance with emerging EU norms. 

Across different sectors no subject to Member States' regulation, uptake may vary. Highly digitalised 
sectors  (IT,  finance)  or  those  already  familiar  with  EU  soft  law  might  be  early  adopters  of 
Recommendation  principles,  integrating  them  into  existing  risk  management.  Smaller  domestic 
firms  may  not  significantly  change  behaviour  unless  prompted  by  national  authorities.  The 
Recommendation could encourage Member States to issue sector-specific guidelines. In terms of 
firm size, option 1 is likely to lead to a divergence, as larger firms are more likely to be aware of and 
follow  an  EU  Recommendation.  They  also  have  the  capacity  to  implement  measures  such  as 
algorithm audits or worker consultations. SMEs, on the other hand, may have low awareness or feel 
less pressure to act. The use of AM in smaller companies could remain largely unchanged, potentially 
even increasing (as cheap algorithmic tools proliferate) without the compensating safeguards that 
larger  firms  voluntarily  adopt.  The  overall  trend  of  AM  growth  would  continue  under  this  option 
(labelling  the  impact  'Small-'),  albeit  with  some  voluntary  constraints  and  legislation  emerging  in 
certain leading companies or Member States. The impact will increase with the number of additional 
countries (relative to the baseline) taking legislative action following the Recommendation.  

Impacts on workers compared to baseline 

For workers, an EU Recommendation would offer some improvements over the baseline scenario, 
but likely only modestly and unevenly. In workplaces where the guidance is heeded, employees could 
experience better protection and transparency. For example, if an employer voluntarily implements 
the recommended principles, workers might be informed about algorithmic decisions affecting them 
(e.g., how a scheduling app decides shifts) that they were previously not informed about. This could 
slightly  reduce  stress,  improve  other  measures  considered  (Table 9),  and  improve  perceived 
fairness,  knowing  that  algorithms  are  not  working  unchecked.  Moreover,  if  the  Recommendation 
spurs the integration of psychosocial risk checks into OSH practices, workers in those firms might 
see steps taken to limit work intensification (for instance, voluntary caps on algorithmically driven 
performance targets or ensuring minimum rest breaks). That would help mitigate some of the stress 
and burnout issues noted under baseline. However, given its non-binding nature, option 1's overall 
impact on workers EU-wide would likely be low. The benefits would be very patchy. Some gains 
might occur via social dialogue, e.g. unions using the EU text to negotiate better terms, which could 
benefit workers in unionised settings, but those in non-union or precarious jobs might see no relief. 

As Table 8 highlights, effective redress and enforcement are major gaps. A Recommendation can 
encourage  Member States  to  strengthen  enforcement  (e.g.  labour  inspectorates  focusing  on AM 

91 

EPRS | European Parliamentary Research Service 

issues),  but  it  cannot  grant  workers  new  rights  to  challenge  decisions  made  by  algorithmic 
management systems or seek collective redress. Thus, compared to baseline, some workers might 
experience a slight improvement in their situation, but many would likely see no tangible difference. 
Overall, stress levels and mental health outcomes for workers might improve marginally (if a subset 
of employers reduces work pressure or surveillance intensity), but not uniformly. Option 1's impact 
on worker autonomy and rest will similarly be limited. 

Also here, the impact will depend on Member States' actions relative to the baseline. This includes 
how the Recommendation influences legislative approaches emerging under the baseline and how 
it affects countries where no legislative action would have emerged under the baseline. 

In quantitative terms, worker risk reduction under option 1 might be rated 'Small+' relative to the 
baseline. It is essentially a mild deviation from the baseline trend – helpful in specific contexts, but 
not enough to significantly alter aggregate outcomes, such as the overall incidence of algorithmic 
stress or work-life balance conflict across Europe.  

Implementation and compliance costs 

At  the  EU  level,  a  Recommendation  is  quick  and  inexpensive.  There  is  no  co-decision,  limited 
drafting and consultation costs, and light follow-up (awareness, guidance, occasional monitoring). 
There is no need for new regulators or inspection regimes, so the administrative burden for EU and 
national authorities remains minimal compared with a binding directive. Hence, administrative costs 
are labelled 'small--'. 

For firms, compliance is voluntary and therefore carries no mandatory cost if Member States do not 
enact  additional  legislation  following  the  recommendation.  Large  employers  may  choose  to 
implement elements (e.g. clearer worker information, basic human-oversight procedures, voluntary 
audits), while many SMEs will likely continue business-as-usual. Any indirect costs would stem from 
national follow-up where Member States convert guidance into rules (e.g. psychosocial risk checks), 
which  would  then  vary  by  country.  Costs  for  companies  are  assessed  at  'Small-'  and  for  SMEs, 
specifically  'Small-'.  Costs  are  to  a  small  extent  offset  by  improved  harmonisation,  though  this 
impact is limited. 

Overall, option 1 is the least demanding and scalable to firm capacity, although uneven uptake can 
create light fragmentation and leave proactive firms bearing small voluntary costs while laggards 
face none. 

4.2.3. Impact of policy option 2 

Option 2 proposes an amendment of existing legislation, either by amending several older directives 
in the labour acquis and/or by amending the PWD, such that the algorithmic management provisions 
apply  to  the  broader  workforce.  This  could  extend  the  advanced  protections  on  AM  currently 
reserved for workers performing platform work to all workers subject to AM, regardless of sector or 
employment  status  (including  solo  self-employed).  The  main  idea  is  that  any  employer  using 
algorithmic management systems for monitoring, supervision, or decisionmaking regarding workers 
would be bound by the requirements similar to those that platforms must follow. 

92 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Addressing legal gaps 

In  principle,  amending  existing  legislation  could  close  the  legal  gaps  identified  in  Section  3.  The 
policy  outcome  would  be  equivalent  to  that  of  option  3  and  amendments  (e.g.  by  amending  a 
combination of the PWD and the older labour acquis) would include the elements listed under '4.1.3 
Option  3  –  New  directive'.  Because  outcomes  related  to  workers  are  equivalent  to  those  under 
option 3, we only describe them briefly here and refer to the more substantive assessment under 
policy  option  3.  The main difference  with  respect  to  option  3  is  under  the heading  'Productivity, 
implementation and compliance costs'.  

Expected effects on AM usage and impacts on workers compared to baseline 

The  overall  impact  on  workers  is  similar  to  option  3.  While  everyday  work  pressure  does  not 
disappear, the overall effect is a 'medium++' improvement in job quality, with fewer unfair outcomes 
and  better  guardrails  in  place  for  monitoring  and  evaluation.  This  is  in  line  with  the  impact 
assessment of the PWD.278 It is expected that option 2 will have a small negative impact on AM usage 
relative to the baseline. However, due to the increased legal complexity of option 2, resulting from 
the amendment of several directives, it is expected to be less proportional in terms of outcomes 
than option 3, which can be better targeted. It is therefore expected that slower or non-uptake is 
likely among resource-constrained, low-margin employers, particularly SMEs. For AM prevalence, 
option 2 is labelled 'Small--'.  

Implementation and compliance costs 

As is the case for option 3, compliance for firms is non-trivial. Option 2 will have higher compliance 
costs for three reasons. First, directly for AM using companies, because several legislative changes 
are  likely  to  occur  in  Member  States  following  the  reimplementation  of  the  amendments  to  the 
relevant  directives.279  Second,  for  platform  companies  operating  under  the  assumption  of 
transposition of the PWD as it is now legislated. Amendments to the PWD will increase uncertainty 
among  platform  companies,  at  least  in  the  period  before  the  amendments  are  decided.  Third, 
indirectly for companies not involved with AM or platform work, as such companies will need to keep 
track of potential amendments to the labour acquis where necessary. As such, option 2 will indirectly 
affect all companies, including those not subject to AM regulation. For companies in general, the 
cost impact is labelled 'medium++' (higher than under option 3). Costs are relatively higher for SMEs 
and are labelled 'medium++/large+' (also higher than under option 3). Costs could be partly offset 
by higher legal certainty and lower costs for companies doing business in several Member States. 

The  one-off  legislative  process  also  adds  public  costs.  In  total,  administrative  costs  are  labelled 
'Medium++/Large+',  higher  than  option  3  ('Medium+').  While  it  is  difficult  to  assess  the  legal 
complexity  in  detail  without  examining  each  individual  directive  to  be  amended,  it  is  clear  that 
amending the PWD in particular is complex. The PWD is designed to regulate a business model with 
highly automated AM as a core element. It is not a priori clear how well uses and definitions of AM in 
the  PWD  will  carry  over  to  other  businesses  that  have  much  less  intense  or  lighter  use  of  AM 
procedures (often assisted by a human manager). Additionally, as detailed above, only some articles 

278   PPMI (2021). Study to support the impact assessment of an EU initiative to improve the working conditions in 

platform work. 

279   The PWD only needs to be transposed by 2 December 2026. 

93 

 
EPRS | European Parliamentary Research Service 

are relevant to amend. Relying on the old labour acquis (i.e., without the PWD) would require equally 
significant rewriting of existing legislation to cover all legal gaps.  

4.2.4. Impact of policy option 3 

Option 3 establishes a dedicated, horizontal Algorithmic Management Directive (AMD) that governs 
algorithmic management across all sectors and employment relationships, including those of solo 
self-employed individuals. Unlike the case of Option 2, a new directive would not be constrained by 
the structure and content of existing legislation, some of which apply more broadly across the labour 
market.  It  can  define  'algorithmic  management'  more  precisely  in  a  non-platform  work  context 
(which is potentially different from a platform work context, where the AM is integral to the business 
model), to cover automated system that allocates tasks, monitors and evaluates performance, or 
makes  or  materially  shapes  decisions  affecting  working  conditions;  and  rule  out  simpler,  less 
intrusive tools. The directive can set its own personal scope, applying by default to all workers in an 
employment  relationship  (and  seeking  employment,  in  case  of  HR  related  cases)  and  solo  self-
employed. It can calibrate obligations by risk and firm size, independent of the existing labour acquis. 
so  that  high-impact  deployments  face  stricter  duties,  while  genuinely  low-risk,  low-scale  uses 
comply in simpler ways. 

Addressing legal gaps 

A purpose-built directive has the advantage of being able to close all gaps identified in the legal 
analysis.  Scope  ceases  to  be  a  limiting  factor  as  the  instrument  can  apply  horizontally  across 
employers  and  sectors,  while  clarifying  coverage  for  non-standard  arrangements.  It  can,  where 
justified, extend a protective subset to dependent self-employed, addressing situations where AM 
significantly constrains autonomy despite formal self-employment. 

Transparency  shifts  from  case-by-case  notices  to  system-level  visibility.  Workers  and  their 
representatives would not only be informed when a particular decision affects them; they would also 
understand the systems in place in their workplace, their purposes, and their key limitations. This 
makes hidden algorithms less likely, reduces the information asymmetry that can fuel mistrust, and 
gives social partners a concrete basis for dialogue and oversight. 

Accountability  and  human  oversight  contain  an  explicit  prohibition  on  fully  automated  adverse 
decisions.  Where  algorithms  materially  shape  outcomes,  the  human  reviewer  must  be  trained, 
independent  enough  to  exercise  judgement,  and  capable  of  correcting  errors.  A  clear  right  to 
(human) explanation makes decisions intelligible and contestable. 

Risk  assessment  and  monitoring  become  continuous  practices  rather  than  one-off  checklists.  By 
integrating OSH, equality and data concerns in a single algorithmic impact assessment, employers 
assess not  just  technical  compliance  but  the  experienced  effects  of  AM  on  workload,  autonomy, 
fairness  and  safety.  High-risk  deployments  can  be  subject  to  stricter  internal  governance, 
notification to authorities, or prior consultation duties. 

The  AMD  can  also  tie  algorithmic  management  to  working-time  protections.  Requiring  advance 
notice  for  AM-generated rosters,  setting  conditions  for  short-notice  changes,  and clarifying  that 
out-of-hours prompts should be disabled or ignored by default which all help prevent technology 
from eroding existing rest and maximum-hours standards. This is a missing link in the current acquis. 

94 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

On data protection and privacy, the directive can clarify what is proportionate in employment. It can 
prohibit  categories  of  invasive  processing,  refine  legitimate-interest  tests  in  AM  contexts,  and 
require that data protection and algorithm impact assessments be performed together, reducing 
duplication while raising the bar for risky tools. 

Bias and discrimination can be addressed by requiring routine fairness testing appropriate to the 
use case, corrective retraining or retirement of biased systems, and cooperation with equality bodies 
for systemic risks. That shifts enforcement from ex-post litigation towards ex-ante prevention. 

Social dialogue and participation are made concrete by information and consultation duties before 
significant  deployments.  For  very  high-risk  tools,  Member  States  may  allow  co-decision  rights 
through  works  councils  or  sectoral  agreements.  This  ensures  AM  is  treated  as  an  organisational 
change rather than a purely technical procurement. 

Finally,  redress  and  enforcement  become  more  coherent.  Workers  gain  clear  complaint  routes, 
protection  from  retaliation,  and  access  to  representative  support.  At  system  level,  coordination 
between labour inspectorates, data-protection and equality authorities is encouraged or mandated, 
reducing the current fragmentation. 

Expected effect on AM usage 

A horizontal  directive  would  change  how  firms  evaluate  and  deploy AM.  The immediate  effect  is 
greater selectivity. Employers will weigh efficiency gains against governance duties, legal risk and 
reputational costs. High-risk or weakly justified tools are likely to be abandoned or redesigned. This 
should  filter  out  the  most  problematic  uses,  such  as  emotion  recognition  for  attentiveness,  and 
always-on surveillance,  without  curbing helpful  applications  that  genuinely  improve  productivity, 
coordination or safety. 

Sectors already reliant on AM, such as large logistics and e-commerce, as well as parts of retail and 
warehousing,  would  continue  to  use  these  systems,  but  with  more  rigorous  processes  including 
formal algorithmic impact assessments, human-in-the-loop checkpoints, calibrated monitoring, and 
clearer  worker  information.  Many  large  employers  that  already  run  GDPR  and  OSH  compliance 
system  would  integrate  processes  established  under  the  AMD  into  their  systems  and  integrate 
algorithmic governance. In finance and manufacturing, where performance analytics and dynamic 
tasking  have  grown,  firms  would  moderate  pacing  systems  that  generate  excessive  intensity  or 
disparate impact and invest in explainability features, override controls and audit trails. 

In SMEs and low-margin settings, the regime is likely to slow the adoption of complex or high-impact 
tools. Some small employers may defer experimental deployments, particularly where off-the-shelf 
systems cannot meet explanation or bias-testing needs. Proportionality would be important here. 
Simplified algorithmic impact assessment for low-risk uses, templates and guidance, and phased 
timelines can keep routine digital tools, basic time-recording, and simple rostering within reach of 
SMEs at no or little extra costs. The market response should also help. Producers of AM software 
will  compete  on  'compliance-ready'  features,  such  as  built-in  transparency  dashboards,  fairness 
checks, and administrator controls that make it easier for their customers (i.e. AM using companies) 
to meet obligations. Over time, this should raise the quality of AM in the market, even if it slows 
down, at an early stage, the rate of adoption. Overall, a small negative effect on AM usage, 'small-', 
is expected. 

95 

EPRS | European Parliamentary Research Service 

Impacts on workers compared to baseline 

For  workers,  Option  3  offers  the  most  substantial  improvement  over  the  baseline.  System-level 
transparency,  coupled  with  case-by-case  specific  explanations,  helps  workers  understand  what 
tools  exist,  what  they  do,  and  how  to  contest  mistakes.  Anxiety  associated  with  'unknown' 
algorithms would decline when significant decisions cannot be taken by software alone and when 
outcomes must be explained.  

Procedural fairness should improve. A trained human manager provides a buffer against data errors, 
and out-of-context metrics. The simple knowledge that adverse actions will be reviewed by a human 
and can be appealed helps alleviate the sense of being at the mercy of a machine. 

Psychosocial risks are better managed. By incorporating stress, fatigue, and work intensity into the 
algorithmic impact assessment and OSH regimes, employers would need to ensure that AM-driven 
pacing and monitoring are compatible with safe work practices. Where negative effects emerge, 
mitigation  is  mandatory.  Over  time,  this  should  reduce  instances  of  unrealistic  targets,  constant 
nudging, and surveillance practices that undermine wellbeing. 

Work-life balance should improve. Guardrails around the misuse of algorithmic scheduling, such as 
providing  advance  notice,  ensuring  the  reasonableness  of  changes,  and  disabling  out-of-hours 
prompts,  make  it  harder  for  technology  to  erode  rest  time  by  stealth.  While  not  eliminating  all 
pressures of modern work, this creates clearer expectations and fewer intrusions into personal time. 

Dignity and privacy gain firmer protection. The directive can draw lines against particularly intrusive 
techniques in employment and require proportionality for any monitoring. Workers are less likely to 
face practices that feel demeaning or invasive, which in turn supports trust in legitimate, safety-
oriented monitoring. 

Workers' voice and access to remedies are strengthened. Information and consultation requirements 
turn  AM  deployment  into  a  topic  for  social  dialogue  in  line  with  other  organisational  changes. 
Individual  rights  are  protected  by  accessible  procedures  and  representative  support,  with 
safeguards against retaliation. The expected overall effect would be a measurable improvement in 
job quality, characterised by lower stress, a better work-life balance, and a less adverse impact on 
health. For policy option 3, the impact is labelled 'medium++'. 

Implementation and compliance costs 

At EU level, drafting and adopting a horizontal directive that intersects labour law, data protection, 
equality  and  OSH  will  take  time.  Once  adopted,  Member  States  face  a  substantial  but  one-off 
transposition  effort,  as  well  as  the  need  to  align  enforcement  across  labour  inspectorates,  data 
protection authorities, and equality bodies. Some Member States may designate a coordination unit 
or build a structured cooperation mechanism. Member states can be supported from the EU level 
through  guidance  and  training  to  enhance  cross-border  consistency  in  transposition  and 
implementation.  

Authorities  will  need  additional  capacity.  Inspectors  and  case-handlers  require  training  in 
algorithmic  issues.  Cooperation  between  labour,  data  and  equality  regulators  can  minimise 
duplication and ensure coherent outcomes. Over time, periodic inspections would be enhanced to 
include AM checks. The directive should align with the AI Act and data-protection law (GDPR) to 
avoid  double  burdens,  allowing  combined  assessments  and  shared  documentation  where 

96 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

appropriate. Admin costs are labelled 'medium+' compared to 'medium++' in option 2 due to the 
more complex amendments of existing legislation in option 2. 

For  companies,  compliance  costs  are  non-trivial  but  deemed  manageable.  They  will  need  to 
inventory  AM  systems,  perform  and  periodically  update  algorithmic  impact  assessments  (often 
combined with Data protection IAs), appoint or train managers as reviewers for significant decisions, 
configure  systems  to  generate  explainable  outputs  and  audit  logs,  and  set  up  information  and 
consultation  routines  before  significant  deployments.  Larger  firms  will  absorb  these  duties  by 
extending  existing  governance  for  data  protection,  safety  and  internal  audit.  They  may  invest  in 
additional compliance staff and in tools that automate parts of the algorithmic IA process, including 
bias  testing  and  explanation  generation.  The  short-run  costs  are  real,  but  they  scale  with 
organisational  complexity  and  are  potentially  offset  by  reduced  legal  risk  and  reduce  costs  for 
companies involved in several Member States. Costs for companies using AM are labelled 'medium+'.  

For SMEs, the relative burden is higher. Proportionality is therefore important, and option 3 offers 
improved ability for proportionality than option 2 (as discussed above). Simplified algorithmic IAs 
for low-risk uses, standard templates and guidance, and phased application of the most demanding 
provisions. Public support, model documents, helplines, and sectoral guidance can lower fixed costs. 
Routinely used digital tools should remain feasible under a light-touch track. Costs for SMEs are 
labelled 'medium+'. Relative to option 2, this reflects the ability to have a better-targeted directive 
towards SMEs and low-scale non-risk usage. Under option 2, all SMEs have to keep attention to the 
amendment  of  existing  legislation,  whether  they  use  AM  or  not.  For  non-AM  using  companies, 
'compliance' will not require changes, but they will still incur legal costs to understand changes in 
national law and ensure that they remain compliant after the changes.  

4.2.5. Summary of impacts 

The  three  options  thus  illustrate  different  pathways  for  addressing  (part  of)  the  challenges  of 
algorithmic management, ranging from guidance to amendments of existing law and the creation of 
a  dedicated  directive.  Each  approach  carries  implications  for  coverage,  enforcement,  costs  and 
administrative  demands,  and  their  eventual  effects  would  depend  not  only  on  the  chosen  legal 
instrument  but  also  on  how  Member  States  transpose  and  implement  it,  how  firms  adapt  their 
practices, and how workers and social partners make use of the rights provided. 

Table 10 below summarises the key comparisons of the impact assessment. 

97 

 
 
EPRS | European Parliamentary Research Service 

Table 20 – Comparative assessment of policy options for addressing AM in the workplace280 

CRITERIA 

BASELINE (0) – NO 
ACTION 

OPTION 1 – 
RECOMMENDATION 

OPTION 2 – AMEND EXISTING 
LEGISLATION 

OPTION 3- NEW DIRECTIVE 
'AMD' 

Coverage of legal gaps (cf. 
Table 7) 

Impact on algorithmic 
management use 

Gaps remain, partly or 
wholly, unaddressed in 
many MS.  

No specific AM rights 
beyond the platform 
sector in many MS. 

Continued growth of AM 
across sectors (with 
uncertainty).  

Some changes in 
practices in MS where 
legislative action is taken. 

SMEs and large firms 
adopt AM without new 
constraints in MS without 
legislative action. 

Low. 

Partial uptake may improve 
transparency, oversight, etc., 
but no uniform guarantee. Many 
gaps likely persist in practice. 

Depends on additional 
measures281 adopted by MS. 

Small-: Growth of AM usage is 
mildly slowed relative to the 
baseline 

Some MS will adopt guidance 
(partly) in planned legislation 

Few MS will initiate new 
legislation 

Voluntary adoption of best 
practices by some firms. 

Depends on additional 
measures adopted by MS. 

High. 

Can cover all gaps identified 
through amendments of 
relevant directives. 

High. 

Can cover all gaps identified  

Small--: Growth in AM usage is 
expected to slow down mildly. 
SMEs may be more cautious 
about adopting AM due to 
compliance duties (which are 
expected to be more complex 
than option 3). 

Small-: Growth in AM usage is 
expected to slow down (but 
less than for option 2, due to 
less complexity). 

SMEs may be more cautious 
about adopting AM due to 
compliance duties.  

280   The assessment of policy options is relative to the baseline and impacts range from 'Small--' to 'Large+'. 

281  

'Additional measures' in the table refers to measures not envisaged under the baseline. It includes two components: 1) National legislation that takes place under the baseline but is 
influenced by the Recommendation, and 2) Member States taking legal action – which would not have been taken under the baseline – as a result of the recommendation. See main 
text for a discussion of these points. 

98 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of work 

CRITERIA 

BASELINE (0) – NO 
ACTION 

OPTION 1 – 
RECOMMENDATION 

OPTION 2 – AMEND EXISTING 
LEGISLATION 

OPTION 3- NEW DIRECTIVE 
'AMD' 

Effects on workers (vs. 
baseline) 

Negative trend – Rising 
stress, intensification, 
loss of autonomy, poorer 
work-life balance.  

Scale of the negative 
trend depends on the 
number of MS with 
legislative action 

Small+: Mild positive impact on 
workers. 

Some MS will adopt guidance 
(partly) in planned legislation 

Few MS will initiate new 
legislation 

Some workers see benefits if 
employers follow guidance (e.g. 
better transparency, some 
oversight).  

Overall risk reduction is low – 
uneven and dependent on 
voluntary compliance. Many 
workers see little change 
relative to baseline 

Depends on additional 
measures adopted by MS. 

Medium++: Workers (including 
solo self-employed) under AM 
should see improvements as 
legal gaps are addressed. 
Less stress and work-life 
balance is improved indirectly. 

Medium++: Workers (including 
solo self-employed) under AM 
should see improvements as 
legal gaps are addressed. 
Less stress and work-life 
balance is improved indirectly. 

Public implementation costs 

Baseline costs only for 
MS with legislative 
action. 

Depends on the number 
of MS with new 
legislative initiatives 

Small--: 

Few MS will initiate new 
legislation. 

Depends on additional 
measures adopted by MS. 

Medium++/Large+: amending 
and re-implementing several 
directives will incur significant 
(one-time) costs for public 
bodies (EU and national). 
Enforcement bodies need extra 
capacity. 

Medium+: Drafting and 
adopting a new directive carries 
significant costs for public 
bodies. These costs are likely to 
be smaller than under option 2 
due to less complexity.  

Enforcement bodies need extra 
capacity.  

99 

 
 
EPRS | European Parliamentary Research Service 

CRITERIA 

BASELINE (0) – NO 
ACTION 

OPTION 1 – 
RECOMMENDATION 

OPTION 2 – AMEND EXISTING 
LEGISLATION 

OPTION 3- NEW DIRECTIVE 
'AMD' 

Cost for companies 

Costs will increase in MS 
that take legislative 
action. 

Costs will increase for 
companies doing 
business in several MS 

SMEs are harder hit due 
to their lower capacity of 
assessing regulations in 
several countries. 

Small-: Small increase for 
companies (including SMEs) 
due to minor additional 
legislation. 

Costs are partly offset (though 
limited) by improved 
harmonisation (following 
guidelines).  

Depends on additional 
measures adopted by MS. 

Medium++. Costs for 
companies are likely to be 
substantial. The opening of 
several directives affects not 
only companies applying AM, 
but also other firms that need 
to pay attention to potential 
changes. SMEs face even higher 
costs (medium++/large+).  

Costs could be partly offset by 
higher legal certainty and lower 
costs for companies doing 
business in several Member 
States 

Medium+. Costs for companies 
(including SMEs) are 
substantial, but lower than 
under option 2, as fewer 
companies are affected and due 
to the potential for better 
targeting/proportionality.  

Costs could be partly offset by 
higher legal certainty and lower 
costs for companies doing 
business in several Member 
States 

Feasibility & timeline  Not assessed in baseline 

Quick to adopt at the EU level; 
relies on the political will of 
Member States to implement 
advice 

Longer legislative process. A 
package of existing legislation 
needs to be amended.  

Longer legislative process 
compared to option 1, but likely 
shorter compared to option 2. 

Legal clarity  

Increase in legal 
fragmentation across the 
EU. 

Weakening of the single 
market 

Only marginally improves legal 
clarity and the single market 

Depends on additional 
measures adopted by MS. 

Legally most complex, as 
amendments are difficult to 
tailor perfectly to AM. While 
some relevant definitions are 
available in the PWD, turning a 
directive regulating a business 
model into a partly horizontal 
directive is likely to be complex. 

Legally clearer than option 2. 

Likely the most proportional 
instrument. 

Complexity in taking care to 
align with existing labour acquis 
and the PWD. 

Improves single market by 
creating a level playing field  

100 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of work 

CRITERIA 

BASELINE (0) – NO 
ACTION 

OPTION 1 – 
RECOMMENDATION 

OPTION 2 – AMEND EXISTING 
LEGISLATION 

OPTION 3- NEW DIRECTIVE 
'AMD' 

Source: Authors' elaboration. 

Improves single market by 
creating a level playing field 

101 

EPRS | European Parliamentary Research Service 

REFERENCES 

Abraha, H., A pragmatic compromise? The role of Article 88 GDPR in upholding privacy in the workplace, 
International Data Privacy Law, Vol. 12(4), 2022, pp. 276–296. 

Acemoglu, D., Anderson, G., Beede, D., Buffington, C., Childress, E., Dinlersoz, E., Foster, L., Goldschlag, 
N., Haltiwanger, J., Kroff, Z., Restrepo, P. and Zolas N., Automation and the Workforce: A Firm-Level View 
from the 2019 Annual Business Survey, Center for Economic Studies (CES), 2022.  

Adams-Prassl,  J.,  Abraha,  H.,  Kelly-Lyth,  A.,  Silberman,  M.  and  Rakshita,  S.,  Regulating  algorithmic 
management: A blueprint, European Labour Law Journal, 2023, pp. 124-151.  

Adams-Prassl, J., Binns, R. and Kelly-Lyth, A., Directly Discriminatory Algorithms, Modern Law Review, 
Vol. 86, 2023, pp. 144-175.  

Agosti,  C.,  Bronowicka,  J.,  Polidoro,  A.  and  Priori,  G.,  Exercising  workers'  rights  in  algorithmic 
management systems - Lessons learned from the Glovo-Foodinho digital labour platform case, European 
Trade Union institute, 2023.  

AISI, About AISI, website.  

Ait  Larbi,  N.,  Costanzo,  E.,  Pych,  E.,  Rauzier,  F.,  Reich,  E.  and  Zibetta,  A.,  All  eyes  on  AI:  Tackling 
digitalisation and artificial intelligence in the workplace, Syndex & IndustriAll Europe, 2024.  

Albagli, D., Dokei, T. and Mitchell, A., AI Watch: Global regulatory tracker - Japan, 2024.  

Aloisi,  A.,  Boss  Ex  Machina:  Employer  Powers  in  Workplaces  Governed  by  Algorithms  and  Artificial 
Intelligence, Working Paper IE Law School, 2022. 

Anderljung, M., Barnhart, J., Korinek, A., Leung, J., O'Keefe, C., Whittlestone, J., Avin, S., Brundage, M., 
Bullock,  J.,  Cass-Beggs,  D.,  Chang,  B.,  Collins,  T.,  Fist,  T.,  Hadfield,  G.,  Hayes,  A.,  Ho,  L.,  Hooker,  S., 
Horvitz, E., Kolt, N., Schuett, J., Shavit, Y., Siddarth, D., Trager, R. and Wolf, K., Frontier AI Regulation: 
Managing Emerging Risks to Public Safety, 2023. 

Baiocco, S. and Fernández-Macías, E., The algorithmic management of work: A basic compass', European 
Commission, 2022, 

Baiocco, S., Fernández-Macías, E., Rani, U. and Pesole, A., The Algorithmic Management of work and its 
implications in different contexts, European Commission, 2022. 

Bernhardt, A., Kresge, L. and Suleiman, R., Data and Algorithms at Work: The Case for Worker Technology 
Rights, Center for Labor Research and Education, 2021. 

Biasi,  M.,  Problema  e  sistema  nella  regolazione  lavoristica  dell'intelligenza  artificiale:  note  preliminari, 
Federalismi.it, 2024, pp. 162-185. 

Bråten, M., Monitoring and surveillance in the workplace, Fafo, 2019. 

Broecke, S., Artificial intelligence and labour market matching, OECD Social, Employment and Migration 
Working Papers, No 284, OECD Publishing, 2023.  

Buolamwini, J. and Gebru, T., Gender Shades: Intersectional Accuracy Disparities in Commercial Gender 
Classification, Proceedings of Machine Learning Research, Vol. 81, 2018, pp. 77–91.  

BusinessEurope, SMEunited, CEE, ETUC and the liaison committee EUROCADRES/CEC, European social 
partners framework agreement on digitalisation, 2020.  

Byhovskaya, A., Employers hold too much power over information. Workers must claim their data rights, 
World Economic Forum, 2021.  

Calabrio, Telia uses automated WFM to transform its entire customer experience [Case study], website. 

102 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Calvino, F., Morris, C., Samek, L. and Squicciarini, M., Identifying and characterising AI adopters: A novel 
approach based  on  big data,  OECD Science,  Technology  and  Industry  Working  Papers,  No.  2022/06, 
OECD Publishing, 2022. 

Cameron, L.D., The Making of the 'Good Bad' Job: How Algorithmic Management Manufactures Consent 
Through Constant and Confined Choices, Administrative Science Quarterly, Vol. 69(2), 2024, pp. 458–
514.  

Cefaliello,  A.,  Moore,  P.V.  and  Donoghue,  R.,  Making  algorithmic  management  safe  and  healthy  for 
workers: Addressing psychosocial risks in new legal provisions, European Labour Law Journal, Vol. 14(2), 
2023, pp. 192-210.  

Center for Labor and a Just Economy, Worker Power and Voice in the AI Response, 2024. 

Cheng, M.M. and R.D. Hackett, A critical review of algorithms in HRM: Definition, theory, and practice, 
Human Resource Management Review, Vol. 31(1), 2021.  

Christenko, A., Jankauskaitė, V., Paliokaitė, A., van den Broek, E. L., Reinhold, K. and Järvis, M., Artificial 
intelligence for worker management: existing and future regulations, 2022. 

Christin, A., Kellogg, K.C. and Valentine, M.A., Algorithms at work: The new contested terrain of control, 
Academy of Management Annals, Vol. 14(1), 2020, pp. 366–410.  

Ciucciovino,  S.,  Intelligenza  artificiale  e  diritto  del  lavoro:  problemi  e  prospettive  -  Risorse  umane  e 
intelligenza  artificiale  alla  luce  del  regolamento  (UE)  2024/1689,  tra  norme  legali,  etica  e  codici  di 
condotta, Diritto delle Relazioni Industriali, No. 3, 2024, pp. 573-614. 

Claxton, G., Rae, M. and Winger, A., Employer-Sponsored Health Insurance 101, 2024.  

Council of Europe, Framework Convention on Artificial Intelligence and Human Rights, Democracy and 
the Rule of Law, No. 225, 2024.  

Cox, T. and Oosterwijk, G. R., Algorithmic management in the workplace. Case studies on the impact of 
algorithmic technologies in seven sectors in the Nordics, Foundation for European progressive studies, 
2024. 

Bundesministerium  für  Wirtschaft  und  Klimaschutz  &  Bundesministerium  für  Digitales,  KI  Einsatz  in 
Unternehmen in Deutschland 2024: Strategische Ausrichtung und internationale Position, 2024, p. 12.  

De  Stefano,  V.  and  Wouters,  M.,  AI  and  digital  tools  in  workplace  management  and  evaluation:  An 
assessment of the EU's legal framework, EPRS, European Parliament, May 2022. 

Department for Industrial and Employment Relations of Malta, FAQs on the Digitals Platform Delivery 
Wages Council Wage Regulation Order of 2022, website. 

DeStefano, T., De Backer, K. and Moussiegt, L., Determinants of digital technology use by companies, 
OECD Science, Technology and Industry Policy Papers, No. 40, OECD Publishing, 2017.  

Doellgast, V., O'Brady, S. and Wagner, I., Negotiating limits on algorithmic management in digitalised 
services: cases from Germany and Norway, Transfer: European Review of Labour & Research, Vol. 29(1), 
2023, pp. 105–120. 

Dupuis, M., Algorithmic management and control at work in a manufacturing sector: Workplace regime, 
union power and shopfloor conflict over digitalisation, New Technology, Work & Employment, Vol. 40(1), 
2025, pp. 81–101. 

European Data Protection Board, Guidelines 07/2020 on the concepts of controller and processor in the 
GDPR, website.  

European  Data  Protection  Board,  Guidelines  on  Data  Protection  Impact  Assessment  (DPIA)  and 
(wp248rev.01), website.  

103 

EPRS | European Parliamentary Research Service 

European  Data  Protection  Board,  Opinion  28/2024  on  certain  data  protection  aspects  related  to  the 
processing of personal data in the context of AI models, website.  

European Economic and Social Committee, A guide to Artificial Intelligence at the workplace, 2022.  

Enqvist,  L.,  Human  oversight'  in  the  EU  artificial  intelligence  act:  what,  when  and  by  whom?,  Law, 
Innovation and Technology, Vol. 15(2), 2023, pp. 508-535.  

Eurofound, Consultation in the enterprise, 2013. 

Eurofound, Automation, digitisation and platforms: Implications for work and employment, 2018.  

Eurofound, Employee monitoring and surveillance: The challenges of digitalisation, 2020.  

Eurofound, Restructuring legislation - European Restructuring Monitor (ERM), 2023. 

Eurofound, Regulatory responses to algorithmic management in the EU, 2024. 

European Commission, communication on Artificial Intelligence for Europe, COM/2018/237 final, 2018.  

European Commission, White Paper on Artificial Intelligence – A European approach to excellence and 
trust, 2020.  

European  Commission,  European  enterprise  survey  on  the  use  of  technologies  based  on  artificial 
intelligence, 2020.  

European Commission, Proposal for a directive on adapting non-contractual civil liability rules to artificial 
intelligence (AI Liability Directive), 2022.  

European Commission, Study exploring the context, challenges, opportunities and trends in algorithmic 
management, 2023. 

Fernandez Macias, E., Urzì Brancati, M.C., Pesole, A., and Wright, S., The platformisation of work, 2023.  

European Commission, communication on A Competitiveness Compass for the EU, COM/2025/30, 2025.  

European Commission, Study exploring the context, challenges, opportunities, and trends in algorithmic 
management in the workplace, 2025.  

European Commission, Ethics Guidelines for Trustworthy AI, 2019.  

European Parliament, Resolution of 21 January 2021 on the right to disconnect.  

Baranowska, N., Biegas, A.J., Dennis, M.J., Fabris, A., Graus, D., Hacker, P., Saldivar, J. and Zuiderveen 
Borgesius, F., Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey, ACM Transactions on 
Intelligent Systems and Technology, 2024.  

Faioli, M., Mansioni e macchina intelligente, Giappichelli, 2018. 

Faioli, M., Assessing Risks and Liabilities of AI-Powered Robots in the Workplace. An EU-US Comparison, 
Diritto della sicurezza sul lavoro, 2025, pp. 79-113.  

Fernández-Macías, E., Urzì Brancati, M.C., Pesole, A. and Wright, S., The platformisation of work, 2023.  

Fraunhofer IPA, Neuer Leitfaden für den sicheren KI-Einsatz bei Audi, 2023.  

Galière, S., 'When food-delivery platform workers consent to algorithmic management: a  Foucauldian 
perspective', New Technology, Work and Employment, Vol. 35, No. 3, 2020, pp. 357-370. 

Gent, C., Cyberboss. The rise of algorithmic management and the new struggle for control at work, Verso, 
London/New York, 2024. 

Gonzalez  Vazquez,  I.,  Curtarelli,  M.,  Anyfantis,  I.,  Brun,  E.  and  Starren,  A.,  Digitalisation  and  workers 
wellbeing: The impact of digital technologies on work-related psychosocial risks, 2024.  

Goods, C., Veen, A. and Barratt, T., 'Is your gig any good?' Analysing job quality in the Australian platform-
based food-delivery sector, Journal of Industrial Relations, Vol. 61(4), 2019, pp. 502-527. 

Gratas, B., What is the Difference Between Assessment and Audit?, 2022.  

104 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Green, A., Artificial intelligence and the changing demand for skills in the labour market, OECD Artificial 
Intelligence Papers, No. 14, OECD Publishing, 2024.  

HAI Stanford, The 2025 AI Index Report, 2025.  

Hertel-Fernandez,  A.,  Estimating  the  prevalence  of  automated  management  and  surveillance 
technologies at work and their impact on workers' well-being, Washington Center for Equitable Growth, 
2024.  

Hießl,  C.,  Case  Law  on  Algorithmic  Management  at  the  Workplace:  Cross-European  Comparative 
Analysis and Tentative Conclusions, 2025.  

Holubová, B., Algorithmic Management: awareness, risks and responses of the social partners, 2022.  

Hunt T. and Pickard, H., Harder, better, faster, stronger? Work intensity and 'good work' in the UK, 2021.  

ILO, Digital Employment Diagnostic Guidelines, International Labour Office, 2023.  

IMD, World Digital Competitiveness Ranking 2024, 2024. 

IPA, DX Trend 2024, Information-technology Promotion Agency, 2024.  

Japanese Government, AI Guidelines for Business Ver1.0, 2024.  

Jarrahi, M.H., Newlands, G., Lee, M.K., Wolf, C.T., Kinder E. and Sutherland, W., Algorithmic management 
in a work context, Big Data & Society, Vol. 8(2), 2021. 

Jarrahi,  M.H.,  Möhlmann,  M.  and  Lee,  M.K.,  Algorithmic  Management:  The  Role  of  AI  in  Managing 
Workforces, MIT, 2023. 

Krzywdzinski,  M.,  Schneiß,  D.  and  Sperling,  A.,  Between  control  and  participation:  The  politics  of 
algorithmic management, New Technology, Work & Employment, Vol. 40(1), 2025, pp. 60–80. 

Lamers, L., Meijerink, J. and Rettagliata, G., Blinded by 'algo economicus': Reflecting on the assumptions 
of algorithmic management research to move forward, Human Resource Management, Vol. 63(3), 2024, 
pp. 413–426. 

Lane, M., Williams, M. and Broecke, S., The impact of AI on the workplace: Main findings from the OECD 
AI surveys of employers and workers, OECD Social, Employment and Migration Working Papers, No. 288, 
OECD Publishing, 2023.  

Larson, R., Controlling social desirability bias, International Journal of Market Research, Vol. 61(5), 2018, 
pp. 534-547.  

Lee, M.K., Kusbit, D., Dabbish, L.A. and Metsky, E., Working with Machines: The Impact of Algorithmic 
and Data-Driven Management on Human Workers, 2015.  

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I. and Kasper, G., The 
Challenges of Algorithm-Based HR Decision-Making for Personal Integrity, Journal of Business Ethics, 
Vol. 160, 2019, pp. 377–392.  

Lin,  J.,  Gao,  H.,  Feng,  X.,  Xu,  R.,  Wang,  C.,  Zhang,  M.,  Guo,  L.  and  Xuet,  S.,  Advances  in  Embodied 
Navigation Using Large Language Models: A Survey, 2024.  

Lynn,  J.,  Gao,  S.,  Kim,  R.Y.,  Lee,  M.K.,  Pandya,  S.S.  and  Schneider,  D.,  Regulating  Algorithmic 
Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace 
Scheduling, 2025. 

Marr, C. and Williams, Z., New York State Bills Push AI Worker Bias Guardrails Past NYC Law, 2024.  

Mast, N. and Kresge, L., How Common is Employers' Use of Workplace Management Technologies? A 
Review 
2022. 
UC 
https://tcf.org/content/report/datafication-employment-surveillance-capitalism-shaping-workers-
futures-without-V 

Prevalence 

Berkeley 

Studies, 

Center, 

Labor 

of 

105 

EPRS | European Parliamentary Research Service 

Mateescu, A., Explainer: Challenging Worker Datafication, SSRN Electronic Journal, 2023.  

Mateescu, A. and Nguyen, A., Workplace Monitoring & Surveillance, 2019.  

Meil, P., Inside looking out: Digital transformation in the German automobile sector and its effects on the 
value chain, in Drahokoupil, J. (ed.), The challenge of digital transformation in the automotive industry: 
Jobs, upgrading and the prospects for development, European Trade Union Institute, Brussels, 2020, pp. 
36–37.  

Meijerink, J. and Bondarouk T., The duality of algorithmic management: Toward a research agenda on 
HRM algorithms, autonomy and value creation, Human Resource Management Review, Vol. 33(1), 2023. 

Meijerink,  J.,  Boons,  M.,  Keegan,  A.  and  Marler,  J.,  Algorithmic  human  resource  management: 
Synthesizing developments and cross-disciplinary insights on digital HRM, The International Journal of 
Human Resource Management, Vol. 32(12), 2021, pp. 2545–2562.  

METI, Digital Transformation: Overcoming of '2025 Digital Cliff' Involving IT Systems and Full-fledged 
Development of Efforts for Digital Transformation, Japanese Ministry of Economy, Trade and Industry, 
2018. 

Metrology.news, Audi Rolls-Out Artificial Intelligence for Quality Control of Spot Welds, website.  

Milanez, A., Lemmens, A. and Ruggiu, C., Algorithmic management in the workplace: New evidence from 
an OECD employer survey, OECD Artificial Intelligence Papers, No. 31, OECD Publishing, 2025.  

Ministerio de Trabajo y Economía Social, Resolución de 19 de mayo de 2023, de la Dirección General de 
Trabajo, por la que se registra y publica el V Acuerdo para el Empleo y la Negociación Colectiva, 2023.  

Möhlmann,  M.,  Zalmanson,  L.,  Gregory,  R. W.,  and  Henfridsson,  Algorithmic Management  of  Work on 
Online Labor Platforms: When Matching Meets Control, MIS Quarterly, 2021.  

Nilsson,  K.H.,  Bodin,  T.,  Brulin,  E.,  Håkansta,  C.,  Lee,  M.K.  and  Matilla-Santander,  N.,  Algorithmic 
management and occupational health: A comparative case study of organizational practices in logistics, 
Safety Science, Vol. 187, 2025. 

Oberai, H. and Anand, I.M., Unconscious bias: thinking without thinking, Human Resource Management 
International Digest, Vol. 26(6), 2018, pp. 14-17.  

OECD, Negotiating Our Way Up: Collective Bargaining in a Changing World of Work, OECD Publishing, 
2019.  

OECD, OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market, OECD Publishing, 
2023.  

OECD, Using AI in the workplace: Opportunities, risks and policy responses, OECD Artificial Intelligence 
Papers, No. 11, OECD Publishing, 2024.  

OECD,  OECD  Digital  Economy  Outlook  2024  (Volume  1):  Embracing  the  Technology  Frontier,  OECD 
Publishing, 2024.  

OSHA, Spain: The 'Riders' Law', New Regulation on Digital Platform Work, 2022. 

OSHA, Worker management through AI: implications for occupational safety and health, 2024. 

OSHA, Digital technologies for worker management: implications for safety and health. A comparative 
study of two automotive companies in Belgium and Italy, 2024. 

Parent-Rocheleau,  X.  and  Parker,  S.K.,  Algorithms  as  work  designers:  How  algorithmic  management 
influences the design of jobs, Human Resource Management Review, Vol. 32(3), 2022, pp. 1–17.  

Peruzzi, M., IA e obblighi datoriali di tutela del lavoratore: necessità e declinazioni dell'approccio risk-
based, Federalismi.it, No. 30, 2024, pp. 225-249. 

106 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Pape, M., Addressing AI risks in the workplace: Workers and algorithms, EPRS, European Parliament, June 
2024 

Pesole, A., Algorithmic Management and the Platformisation of Work in Europe: Evidence from Spain and 
Germany, The Indian Journal of Labour Economics: An Organ of the Indian Society of Labour Economics, 
2025, pp. 1–28. 

Piasna, A., Algorithms of Time: How Algorithmic Management Changes the Temporalities of Work and 
Prospects for Working Time Reduction, Cambridge Journal of Economics, Vol. 48(1), 2024, pp. 115–132. 

Quirke, B., Making the Connections, Routledge, 2017.  

Rani, U., Pesole, A. and Gonzalez Vazquez, I., Algorithmic Management practices in regular workplaces: 
case studies in logistics and healthcare, 2024.  

Salvi del Pero, A., Wyckoff, P., and Vourc'h, A., Using Artificial Intelligence in the workplace: What are the 
main ethical risks?, OECD Social, Employment and Migration Working Papers, No. 273, OECD Publishing, 
2022.  

Shapiro, A., Between autonomy and control: Strategies of arbitrage in the 'on-demand' economy, New 
Media & Society, Vol. 20(8), 2018, pp. 2954-2971. 

Simms, K., 4 AI case studies: Insights from H&M, Sage, Telenor and Vipps MobilePay, VUX World, 2025.  

Social Europe, Open Letter: Algorithmic Management and the Future of Work in Europe, 2024.  

Stohr,  E.  and  Zhao,  J.,  Workflow  Automation:  Overview  and  Research  Issues,  Information  Systems 
Frontiers, Vol. 3, 2001, pp. 281-296.  

Strohmeier,  S.  and  Piazza,  F.,  Artificial  Intelligence  Techniques  in  Human  Resource  Management—A 
Conceptual  Exploration,  Intelligent  Techniques  in  Engineering  Management,  Springer  International 
Publishing, Cham, 2015, pp. 149–172. 

Szpejna, M. and Boudalaoui-Buresi, Z.,  The scope of EU labour law, Policy Department for Economic, 
Scientific and Quality of Life Policies, European Parliament, 2020.  

Thorn  Jensen,  M.,  Oosterwijk,  G.  and  Sonne  Nørgaard,  A.,  Computer  in  Command:  Consequences  of 
Algorithmic Management for Workers, Foundation for European Progressive Studies, 2024. 

Treu, T., CRSD, direttiva sui lavoratori delle piattaforme e valutazione dei rischi, Federalismi.it, No. 30, 
2024, p. 2-19. 

UNI Europa, Database of AI and AM in collective bargaining agreements, website.  

Urzi Brancati, M.C. and Curtarelli, M., Digital tools for worker management and psychosocial risks in the 
workplace: evidence from the ESENER survey, 2021. 

Urzì Brancati, M.C., Curtarelli, M., Riso, S. and Baiocco, S., How digital technology is reshaping the art of 
management, 2022. 

Urzì Brancati, M.C., Digital technologies at work and psychosocial risks: evidence and implications for 
occupational safety and health, 2024.  

Verband der Automobilindustrie, 'Workforce developments'. 

Wirtschaftskraft,  Audi  startet  Roll-out  einer  Künstlichen 
Schweißpunkten, 2023.  

Intelligenz  zur  Qualitätskontrolle  von 

Wood, A.J., Algorithmic Management, Weizenbaum Journal of the Digital Society, Vol. 4(3), 2024. 

Wood, A.J., Graham, M., Lehdonvirta, V. and Hjorth, I., Good Gig, Bad Gig: Autonomy and Algorithmic 
Control in the Global Gig Economy, Work, Employment and Society, Vol. 33(1), 2019, pp. 56-75. 

107 

EPRS | European Parliamentary Research Service 

Zayid, H., Alzubi, A., Berberoğlu, A. and Khadem, A., How Do Algorithmic Management Practices Affect 
Workforce Well-Being? A Parallel Moderated Mediation Model, Behavioral Sciences, Vol. 14(12), 2024, p. 
1123.  

Zolas, N., Beede, D.N., Brynjolfsson, E., Buffington, C., Dinlersoz, E., Foster, L., Goldschlag, N., Gregory, 
R. W., Kroff, Z. and McElheran, K., Advanced Technologies Adoption and Use by U.S. Firms: Evidence 
from the Annual Business Survey, CES 20–40, 2020. 

Zuboff,  S.,  The  Age  of  Surveillance  Capitalism:  The  Fight  for  a  Human  Future  at  the  New  Frontier  of 
Power, Harvard Business School, 2019. 

EU Legislation 

Council Directive of 12 June 1989 on the introduction of measures to encourage improvements in the 
safety and health of workers at work, 89/391/EEC, 1989.  

Council Directive of 29 May 1990 on the minimum safety and health requirements for work with display 
screen equipment, 90/270/EEC, 1990.  

Council Directive of 30 November 1989 concerning the minimum safety and health requirements for the 
workplace, 89/654/EEC, 1989.  

Council Directive  of  20  July  1998  on  the  approximation  of  the  laws  of  the Member  States  relating  to 
collective redundancies, 98/59/EC, 1998.  

Council  Directive  of  29  June  2000  implementing  the  principle  of  equal  treatment  between  persons 
irrespective of racial or ethnic origin OJ L 180, 19.7.2000, p. 22–26, 2000/43/EC, 2000.  

Council  Directive  of  27  November  2000  establishing  a  general  framework  for  equal  treatment  in 
employment and occupation, OJ L 303, 2.12.2000, p. 16–22, 2000/78/EC, 2000.  

Directive  of  the  European  Parliament  and  of  the  Council  of  11  March  2002  establishing  a  general 
framework for informing and consulting employees in the European Community, 2002/14/EC, 2002. 

Directive of the European Parliament and of the Council of 12 July 2002 concerning the processing of 
personal data and the protection of privacy in the electronic communications sector (Directive on privacy 
and electronic communications), OJ L 201, 31.7.2002, p. 37–47, 2002/58/EC, 2002.  

Directive of the European Parliament and of the Council of 4 November 2003 concerning certain aspects 
of the organisation of working time, OJ L 299, 18.11.2003, p. 9–19, 2003/88/EC, 2003.  

Directive of the European Parliament and of the Council of 5 July 2006 on the implementation of the 
principle of equal opportunities and equal treatment of men and women in matters of employment and 
occupation (recast), OJ L 204, 26.7.2006, p. 23–36, 2006/54/EC, 2006.  

Directive  of  the  European  Parliament  and  of  the  Council  of  6  May  2009  on  the  establishment  of  a 
European Works Council or a procedure in Community-scale undertakings and Community-scale groups 
of undertakings for the purposes of informing and consulting employees (Recast), OJ L 122, 16.5.2009, 
p. 28–44, 2009/38/EC, 2009.  

Directive of the European Parliament and of the Council of 16 September 2009 concerning the minimum 
safety and health requirements for the use of work equipment by workers at work, consolidated text, 
2009/104/EC, 2009.  

Regulation of the European Parliament and of the Council of 27 April 2016 on the protection of natural 
persons with regard to the processing of personal data and on the free movement of such data, and 
repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119, 4.5.2016, p. 1–88, (EU) 
2016/679, 2016.  

108 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Directive of the European Parliament and of the Council of 20 June 2019 on transparent and predictable 
working conditions in the European Union, OJ L 186, 11.7.2019, p. 105–121, (EU) 2019/1152, 2019.  

Directive of the European Parliament and of the Council of 20 June 2019 on work-life balance for parents 
and carers and repealing Council Directive 2010/18/EU, OJ L 188, 12.7.2019, p. 79–93, (EU) 2019/1158, 
2019.  

Directive of the European Parliament and of the Council of 10 May 2023 to strengthen the application of 
the principle of equal pay for equal work or work of equal value between men and women through pay 
transparency and enforcement mechanisms, OJ L 132, 17.5.2023, p. 21–44, (EU) 2023/970, 2023.  

Regulation of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules 
on  artificial  intelligence  and  amending  Regulations  (EC)  No  300/2008,  (EU)  No  167/2013,  (EU)  No 
168/2013,  (EU)  2018/858,  (EU)  2018/1139  and  (EU)  2019/2144  and  Directives  2014/90/EU,  (EU) 
2016/797 and (EU) 2020/1828 (Artificial Intelligence Act), OJ L, 2024/1689, 12.7.2024, (EU) 2024/1689, 
2024.  

Directive  of  the  European  Parliament  and  of  the  Council  of  23  October  2024  on  improving  working 
conditions in platform work, OJ L, 2024/2831, 11.11.2024, (EU) 2024/2831, 2024.  

Case Law 

Judgment of the Court of 3 July 1986, Lawrie-Blum v Land Baden-Württemberg, Case 66/85, para. 17.  

Judgment of the Court (Fifth Chamber), 8 February 2001, Wolfgang Lange v Georg Schünemann GmbH, 
C-350/99, ECLI:EU:C:2001:84.  

Judgment of the Court (Fourth Chamber), 18 December 2014, Fag og Arbejde (FOA) v Kommunernes 
Landsforening (KL), C-354/13, ECLI:EU:C:2014:2463. 

Judgment of the Court (First Chamber) of 24 November 2016, David L. Parris v Trinity College Dublin 
and Others, C-443/15, ECLI:EU:C:2016:897. 

Judgment of the Court of Justice of 21 February 2018, Ville de Nivelles v Rudy Matzak, C-518/15, ECLI: 
EU:C:2018:82. 

Judgment of the Court (Grand Chamber) of 17 April 2018, Vera Egenberger v Evangelisches Werk für 
Diakonie und Entwicklung e.V., C-414/16, ECLI:EU:C:2018:257. 

Judgment of the Court (Grand Chamber), 5 June 2018, Wirtschaftsakademie Schleswig-Holstein, Case 
C-210/16, ECLI: EU:C:2018:388. 

Judgment  of  the  Court  of  14  May  2019,  Federación  de  Servicios  de  Comisiones  Obreras  (CCOO)  v 
Deutsche Bank, C-55/18.  

Judgment of the Court (Second Chamber), 29 July 2019, Fashion ID, Case C-40/17, ECLI: EU:C:2019:629. 

Ruling of the Court of Bologna, Ordinanza of 31 December 2020.  

Judgment of the Court (First Chamber), 30 March 2023, Hauptpersonalrat der Lehrerinnen und Lehrer, 
C-34/21, ECLI:EU:C:2023:270.  

Judgment of the Court 7 March 2024, IAB Europe, C-604/22, ECLI:EU:C:2024:214. 

Judgment  of  the  Court  (First  Chamber),  27  February  2025,  Dun  &  Bradstreet  Austria,  C-230/22, 
ECLI:EU:C:2025:117.  

109 

 
 
 
EPRS | European Parliamentary Research Service 

ANNEX A - ALGORITHMIC MANAGEMENT DEFINITIONS 

AUTHOR/INSTITUTION 

& YEAR 

DEFINITION 

Lee et al. (2015)  Algorithms and their supporting systems that take on manager roles. 

Möhlmann & Zalmanson 
(2017) 

Software algorithms that oversee and control many remote workers by 
tracking and automating decisions. 

Leicht-Deobald et al. 
(2019) 

Algorithms designed to assist and govern HR decisions. 

Wood et al. (2019)  An extension of how companies manage customers, but applied to workers. 

Duggan et al. (2020)  A control system where self-learning algorithms make and act on decisions 

about labour, reducing human involvement, often using worker data. 

Gal et al. (2020)  Computer methods that use digital data from different parts of an 
organisation to understand worker behaviour. 

Cheng & Hackett (2021)  Computer programs that efficiently process information to recommend HR 

actions like hiring or training. 

Jarrahi et al. (2021)  An ongoing process where people in an organisation interact with 

algorithms that guide their work. 

Möhlmann et al. (2021)  Collecting and using large amounts of data on a platform to create and 

improve algorithms that handle traditional management tasks. 

Newlands (2021)  A collection of computer processes that automatically generate data, 

evaluate worker behaviour, and assign tasks. 

Meijerink & Bondarouk 
(2023) 

A control system based on data that computers can read and software 
algorithms. 

Piasna (2024)  AM as 'the use of a diverse set of technological tools and techniques to 

Rani, Pesole, and 
Gonzalez Vazquez (ILO 
2024) 

automate managerial roles in companies, including task allocation, 
monitoring, evaluation and the disciplining of workers'. She further states 
that its reliance on AI allows for the automated or semi-automated 
implementation of decisions, thereby eliminating the need for human 
intervention. 

The ILO (2024) report then revises and expands upon this definition to 
capture a broader scope of digital technologies' effects on work 
organisation and monitoring. This broader approach includes: (i) Specific-
purpose digital technologies, those designed for a particular function or 
application. (ii) General-purpose digital technologies, those with wider 
applications that can be adapted to different situations, even if not 
explicitly designed for work coordination. The ILO report notes that 
technologies not explicitly intended for work coordination can still have 
significant direct effects on work organisation, task allocation, job quality, 
and industrial relations. For example, the report highlights how instant 
messaging apps like WhatsApp can exert strong control and pressure over 
workers, and algorithms designed to manage patient flows in healthcare can 
also affect work organisation and potentially be used for worker monitoring. 

Eurofound (2024) 

It adopts a broader conceptual approach. It considers the impact of 
'specific-purpose' and 'general-purpose' digital technologies, including 

110 

Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

AUTHOR/INSTITUTION 

& YEAR 

DEFINITION 

Dupuis (2025) 

Faioli (2025) 

OECD (2025) 

OSHA (2025) 

those not explicitly designed for work coordination but which nonetheless 
exert a significant influence on work organisation, task allocation, job 
quality, and industrial relations. This includes tools like instant messaging 
apps that can exert control or pressure over worker. 

AM as 'a system of control where self-learning algorithms are given the 
responsibility for making and executing decisions affecting labour, thereby 
limiting human involvement and oversight of the labour process'. Dupuis 
notes that these definitions highlight the automation of management 
functions and their potential impact on labour effort, but may not fully 
capture the complexity and variety of tasks algorithms can perform, or who 
controls their implementation 

AI/AM as a 'Third Element'. The presence of AI, particularly advanced forms 
like Frontier AI or General-Purpose AI (GPAI), introduces a 'third element' 
into the traditional worker-employer relationship. This intelligent machine or 
robot actively interacts with workers and employers, challenging 
established legal, HR management and organisational frameworks.  

AM as 'the use of technological tools which may include artificial intelligence 
(AI), to fully or partially automate tasks traditionally carried out by human 
managers'. This broad definition encompasses the use of software for 
functions such as giving instructions to workers, monitoring them, or 
evaluating them. This aligns with similar broad understandings of 
algorithmic management used in their survey 

AI and algorithm-based worker management (AIWM) refers to digital 
systems that gather work-related data, including data on workers and tasks. 
This data is then fed into algorithmic or AI systems. The purpose of these 
systems is to make automated or semi-automated decisions or provide 
information to employers, managers, human resource services, and 
sometimes workers. These decisions and information are used for various 
managerial functions, including allocating work shifts, allocating work tasks, 
supervising/monitoring workers' performance or behaviour, evaluating 
workers' performance. OSHA emphasizes that AIWM can lead to optimised 
work organisation, potentially benefiting both employers and workers if 
occupational safety and health (OSH) fundamentals are properly addressed. 
It also notes that these systems redefine task execution, coordination, and 
worker supervision. While automated decisionmaking involves AI systems 
making decisions independently, semi-automated systems offer insights 
and support for informed decisionmaking by humans. 

111 

 
 
 
EPRS | European Parliamentary Research Service 

ANNEX B – DESCRIPTION OF SURVEY DATA SOURCES 

This Annex gives a brief description of each of the four surveys. 

OECD (OECD2024) 

The  OECD  employer  survey282  is  conducted  by  the  OECD  under  its  AI-WIPS  programme  (with 
fieldwork by Ipsos). The survey targets mid-level managers/supervisors in firms so they can report 
directly on the tools their workplace uses and how those tools affect management and workers. It 
covers 6,047 firms with 20+ employees across six countries, France, Germany, Italy, Japan, Spain 
and the United States, with data collected from June to August 2024. The questionnaire measures 
the prevalence and intensity of algorithmic management (AM) via 15 concrete use cases grouped 
into  three  categories:  instruction  (e.g.,  allocating  work  schedules/activities,  providing  task 
instructions), monitoring (e.g., tracking working time, completion and speed of work, content/tone 
of communications, worker location, health & safety, fatigue/alertness), and evaluation (e.g., setting 
targets, rewarding or sanctioning performance, leaderboards). Beyond adoption, it asks managers 
why firms do not adopt (e.g., costs, staff resistance, concern for workers), and gathers perceptions 
of the impacts on managerial decision quality, speed, and autonomy, as well as job satisfaction and 
skill  needs  (managerial,  analytical,  and  social). 
It  also  probes  trustworthiness  concerns 
(accountability for wrong decisions, explainability, protection of workers' physical/mental health) 
and inventories governance measures in place (guidelines, worker consultation, audits, options to 
opt-out/access/request correction of data), enabling cross-country comparisons of both practices 
and safeguards.  

ESENER (ESENER2024) 

ESENER 2024 (European Survey of Enterprises on New and Emerging Risks). Run by the European 
Agency for Safety and Health at Work (EU-OSHA), the 2024 wave targets establishments with five 
or  more  employees,  interviewing  the  person  most  knowledgeable  about  OSH  risk  management 
(typically  the  senior  manager  responsible  for  OSH/HR)  and,  where  applicable,  worker 
representatives.  The  sample  comprises  41,458  establishments  across  the  EU-27,  plus  Iceland, 
Norway,  and  Switzerland,  with  fieldwork  conducted  from  May  to  October  2024.  For  measuring 
algorithmic management (AM), the master questionnaire includes a digitalisation module that asks 
whether  the  establishment  uses  machines/systems/computers  that  automatically  allocate 
tasks/working time/shifts, systems that monitor workers' performance or behaviour, AI systems to 
perform work tasks, as well as robots interacting with workers and wearables, allowing proxies of 
AM exposure at the establishment level. It also inquires whether routine risk assessments cover the 
use  of  digital  technologies  (including  automatic  allocation/monitoring)  and  whether  employees 
were consulted about the health and safety implications of such technologies (e.g., Q314). Beyond 
items,  ESENER  2024  covers  core  OSH  management  practices  (regular  risk 
AM-relevant 
assessments, 
(stress, 
harassment/violence procedures), training, remote/hybrid work, drivers and barriers to addressing 
risks, and inspectorate interactions. 

risk  management 

internal/external 

psychosocial 

expertise), 

282   Algorithmic management in the workplace, OECD, 2025 

112 

 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Eurobarometer 

Eurobarometer 554283 is an EU-wide public-opinion survey fielded in April–May 2024 that includes 
a  dedicated  workplace  module.  The  sample  size  is  26,000  respondents  distributed  on  27  EU 
countries. It asks employed respondents about their awareness of their employer's use of digital 
technologies/AI to manage work; whether such tools have been used in their current/most recent 
workplace  for  six  core  management  functions:  hiring,  task  allocation,  scheduling,  monitoring, 
performance assessment (rewards/sanctions), and safety; what information/explanations and data 
access  they  received;  and  their  attitudes  to  robots/AI  and  to potential  rules  (privacy  protection, 
limits on automated monitoring/decisions, transparency, and worker involvement). The instrument 
thus captures worker-level exposure and perceptions rather than establishment-level adoption. 

EWCS2024 

EWCS  2024  (European  Working  Conditions  Survey).  Run  by  Eurofound,  the  2024  wave  targets 
workers  (employees  and  the  self-employed)  across  35  European  countries  (EU-27  plus  eight 
neighbours);  fieldwork  ended  in  November  2024,  with  a  target  of  around  50,000  interviews.  For 
measuring algorithmic management (AM), the questionnaire includes worker-level items on whether 
a computer/system monitors work performance, allocates tasks, or determines when to work (used 
in our analysis as Q56A–C), enabling a binary exposure indicator and breakdowns by worker and job 
characteristics. Beyond AM, EWCS 2024 covers core job-quality domains (earnings, prospects, skills 
&  discretion,  working  time,  work  intensity,  social  and  physical  environment),  plus  modules  on 
digitalisation/AI  at  work,  remote  work,  OSH/psychosocial  risks,  work–life  balance,  training, 
autonomy, and social relations. 

283   Artificial Intelligence and the future of work. 

113 

 
 
 
EPRS | European Parliamentary Research Service 

ANNEX  C  –  AM  PREVALENCE  BY  WORKER  AND  WORKPLACE 
CHARACTERISTICS 

Table C1 – AM prevalence among employees by company size (EWCS2024) 

COUNTRY  TOTAL 

COMPANY SIZE 

Micro (<10 
employees) 

Small (<50 
employees) 

Medium (<250 
employees) 

Large (>250 
employees) 

47 

55 

63 

39 

43 

46 

40 

73 

50 

38 

53 

43 

42 

51 

39 

60 

51 

35 

59 

50 

45 

64 

45 

35 

42 

51 

46 

53 

53 

57 

66 

32 

46 

49 

46 

79 

53 

26 

52 

47 

55 

67 

35 

62 

59 

40 

75 

54 

44 

63 

59 

60 

52 

57 

60 

40 

EU 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

42 

50 

59 

36 

35 

43 

38 

70 

43 

27 

42 

41 

37 

49 

33 

53 

46 

31 

60 

41 

40 

66 

40 

27 

40 

51 

40 

40 

31 

38 

51 

31 

31 

41 

23 

53 

40 

19 

32 

30 

21 

40 

24 

40 

39 

29 

45 

25 

32 

69 

18 

27 

35 

43 

27 

30 

41 

51 

54 

39 

35 

41 

35 

74 

40 

35 

43 

45 

33 

54 

35 

46 

50 

29 

54 

41 

35 

67 

37 

17 

43 

52 

37 

42 

Source: Authors' calculations based on EWCS2024  

114 

 
 
 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Table C2 – AM prevalence of employees by sex, education and age (EWCS2024) 

COUNTRY 

TOTAL 

SEX 

EDUCATIONAL 
ATTAINMENT 

AGE GROUP 

Women 

Men 

Low 

Medium 

High 

20-34 

35-49 

60-64 

EU 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

42 

50 

59 

36 

35 

43 

38 

70 

43 

27 

42 

41 

37 

49 

33 

53 

46 

31 

60 

41 

40 

66 

40 

27 

40 

51 

40 

40 

41 

49 

57 

35 

39 

35 

36 

71 

40 

24 

38 

42 

33 

52 

32 

52 

50 

30 

53 

40 

37 

68 

40 

25 

41 

47 

40 

40 

44 

53 

60 

35 

31 

51 

40 

69 

46 

30 

46 

41 

41 

46 

35 

52 

45 

32 

63 

42 

41 

64 

39 

29 

42 

55 

40 

40 

28 

37 

40 

9 

9 

22 

34 

57 

44 

5 

16 

43 

10 

5 

9 

37 

30 

4 

32 

29 

18 

53 

2 

13 

35 

62 

30 

32 

43 

51 

57 

28 

23 

45 

49 

72 

44 

14 

42 

44 

32 

49 

34 

47 

53 

26 

63 

31 

36 

69 

37 

29 

37 

55 

38 

35 

Source: Authors' calculations based on EWCS2024 

46 

55 

64 

48 

45 

44 

33 

71 

42 

36 

52 

40 

45 

49 

41 

57 

51 

37 

64 

53 

50 

65 

45 

36 

57 

48 

44 

48 

45 

57 

58 

40 

41 

54 

45 

69 

44 

23 

43 

40 

32 

53 

38 

58 

39 

37 

66 

51 

36 

75 

43 

27 

50 

56 

35 

47 

42 

49 

59 

37 

33 

43 

37 

70 

45 

30 

43 

41 

39 

47 

34 

53 

48 

32 

54 

38 

46 

61 

39 

28 

43 

48 

43 

39 

40 

44 

58 

28 

31 

35 

32 

71 

37 

25 

35 

46 

41 

47 

23 

39 

55 

21 

51 

33 

21 

62 

35 

22 

26 

50 

37 

33 

115 

 
 
EPRS | European Parliamentary Research Service 

Figure C1 – AM exposure by economic activity (EU27, %) 

Financial services
Transport
Industry
Public administration
Other services
EU average
Health
Commerce & hospitality
Education
Construction
Agriculture

0

10

20

30

40

50

60

70

80

AM exposure (% of workers)

Source: Authors’ calculations based on EWCS2024. 

Figure C2 – AM exposure by occupational groups (EU27, %) 

Clerical support workers (ISCO4)
Technicians and associate professionals (ISCO3)
Managers (ISCO1)
Professionals (ISCO2)
Plant and machine operators, and assemblers (ISCO8)
EU average
Craft and related trades workers (ISCO7)
Service and sales workers (ISCO5)
Elementary occupations (ISCO9)
Skilled agri, forestry and fishery workers (ISCO6)

Source: Authors’ calculations based on EWCS2024. 

116 

0

10

20

30

40

50

60

AM exposure (% of workers)

 
 
 
 
 
 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

Table C3 – AM prevalence of employees' additional characteristics (EWCS2024) 

COUNTRY 

TOTAL 

UNION OR 
OSH 
REPRESENTAT
IVE 

PERMANENT 
CONTRACT 

FULLTIME 

MIGRANT 
BACKGROUND 

No 

Yes 

Yes 

No 

Yes 

No 

Migrant 

Native 

EU 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

42 

50 

59 

36 

35 

43 

38 

70 

43 

27 

42 

41 

37 

49 

33 

53 

46 

31 

60 

41 

40 

66 

40 

27 

40 

51 

40 

40 

32 

31 

48 

29 

28 

38 

31 

60 

35 

22 

30 

23 

23 

41 

29 

37 

34 

23 

53 

33 

26 

67 

29 

20 

36 

38 

30 

25 

47 

57 

62 

42 

41 

48 

40 

72 

47 

32 

49 

45 

43 

56 

38 

57 

53 

38 

61 

44 

44 

66 

44 

37 

46 

52 

47 

46 

44 

53 

61 

37 

36 

43 

38 

71 

45 

29 

44 

42 

40 

48 

34 

55 

50 

33 

61 

42 

45 

68 

42 

27 

42 

52 

43 

40 

35 

25 

47 

22 

33 

50 

39 

63 

23 

24 

31 

42 

24 

53 

29 

42 

39 

15 

36 

30 

14 

56 

28 

25 

50 

42 

25 

43 

44 

54 

61 

36 

34 

43 

41 

72 

45 

29 

43 

43 

39 

50 

33 

57 

51 

32 

61 

42 

41 

66 

41 

27 

41 

52 

42 

41 

34 

31 

50 

9 

44 

50 

31 

59 

22 

9 

31 

30 

22 

27 

25 

35 

34 

8 

51 

33 

23 

65 

22 

30 

49 

35 

23 

34 

41 

47 

51 

64 

32 

41 

41 

77 

46 

12 

32 

46 

29 

45 

71 

57 

45 

24 

61 

35 

27 

58 

43 

28 

n.a. 

55 

32 

38 

Source: Authors' calculations based on EWCS2024. 

43 

52 

62 

35 

36 

44 

37 

68 

43 

28 

45 

41 

39 

49 

33 

49 

48 

31 

50 

42 

44 

68 

39 

27 

42 

50 

42 

40 

117 

 
 
 
EPRS | European Parliamentary Research Service 

ANNEX  D  –  CONTACTS 
STRUCTURE 

We contacted and interviewed: 

INTERVIEWED  AND 

INTERVIEWS' 

•  President of CEC (Confédération Européenne des Cadres)  

•  Vice President, Global Chief AI Governance Officer at Wipro and AI ethics expert 

•  Responsible for AI, industriAll Europe 

•  Vice-President of EESC's Section on Employment, Social Affairs and 

Citizenship (SOC) and Rapporteur of SOC/803 opinion on 'Pro-worker AI' 

•  NYU School of Professional Studies, teaching Digital Workplace Design and Design thinking 

for HR 

The  interview  structure  is  informed  by  the  desk  analysis,  focusing  on  recently  proposed  or 
implemented AI/AM tools, emerging impacts of AI/AM on employment relationships, employment 
structure, changing demands, labour shortages, economic performance, working conditions, health 
and safety, and fundamental rights, insights into social inclusion implications of AI/AM technologies 
in workplaces. The scheme we used for the interviews is based on these queries:  

I. Validation of desk review and general perceptions 

1. Based on your understanding, do our initial findings on the broad impacts of AI/AM (e.g., 
on  productivity,  working  hours,  health,  safety,  and  fundamental  rights)  align  with  your 
observations  from  your  sector/area  of  expertise?  Could  you  elaborate  on  any  points  of 
agreement or divergence? 

2.  From  your  perspective,  what  are  the  most  significant  positive  developments  or 
opportunities arising from the introduction of AI/AM in workplaces today? 

3. Conversely, what are the most pressing concerns or challenges you are currently observing 
related to AI/AM adoption? 

II. Emerging AI/AM tools and their real-world application 

4. Are there any recently proposed, piloted, or implemented AI/AM tools or systems that you 
believe are particularly impactful, either positively or negatively, that our desk review might 
not have fully captured? Can you describe them and their intended/actual effects? 

5. How do these newer tools differ from earlier generations of automation in terms of their 
implications for workers and management? 

III. Specific impact areas 

A. Employment relationships & structure 

118 

 
 
 
 
 
Digitalisation, artificial intelligence and algorithmic management in the workplace: Shaping the future of 
work 

6.  How  is  AI/AM  altering  the  nature  of  the  employment  contract  or  common  work 
arrangements  (e.g.,  remote  work,  platform  work,  hybrid  models)?  Are  you  seeing  shifts  in 
legal or contractual terms? 

7.  What  impact  is  AI/AM  having  on  collective  bargaining  processes  or  the  role  of  social 
dialogue? Are new areas for negotiation emerging? 

8.  How  is  AI/AM  influencing  the  required  workforce  skillset?  Are  you  observing  new  skill 
shortages or a change in demand for certain roles? 

9. Are there specific job roles or sectors where AI/AM is leading to significant labour shortages 
or, conversely, to a reduction in demand for human labour? 

B. Working conditions and health and safety 

10.  In  what  ways  is  AI/AM  affecting  the  organisation  of  work  within  companies  (e.g., 
decisionmaking processes, team structures, supervision)? 

11. How is AI/AM influencing working time (e.g., scheduling, flexibility, monitoring)? Are you 
seeing changes in work-life balance as a result? 

12. What are the key implications of AI/AM for training and skill development needs for both 
managers and employees? 

13. Regarding health and safety, what concerns do you have about the potential for AI/AM to 
increase  work  intensity  or  contribute  to  mental  health  problems  (e.g.,  stress,  burnout, 
surveillance anxiety)? 

C. Economic performance and fundamental rights 

14. From an economic perspective, how is AI/AM impacting productivity and overall economic 
performance in organisations you observe? Are the benefits widely distributed? 

15. How are fundamental rights (e.g., privacy, non-discrimination, human dignity, freedom of 
association)  being  protected  or  challenged  in  workplaces  using  AI/AM?  Can  you  provide 
examples? 

16.  In  what  ways  can  AI/AM  technologies  lead  to  or  exacerbate  bias  and  discrimination  in 
hiring, performance management, or other workplace decisions? How are these issues being 
addressed? 

IV. Social inclusion and unforeseen implications 

17. What are the key social inclusion implications of AI/AM introduction in workplaces? Are 
certain  demographic  groups  or  types  of  workers  disproportionately  affected  (positively  or 
negatively)? 

18.  Are  there  any  unforeseen  challenges  or  opportunities  related  to  AI/AM  that  our  desk 
review or these questions haven't addressed, which you believe are critical for policymakers 
to consider? 

V. Future outlook and policy implications 

119 

 
 
 
 
 
 
 
 
 
 
 
 
 
EPRS | European Parliamentary Research Service 

19.  Looking  ahead,  what  do  you  see  as  the  most  critical  areas  for  policy  intervention  or 
regulatory oversight regarding AI/AM in the workplace? 

20. What role do you believe your organisation/sector should play in shaping the future of 
AI/AM to ensure a fair and beneficial transition for all? 

120 

 
 
 
 
Q
A
-
0
1
-
2
5
-
2
2
6
-
E
N
-
N

This  study  investigates  the  integration  and  effects  of 
artificial intelligence and algorithmic management (AM) 
technologies  within  European  workplaces,  extending 
beyond  the  realm  of  platform  work.  It  thoroughly 
examines  the  European  regulatory  framework  for  the 
in  stakeholder 
use  of  AM  technologies,  engages 
interviews,  and  analyses  case  studies  within  the 
logistics,  healthcare, 
telecoms,  automotive  and 
manufacturing sectors, revealing notable gaps in worker 
protection and regulatory oversight.  

The  study  estimates  that  exposure  to  AM  could  rise 
from 42.3 % to 55.5 % in the medium term, generating 
productivity  opportunities  but  also  challenges 
in 
employment relations, working conditions and workers' 
wellbeing. It identifies key regulatory gaps in AM use at 
work,  and  explores  the  role  EU  legislation  could  play 
both  in  addressing  the  gaps  and  in  guaranteeing 
responsible  use  of  AM  in  the  workplace.  It  develops 
three  policy  options:  adopting  a  recommendation; 
amending  a  package  of  existing 
legislation;  and 
adopting  a  new  legislative  instrument  to  regulate  the 
use of AM.  

The added value of acting at EU level has been identified 
in ensuring the same level of protection for all workers, 
improving  working  conditions  and  safeguarding 
fundamental  rights.  For  companies,  having  a  common 
set  of  rules  would  enhance  the  level  playing  field  and 
increase legal certainty. 

This is a publication of the European Added Value Unit 
EPRS | European Parliamentary Research Service 

This document is prepared for, and addressed to, the Members and staff of the European 
Parliament as background material to assist them in their parliamentary work. The content of the 
document is the sole responsibility of its author(s) and any opinions expressed herein should not 
be taken to represent an official position of the Parliament. 

PDF  

ISBN 978-92-848-3082-4 | doi: 10.2861/0136788| QA-01-25-226-EN-N