June 2021

Advanced Technologies
for Industry – Policy brief
Collaborative robots, human-AI systems and the role for
policy
 Policy brief – Collaborative robots, human-AI systems and the role for policy

This report was prepared by Kincsö Izsak and Palina Shauchuk from Technopolis Group.

EUROPEAN COMMISSION
European Innovation Council and SMEs Executive Agency (EISMEA)
Unit I-02.2 SMP / COSME Pillar
E-mail: EISMEA-SMP-COSME-ENQUIRIES@ec.europa.eu

Directorate General for Internal Market, Industry, Entrepreneurship and SMEs

Unit D.2 — Industrial forum, alliances, clusters
E-mail: GROW-ATI@ec.europa.eu

European Commission
B-1049 Brussels

LEGAL NOTICE
The information and views set out in this report are those of the author(s) and do not necessarily reflect the official
opinion of EISMEA or of the Commission. Neither EISMEA, nor the Commission can guarantee the accuracy of the
data included in this study. Neither EISMEA, nor the Commission or any person acting on their behalf may be held
responsible for the use, which may be made of the information contained therein.
More information on the European Union is available on the Internet (http://www.europa.eu).

PDF ISBN 978-92-9460-794-2 doi:10.2826/335679 EA-01-21-136-EN-N

Luxembourg: Publications Office of the European Union, 2021

© European Union, 2021

June 2021
 Policy brief – Collaborative robots, human-AI systems and the role for policy

Table of contents

Background.......................................................................................................................... 4
Section 1 .............................................................................................................................. 5
1. Impact of automation on industry and the workplace ................................................. 5
1.1 Human-robot and human-AI systems for higher industrial competitiveness.......................... 5
1.2 Impact of automation on work ....................................................................................... 6
1.3 Skills challenge ........................................................................................................... 8
1.4 Key policy challenges ................................................................................................... 8
Section 2 .............................................................................................................................11
2. Policy measures and initiatives..................................................................................11
2.1 European policy framework ..................................................................................... 11
2.2 National policies..................................................................................................... 13
Section 3 .............................................................................................................................16
3. Policy considerations .................................................................................................16
Bibliography .......................................................................................................................17
About the ‘Advanced Technologies for Industry’ project .....................................................19

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

Section

Background
This Policy Brief has been developed in the As highlighted by the Harvard Business Review
framework of the Advanced Technologies for (2020), companies that automate their operations
Industry (ATI) project, initiated by the European will see productivity gains only if they put in place
Commission’s Directorate-General for Internal mechanisms to enhance collaborative intelligence.
Market, Industry, Entrepreneurship and SMEs (DG Humans, AI and machines thus need to work
GROW), and the European Innovation Council and together, which demands better understanding of
Small and Medium-sized Enterprises Executive how these new systems operate and better skills
Agency (EISMEA). Policy Briefs analyse national to manage them.
and regional policy measures focused on a specific
The Covid-19 pandemic has spurred interest in
challenge, technological area or mode of
digital industrial operations and business models.
implementation, and they explore policy tools
Workers and employees were forced to rely on
designed and implemented with the aim of
digital tools and interact with machines much
fostering the generation and uptake of advanced
more than before the crisis.
technologies. The reports provide a comparative
analysis and bring examples of relevant national In this context, the specific objectives of this
and regional policy measures in the EU. analysis have been to:
This report focuses on analysing the consequences • Discuss current trends in human-machine
of artificial intelligence (AI) and robotics-based collaboration and map the different challenges
automation on industry and work, the related related to these new complex systems
human-machine systems and human-computer
interaction, and the need for policy to support a • Identify policy approaches and measures
positive transition and mitigate the potential risks. deployed to foster a beneficial shift towards
human-controlled automation and mitigate
Automation (mechanical or virtual) is a process or the related risks
task performed by software or a machine.
Automation comes in many shapes and differs • Explore any policy gaps in support of
with respect to its degree of flexibility and the technological transformation and provide
functional autonomy with which it performs a inspiration for policy action
single task or a variety of tasks. Industrial The report is made up of three parts. The first
automation uses robots and other automated tools section identifies the key policy challenges. The
in different industrial settings (e.g. 3D printing, second section analyses policy responses,
machines for hazardous environments). Business strategies and policy initiatives at EU and national
process automation is in charge of basic non- levels. The third section offers a summary of the
manufacturing activities (i.e. software tools for findings and policy considerations.
human resources, accounts and contract
management). Robotic process automation is This study is based on a comprehensive literature
used in higher-level automation, where software review, interviews and expert assessment.
is used in certain circumstances to augment
process performance and carry out complex tasks.

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

Section 1

1. Impact of automation on industry and the workplace

Key messages
Artificial intelligence, or AI as it is widely known, robotics and other related advanced technologies are
undoubtedly transforming our industries. Collaborative robots and AI will play an important role in
increasing industrial competitiveness as they enable manufacturers to produce better products in a safer
setting, and to do so more cost-effectively.
A wide range of studies have tried to predict to what extent industrial automation would replace jobs
and have an impact on employment in general. Some more recent research now suggests more
optimistic outcomes in favour of job creation. Although the exact long-term affects cannot be fully
foreseen, currently there are no substantial negative effects on occupations observed, according to the
latest evidence. Human-machine systems can actually create new opportunities that increase the
demand for new forms of skilled professionals.
Besides the impact of AI and robotics on jobs, the work-related and broader societal effects should also
be considered. Human-robot and human-AI interactions can have a negative impact on human
relationships and affect worker safety, but they can also create psychosocial stress. Change
management and organisational innovation that can address these impacts will be key to motivating
people to adopt a new mindset and behaviour regarding these digital developments.
Policy actors will need to run more foresight exercises to better assess the repercussions of how AI and
robotics can be integrated into the workflow. Policymakers will need to tackle questions around
occupational safety, health, liability and ethics. They will also need to incentivise quality aspects
through better data collection and data management.
Policy debates should not only discuss AI and robotics from a technological perspective but also how
collaborative and intelligent systems can be created that address industrial as well as broader societal
goals that combine the power of humans and machines working together to create new ‘human value’.
1.1 Human-robot and human-AI systems for Instead, there is a need to think in terms of
higher industrial competitiveness human-machine or human-computer systems
where the two collaborate.
Artificial intelligence, robotics and other related
advanced technologies are transforming all
sectors of the economy and have a profound Box 1: Cobots (collaborative robots) in practice
impact on industry, the workplace and on our Collaborative robots equipped with a vision camera enable
society. The analysis of the ATI business survey1 the sub-assembly of automotive engines. They can support
highlighted that while robotics technologies were the inspection of components and increase the accuracy of
initially deployed in manufacturing, new outcomes where manual inspection is subject to errors. The
applications are emerging, and multiple novel use robot operates next to employees on the line and creates a
cases proliferate. Robotics is used for a wide human-machine system, increasing efficiency and quality
variety of tasks, from shop floor production control.
automation to warehouse inventory management,
but it also has great potential in the healthcare
sector. AI-related applications also encompass
today a mix of horizontal solutions and industry-
specific scenarios.
In our current context, attention is often put on
the technology, on the robotic application or AI-
software, while the importance of the human
component is less prominent. Putting aside all the
futuristic scenarios and looking at the actual
practice, robotics and algorithms cannot fully
Source:https://www.universal-robots.com/case-
operate without a human agent guiding and
stories/comprehensive-logistics/ picture from Freepik
interpreting these new digital/electronic tools.

1 please see ATI Watch on ‘Technology focus on artificial https://ati.ec.europa.eu/reports/technology-watch/technology-

intelligence’ available at focus-artificial-intelligence

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

In the factory, collaborative robots play an In the work of Smids, et al. (2020), the authors
increasingly important role and enable provide several examples of how robotisation can
manufacturers to produce more cost-effectively. affect the work environment. The possible
‘Cobotics’ involves direct collaboration between opportunities or threats of introducing robots in
workers and robots in a shared space, where the workplace are summarised in Table 1.
human capabilities are significantly enhanced by
Although automation has already replaced many
advanced machines. Applications include remote
human tasks (especially in finance) and will
collaboration, co-manipulation, or the worker
displace many other jobs over the coming years,
wearing an exoskeleton that increases his or her
it is also generating demand for different skills or
power, stamina or performance. In this new
changing the nature of tasks. An OECD (2018)5
interaction, while the machine is doing the
study on automation, skills use and training
repetitive tasks, the worker can focus on problem-
reveals that about 14% (66 million workers) of
solving. Cobots can be used for various tasks such
jobs in OECD countries are at high risk of
as waste-sorting, picking, heavy-lifting, loading,
replacement due to automation, while 32% of jobs
dirty or dangerous tasks. For instance, Amazon
have a 70%-plus risk of changing. The median job
has more than 200 000 mobile cobots working in
in the study has a 48% chance of being partly
its warehouse network that help speed up
automated. Jobs of junior-level workers are at the
delivery.
highest risk of automation, followed by senior-
In the area of cobotics, the latest technological level workers.
developments concern mostly vision and sensor
Table 1: Opportunities and threats of robots
systems and how they increase precision and
flexibility in given tasks. The effectiveness of
sensing technologies is crucial for worker safety as
well. They can assess if a person is getting too
close2 to a dangerous operation or activity.
In a similar way, human-computer interaction is
the backbone of successful AI transformation
projects. AI systems can play a beneficial role also
in addressing the Covid-19 pandemic, for instance
by modelling infection dynamics and socio-
economic impact, monitoring physical distancing
and supporting the development of vaccines3.
Artificial intelligence is based on data selected and
compiled by people. In many current applications,
AI is only useful if accompanied and interpreted by
a human who understands how it operates and
how to deal with the results. For example,
machine-learning can be effectively used for
streetlight automation planning. Nevertheless,
human experts are needed to make decisions in
certain contexts4, and human–AI interaction leads
to much better overall outcomes than letting
machine-learning models operate on their own.
An important question for the future is how we can
create interactive intelligent systems that address
our key societal goals and combine the power of
humans and machines to create new value.
1.2 Impact of automation on work
The rise of AI and/or robotics in automated factory Source: Smids, et al.(2020)
operations and business processes has created a A recent Cedefop analysis (2020) concludes that
lot of debate around the impact of automation on there is no need to paint a bleak picture of the
jobs. With the seminal work of Frey and Osborne impact of automation. The study findings show
(2013), many studies have cautioned that a range that five years on from the predictions of Frey and
of jobs could potentially disappear or diminish in Osborne (2013), there has been little evidence
relevance as a result of technological of substantial negative effects on the
transformation. occupations highlighted as fully

2 https://www.theengineer.co.uk/technical-qa-cobotics/ 5https://www.oecd-ilibrary.org/employment/automation-skills-
3 IEEE (2020) Statement Regarding the Ethical Implementation use-and-training_2e2f4eea-
of Artificial Intelligence Systems (AIS) for en;jsessionid=WkhPit5ivkJFuu68Dv1ee-4E.ip-10-240-5-122
Addressing the COVID-19 Pandemic
4 Nascimento et al. (2018)

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

automatable. They drew attention to the Although tasks that address clear objectives and
importance of careful monitoring but also warn complete predictable actions are more suitable for
against a ‘technological alarmism’ (McGuinness et machine-learning, it does not mean that all tasks
al., 2019), in the absence of strong evidence of requiring emotional intelligence and creativity will
negative effects. Similarly, a McKinsey study be out of AI’s reach, as can be seen in industrial
argues that the adoption of AI might not have design or customer-service chatbots. When AI-
the substantial effect on net employment systems and machine-learning become more cost-
that many feared. According to the average effective than humans on a task, profit-
global scenario “total full-time-equivalent- maximising managers may increasingly seek to
employment demand might remain flat, or even automate such tasks, with a view to increasing
that there could be a slightly negative net impact productivity and lowering prices, with an effect on
on jobs by 2030”6. the overall economy, shifting labour demand and
restructuring organisations and sectors9. AI could
It should be noted that automation generally does
increase labour productivity by up to 40% by 2035
not affect entire jobs but specific tasks (see Table
in developed countries compared to expected
3). AI plays a supportive role, empowering or
baseline levels10.
helping humans to perform better in handling
complex and critical situations that require Different countries and industry sectors will feel
judgement and creative thinking. AI-driven the impact of ‘job automatability’ more than
demand is expected to rise for the following types others. For example, jobs in Anglo-Saxon, Nordic
of jobs7, 8: countries and the Netherlands are expected to be
less affected than those in eastern and southern
• Developers of new AI technologies (e.g.
European countries11. The financial sector is
software and application developers, robotics expected to be largely impacted (e.g. financial
engineers, AI and machine-learning services where algorithms can lead to faster and
specialists) more efficient analysis), while the health sector
• Jobs engaging with AI technologies (e.g. data may be relatively less impacted due to a greater
analysts and scientists, e-commerce and reliance on social skills and the need for human
involvement12.
social media specialists)
• Roles for supervising AI technologies (process Table 3: Shares of employees in EU27 with high
automation risk in 2020 (occupations with a higher risk
automation experts, information security
than 10%), based on Cedefop
analysts)
• Roles leveraging human skills and facilitating Occupations
Subsistence farmworkers 18%
societal shifts that accompany new
Other manufacturing workers 18%
technologies (e.g. customer service workers, Handicraft & printing workers 18%
sales and marketing professionals, training Machine & plant operators 17%
and development, organisational development Assemblers 17%

specialists, innovation managers) Construction workers 16%

Trades workers 15%

Table 2: Examples of tasks and sectors impacted by AI Operators and assemblers 15%
and cobots Metal & machinery workers 15%
Technical labourers 13%
Tasks Sectors
Farmworkers and gardeners 13%
Professional services,
Data management Drivers & vehicle operators 13%
healthcare (e-health)
Cleaners and helpers 13%
Procurement management, data
Government, industry Other elementary workers 12%
processing, information retrieval
Farm and related workers 12%
Accounting Financial services
Elementary workers 12%
Professional services,
Telemarketing
telecom and media Agricultural labourers 12%

Radiology Healthcare Electroengineering workers 11%

Healthcare (patient Protection workers 10%
Administration
administration) Food preparation helpers 10%
Harvesting Agriculture 0% 5% 10% 15% 20%
Automotive,
Assembly Percentage
manufacturing
Utilities,
Waste collection Source: Cedefop, Skills Panorama available at
manufacturing
https://skillspanorama.cedefop.europa.eu/en/dashboar
Source: Technopolis Group
d/automation-risk-occupations#2

6 https://www.mckinsey.com/featured-insights/artificial- 9 Ibid
intelligence/notes-from-the-ai-frontier-modeling-the- 10 OECD (2019). Artificial Intelligence in Society
impact-of-ai-on-the-world-economy 11 OECD (2020)
7 Caruso, L. (2018) 12 https://www.pwc.co.uk/services/economics/insights/the-
8 World Economic Forum. (2018) impact-of-automation-on-jobs.html

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

1.3 Skills challenge are fully automating specific work tasks and
expect that employees will ‘upskill’ on the job or
To reap the full benefits of automation, AI and
in their own time.
robotics, businesses will need to make substantial
organisational changes. At the same time, they The Covid-19 pandemic has demonstrated a more
will have to address skill shifts and provide urgent need for reskilling and upskilling. Covid-19
continuous learning options. The introduction of sped up the adoption of fully digitised approaches
automation means human resources staff need to such as in-person learning through live video and
be prepared; resource planning, recruitment, social sharing. To make sure that organisations
selection and retention, learning and thrive after the pandemic, new strategies are
development, remuneration and benefits systems needed to consolidate this progress18. With
and career planning functions13. In addition, they employee engagement levels on the decline during
may need to adapt organisation structures, work the crisis – and the corresponding impact on
processes and systems. morale, wellbeing and productivity – supporting
personal growth is going to be a key driver of
Research conducted by McKinsey suggests that
overall engagement and thus better performance.
the need for advanced technological skills (e.g.
programming) is expected to grow rapidly, as well 1.4 Key policy challenges
as social, emotional and higher cognitive skills,
Human-robot and human-AI systems are
such as creativity, critical thinking and complex
expected to play a key role in the future of work.
information processing. By 2030, demand for
In order to avoid conflicts between robots and
social and emotional skills is expected to grow
humans, the rules of interaction should be clearly
across all industries by 22% in Europe, while
defined. Beyond the clear benefits to perform
demand for entrepreneurship and initiative taking
dangerous tasks or process big data, these
will rise by 32%. However, demand for physical
technologies can potentially undermine
and manual skills, as expected, will decline (11-
occupational safety and the health of workers.
16% overall) but still remain the biggest category
Flexibility, reliability and autonomy allowed in
of workforce skills in many countries in the coming
cobotics remain a challenge19. The environment
decade14. For instance, basic data-input and
ideally fitted for a robot is usually different than a
processing skills will drop by 19-23% for all
workplace better suited to human workers20.
sectors for the same period, as machines
increasingly take over data-entry tasks15. Weiss et al. offer a theoretical and methodological
framework for managing digital transformation
The World Economic Forum predicts that nearly
within organisations, including task allocation and
50% of all employees will require significant
reskilling, to ensure that automation benefits both
reskilling and upskilling in the coming years16. It
employers and employees, and is thus accepted
is estimated that 35% of workers will need to
by the workforce. USUS models developed by the
obtain additional training of up to six months, 9%
authors help to evaluate human-robot
will require reskilling lasting between six and 12
collaboration considering usability, social
months, while 10% are expected to require
acceptance, user experience and societal impact21.
additional training of more than a year. Two-thirds
Such models and decision-making support can
(66%) of respondents to a McKinsey study listed
also mitigate the negative impacts on workers’
their top-ten priority is to address automation-
conditions and well-being.
related skills gaps (see Figure 2)17.
Successful technological transformation which
The Global Human Capital Trends survey (2021)
harnesses the power of human-robot/AI
reveals significant future demand for human skills,
interactions faces various challenges that can be
such as complex problem-solving (63%),
addressed both by policy and change in industrial
cognitive abilities (55%), social skills (52%)
behaviour. Key challenges include:
and process skills (54%). A shortage of
technical skills is another major concern for over • Data and technology
60% of respondents. • Qualifications and skills
Businesses are adopting different strategies to • Change management and organisation
address the growing shift in skills demanded • Safety and occupational health
thanks to automation and AI. For instance, some • Liability
companies are hiring new temporary staff with • Ethical behaviour
relevant skills in new technologies, while others

13 Nankervis, A. et al. (2019) Occupational Health and Safety and the Quality
14 McKinsey (2018) Requirements
15 Ibid 20 Royakkers, L., & van Est, R. (2015). A literature review
16 WEF (2018) Future of Jobs on new robotics: automation from love to war.
17 McKinsey (2020) International journal of social robotics, 7(5), 549-570
18 McKinsey, 2020 21 Weiss, A., Bernhaupt, R., Lankes, M., and Tscheligi, M.
19 See also Pauliková, A.; Gyurák Babel’ová, Z.; Ubárová, (2009). The USUS evaluation framework for human-robot
M. (2021). Analysis of the Impact of Human-Cobot interaction
Collaborative Manufacturing Implementation on the

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

Collaborative robotics and AI as part of mishaps. The regulatory framework needs to fit
Industry 5.0 strategies human-machine systems and new ways of
working.
Numerous examples and use cases demonstrate
the positive impact of using AI and machines to The reasons behind an AI-based system’s errors
augment human capabilities. Despite this, the are difficult to verify since they continuously learn
uptake of robotics is still low. According to the and their new decisions cannot always be traced
latest Eurostat statistics (2020), 5% of enterprises back. Failures can be related both to the
used industrial robots in the EU27. The ATI programmer feeding AI with biased training data
business survey found that 24% of organisations but also failures in terms of cybersecurity22.
in the sample used robotics technology (broadly
Skills needs beyond technology
defined) in 2020. There is also still much to do not
just to foster more adoption but also to encourage The skills challenge has already been highlighted
the responsible use of robotics and AI across above, but its policy implications have to be
industries. In this sense, the thinking in reiterated. To meet the requirements of human-
collaborative robotics and AI should be integrated machine systems, industry will need more workers
into the Industry 5.0 discussions. with specialised skills on how to interact with
robots. As AI and robotics are introduced in
Industry 5.0 reflects the shift towards making
workplaces, they lead to a change in the mix of
industries more future-proof, resilient, sustainable
occupations as well as skills and educational
and human-centred. It is understood as a forward-
requirements. To ensure the effectiveness of
looking exercise by looking at emerging societal
humans working alongside machines, the work will
trends and developing innovative technologies in
need to be redesigned and retooled. Several skills
a human-centric way. Industry 5.0 is about
including technological as well as social and
empowering employees and opening up new
emotional capabilities will increase, while demand
horizons by working with advanced technologies.
for physical and manual skills is expected to
Related to the shift to Industry 5.0 models, drop23. Firms will need to put in place new types
governance structures will need to be adjusted as of training programmes that balance motivational
well. Collaborative AI and robotics not only aspects (sense of purpose) with overall efficiency
concern industry; they also have an important gains from robotisation of industrial production24.
impact on employees, citizens and our society as
Enhancing the skills of women and increasing their
a whole. Future employees will need to be involved
participation in AI/robotics development should be
in the design of AI technologies or robotics
a priority to reduce gender bias. The World
solutions in order to limit and control any potential
Economic Forum’s Global Gender Gap Report
bias, misfit or discrimination. The modalities of
conducted in 2019 showed that only 22% of AI
governance will need to be elaborated; how a
professionals are female, which can have a huge
broader set of stakeholders can participate in
impact on the design of algorithms.
aspects such as data management, technology
design and the actual use case. In this sense, a Impact on health and safety
human-centred design approach needs to be part
of the process. The potential negative consequences of AI and
robotics applications are manifold including stress,
Managing trustworthy data discrimination, musculoskeletal disorders, but also
the possibilities of work intensification and
AI and robotics-based automation relies on large
psychosocial risks. AI can amplify various
volumes of data. The quality of the original
occupational safety and health risks, although it is
datasets are crucial to ensure that these new
not the technology itself but the way human-
systems operate effectively. Data should also be
AI/robotics interactions are set up and
balanced against privacy, transparency,
implemented that cause negative or positive
accessibility and security. This in turn needs
consequences25. There is also much work still
adequate data protection practices, data
needed to develop optimal ergonomics in terms of
management, safe data sharing and cybersecurity
the physical constraints on operators (e.g. in using
measures.
exoskeletons). There are also potential negative
Liability consequences on mental health.
The use of collaborative robots and AI systems The EU-OSHA report, ‘Foresight on new and
raises legal issues around liability and emerging occupational safety and health risks
responsibility which need to be considered. It associated with digitalisation by 2025’, identifies
should be clarified who is legally responsible to pay
for losses or compensation in the event of

22 European Parliament, 2019 on the Occupational Health and Safety and the Quality
23 https://www.mckinsey.com/featured-insights/future-of- Requirements
work/ai-automation-and-the-future-of-work-ten-things-to- 25 https://osha.europa.eu/en/publications/osh-and-future-

solve-for work-benefits-and-risks-artificial-intelligence-tools-
24 Pauliková, A. et al. (2021) Analysis of the Impact of workplaces
Human–Cobot Collaborative Manufacturing Implementation

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

the following associated risks and challenges for technologies to properly take account of the
occupational health and safety26: human aspects.
• The potential for automation to remove Ethics
humans from hazardous environments, but
Ethical behaviour will be important to ensuring
also to introduce new risks, particularly
that our society as a whole can mitigate the
influenced by the transparency of the
negative consequences of AI including bias or
underlying algorithms and by human-machine
discrimination. It is important that technology
interfaces.
designers take more responsibility, guided by law
• Psychosocial and organisational factors that
and policy initiatives, and act in an ethical and
will become increasingly more important
responsible way. Human-computer interaction
because ICT-enabled technologies can drive
needs to be repurposed to engage with legal and
changes in the types of work available; the
regulatory aspects of a system including
pace of work; how, where and when it is done;
participatory design27. For instance, AI algorithms
and how it is managed and overseen.
can be effectively applied in human resources
• Increasing work-related stress, particularly as
management and in the recruitment of new staff.
a result of the impact of increased worker
Nevertheless, built-in biases can create issues
monitoring made possible by advances in and
such as favouring a certain race, gender or
the increasing presence of wearable
characteristics. As a result, the actual decisions
technologies, 24/7 availability, blurred
taken might exclude certain groups of people from
boundaries between work and private life, and
the labour market, moreover lead to less
the online platform economy.
innovation (if real talent is not recognised).
• Risks associated with new human-machine
Training data that includes information about a
interfaces, in particular related to ergonomics
broad group of candidates are essential. AI
and cognitive load.
algorithms should be carefully trained and the
• Cybersecurity risks due to increased
decision-maker fully acquainted with how AI
interconnectedness between things and
actually works.
people.
Collaboration between academics, industry, social
partners and governments on research and
innovation will be key in the development of

26EU-OSHA. (2018). Foresight on new and emerging publications/publications/foresight-new-and-emerging-

occupational safety and health risks associated with occupational-safety-and-health-risks/view
digitalisation by 2025, Luxembourg: Publications Office of 27
Urquhart and Rodden (2016)
the European Union. Retrieved June, 2020 from
osha.europa.eu: https://osha.europa.eu/en/tools-and-

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

Section 2

2. Policy measures and initiatives

Key messages
At national level, various EU Member States have an AI strategy and programmes to foster technological
development including robotics or the uptake of cobotics solutions. There are only a few examples
where policy measures are in place to tackle the related risks, incentivise ethical behaviour
and systematically monitor the impact of human-machine systems on work. Initiatives focus
much more on the technological challenge and less on the organisational and ethical aspects that such
transformation and new collaborative AI or robotics will require. Despite national AI strategies launched
in most countries, few of them discuss the implications of AI on ethical behaviour and with regard to
the labour market. Nevertheless, we find various research projects financed at national level that aim
at assessing the longer-term impact of robotics or AI.
The European Union has been particularly active in setting new requirements towards AI
technology-based solutions. More needs to be done in terms of integrated thinking where AI and
robotics are not regarded separately but in the context of human-machine systems where technology,
human intelligence and creativity work together in harmony.

2.1 European policy framework policy examples from international, European and
national levels.
Research and innovation policies across Europe
actively promote the development of AI and Human wisdom is being challenged on a range of
robotics technologies. Yet, the understanding of issues that AI raises today, including privacy,
how workers and robots, employees and AI fairness, safety, democracy and sustainability.
software can function alongside one another is still
The European Union has been particularly active
at an early stage. Beyond unleashing the potential
in setting new requirements towards AI-based
in these technologies, policy will need to foster a
technologies and solutions. Most recently, in April
positive technological transformation and, at the
2021, the European Commission launched a
same time, play an important role in mitigating the
proposal for a regulation laying down harmonised
less beneficial (and potentially harmful) societal
rules on AI28. The objective is to turn Europe into
(or even health) consequences of automation and
the global hub for trustworthy AI. The proposal
human-machine interactions. This section brings
strives to balance the numerous risks and benefits

Figure 1: Whitepaper on AI – key points

Source: European Commission, 2020

28Please see
https://ec.europa.eu/commission/presscorner/detail/en/ip_
21_1682

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

the use of AI can provide. The Regulation on AI safety, human oversight, well-being and
pursues four objectives: accountability will need to be respected.
• To ensure that AI systems placed on the EU Also relevant for the ethical perspective is a report
market are safe and in line with existing EU published in 2019 by the EU Agency for
law on fundamental rights and values; Fundamental Rights on Data Quality and Artificial
• To ensure legal certainty when facilitating Intelligence on mitigating bias and error to protect
investment in and innovation into AI; fundamental rights. The report stresses the
importance of being aware of the flaws of data
• To enhance governance and effective used to train AI algorithms and to mitigate
enforcement of the existing law on
potential biases, including gender bias, that
fundamental rights and safety requirements
undermine the principle of non-discrimination.
applicable to AI systems;
Standardisation of robotics
• To facilitate the development of a single
market for lawful, safe and trustworthy AI The European Parliament adopted a resolution on
applications and to prevent market 16 February 2017 with recommendations to the
fragmentation. Commission on civil law rules on robotics. The
main assumptions are the human right to privacy,
The history of AI legislation goes back to 2018,
when under the supervision of Mariya Gabriel, respect for human frailty, transparency in the
former Commissioner for Digital Economy and programming of robotic systems, and the need for
Society, the High-Level Expert Group on Artificial predictability of robotic behaviour. The resolution
Intelligence (AI HLEG) was created to support the clarifies the definition of ‘smart autonomous
implementation of a European approach to AI and robots’ and recommends an ethical framework.
robotics. This included the elaboration of The International Organisation for Standardisation
recommendations on future-oriented policy
(ISO), serving as the worldwide federation of
development and on ethical, legal and societal
national standards organisations, prepares
issues related to AI, including socio-economic
challenges. standards concerning robots through the ISO
Technical Committee 299 with the title ‘Robotics’.
The Guidelines propose seven key requirements It provides respectively guidelines for the design
that AI systems should meet in order to be and implementation of a collaborative workspace
trustworthy: human agency and oversight, that reduces risks to people, and it provides a
technical robustness and safety, privacy and data foundation for work in this area, since we expect
governance, transparency, diversity, non- to learn more as applications are deployed and
discrimination and fairness, societal and technology develops.
environmental well-being and accountability. For
It specifies the definitions, important
each of these key requirements, a practical
characteristics of safety control systems, factors
implementation guide has been produced.
to be considered in the design of collaborative
The second deliverable includes 33 practical robot systems, built-in safety-related systems and
recommendations on how to empower and protect their effective use and guidance on implementing
humans and society in the AI era, while creating the following collaborative techniques: safety-
multi-stakeholder alliances, which offer a tailored rated monitored stop; hand guiding; speed and
approach to capturing new technological separation monitoring; power and force limiting.
opportunities in the Single European Market.
Standardisation of AI
The European Commission published a White
The professional association Institute of Electrical
Paper and a Report on the safety and liability
and Electronics Engineers (IEEE) has launched a
aspects of AI29 in February 2020. The White
proposal aimed at technology experts and
Paper includes guidelines on how to adopt a
researchers to issue new standards for AI,
human-centred approach to AI, recognising
particularly by investigating and focusing on
that trust needs to be built in order for society to
ethics-related issues of autonomous and
take up AI applications. It also highlights the
intelligent systems. More specifically, 13
potential risks of certain AI applications and the
committees have been created to cluster insights
need to uphold the principles of non-
from technical and sociological experts from
discrimination, fairness and transparency. Privacy,
academia, industry, civil society, policy and
governments. The ‘Ethically Aligned Design’30

29https://ec.europa.eu/info/sites/default/files/commission- 30
https://standards.ieee.org/content/dam/ieee-
white-paper-artificial-intelligence-feb2020_en.pdf standards/standards/web/documents/other/ead_v2.pdf

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

includes concrete recommendations on “how to (UR), which demonstrate a thriving Danish

establish ethical and social implementations for robotics industry. The robotics cluster provides
intelligent and autonomous systems and members with opportunities to expand their
technologies, aligning them to defined values and network, accelerate growth and innovation, access
ethical principles that prioritise human well-being funding, find new collaboration partners and
in a given cultural context.”. increase visibility. Business support is provided to
In 2020, ISO and International Electrotechnical innovation and collaboration with knowledge
Commission (IEC) jointly established a institutions, but also for internationalisation.
standardisation committee on AI (the ISO/IEC JTC One project in the robotic cluster is putting
1/SC 42), with the aim of setting internationally in place a collaborative construction site
recognised common standards in the fields of big robot for assistive logistics tasks. This project
data (particularly by agreeing on definitions and focuses on exploring the possibilities for
architecture requirements) and AI (particularly by automation in the construction industry using
investigating trustworthiness). mobile robots to help transport tools and
materials. Construction relies on human-operated
2.2 National policies
tools and machinery, which makes it an
At national level, various EU Member States have underutilised sector in terms of robotic
an AI strategy and programmes to foster automation. Enabling a mobile robot to navigate a
technological development including robotics or construction site and help with these logistics
the uptake of cobotics solutions. There are only a tasks is the first step on the road to solving more
few examples where policy measures are in place complex tasks using robotics in construction. It
to tackle the related risks, incentivise ethical was carefully tested how existing mobile robots
behaviour and systematically monitor the impact can be adapted to perform a variety of logistics
of human-machine systems on work. Among the tasks around construction sites when equipped
examples identified, several Member States (e.g. with additional sensors and perception algorithms.
Denmark, Finland, France, Germany, the
Box 2: Cobot Knowledge Lab
Netherlands) have a programme to foster
skills development and adjust training and The Danish robot clusters foster skills development
education to the new needs of the future through the so-called ‘Cobot Knowledge Lab’. This lab
helps SMEs learn how to operate cobots. The project is
workplace. Despite national AI strategies
run by Odense Robotics in collaboration with Danish
launched in most EU countries, few of them Technological Institute, DIRA, the University of
discuss the implications of AI on ethical Southern Denmark, Aarhus University and Fraunhofer
behaviour and with regard to the labour IPA in Germany. The Industriens Fond has donated
market. Nevertheless, we find various almost DKK 6 million to the Cobot Knowledge Lab. The
nationally funded research projects assessing consortium brings together unique competences within
the longer-term impact of robotics or AI. technology, business, management, organisation,
‘human factors’ and ethics that can support Denmark’s
Ethical considerations of human-robot/AI leading position in the development and use of cobots.
systems and how they will influence work, The consortium behind the project is developing an
safety or society are often embedded in intensive course based on knowledge gained throughout
research projects conducted within innovation the project targeting managers and employees of
Danish manufacturing companies who are considering
ecosystems and clusters driving robotics or AI
implementing cobots.
developments.
Skills development in Denmark, Ireland
and the Netherlands
In Denmark, the Odense Robotics,
RoboCluster and UAS Denmark joined forces
in 2021 and now cover different parts of the
robotics value chain, integrating technologies,
related industries and different geographies.
Simon Kollerup, Denmark’s Minister of
Industry, Business and Financial Affairs,
Source: Odense Robot Cluster, picture from Freepik
unveiled two flagship projects, Mobile
Industrial Robots (MiR) and Universal Robots

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

The processing of sensor data and extraction of • Task-specific analysis of the effects of digital
high-level information about the surrounding change and the development of human-
environment, i.e. position of obstacles and centred guiding principles for working in a
humans, are vital for this application. digital world of work.
Skillnet Ireland is the national agency dedicated • Systematisation of new requirements for
to the promotion and facilitation of workforce technical and organisational occupational
learning in Ireland. Its mission is to facilitate safety.
increased participation in enterprise training and Putting ethical guidelines into practice,
workforce learning within SMEs31. France and Germany
The Cobotics Skillnet targets industries which The wider debate about ethical considerations
provide economic impact of scale, namely; around artificial intelligence is also pertinent at
fintech, logistics, healthcare, retail and national level. Various guidelines have been
manufacturing. The objective is to remove barriers published which raise similar questions such as the
to job creation, job retention and competitiveness EU White Paper on AI. Nevertheless, as the report
by boosting Irish ‘robotic density’. The initiative of the Bertelsmann Stiftung33 (2020) also
provides training to both industry and graduates stressed, the next urgent step is to put these
including new robotics apprenticeships. recommendations into practice. They draw
In the Netherlands, the cabinet has recently laid attention to the importance of integrating ethical
down the implementation of the Technology Pact criteria from the start when developing any AI
for 2021. It focuses on revised training needs at system.
all levels and includes an action plan for change. One of the suggested proposals on the table is the
The pact presents a long list of concrete and bold creation of an ethics label for AI systems34 to
actions to address the digital and technological be used by AI developers to communicate the
skills gap including training on AI and robotics. quality of their products. The advantage of such a
Optimisation of work systems, Germany label would be to inform the user how the system
The German Federal Institute for Occupational has been set up and if it follows the necessary
Health and Safety (BAuA)32 is a departmental ethical requirements.
research institution of the Federal Ministry of In France, the national AI strategy presented in
Labour and Social Affairs. The institute is leading 2019 discusses, in particular, the impact of AI on
a range of studies and projects in topics such as labour and working conditions. Prior to the
new technologies and new forms of work, human- strategy, the French National Commission for
machine interaction and digital ergonomics. Informatics and Freedoms (CNIL) conducted a
Deeper understanding of how technological public debate on algorithms and AI in 2017 with
change will affect the workplace is expected to the aim of reflecting on the social issues raised by
contribute to a better design of future work digital technologies 35. It raised questions such as
systems. Digital ergonomics for example is “how to deal with the new forms of responsibilities
understood as an “umbrella term for digital models that involve complex and highly segmented
and methods for planning, realisation and ongoing algorithmic systems”. Since algorithms allow the
improvement of products and socio-technical work delegation of increasingly critical tasks including
systems”. Successful human-machine interactions the machine reasoning, AI might be considered to
need the ability of the technology to adapt to be just and neutral, which might open the way to
individual preferences. excessive trust. There is a need for human control
With its interdisciplinary programme focused on mechanisms to avoid excessive depedency on, for
‘Occupational Safety & Health in the Digital World example, machine-generated suggestions for
of Work’, the objective is to contribute to human- critical decisions.
centred design of technological change. The Another practical contribution to the ethical
programme has three components: requirements debate is via the recently set up
• Systematic data monitoring of technological Artificial and Natural Intelligence Toulouse
change and its impact on working conditions. Institute (ANITI) in France, led by the University
of Toulouse. The new institute targets strategic

31 http://www.coboticsskillnet.ie/about/ 34 https://irights-lab.de/en/aiethicslabel/
32 https://www.baua.de 35https://www.cnil.fr/fr/comment-permettre-lhomme-de-
33 https://www.bertelsmann-stiftung.de/en/our-projects/ethics- garder-la-main-rapport-sur-les-enjeux-ethiques-des-
of-algorithms/project-news/from-principles-to-practice-how- algorithmes-et-de
can-we-make-ai-ethics-measurable

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

application sectors such as

Box 3: AI testing and quality
mobility/transportation and
robotics/cobotics with the aim of In Germany the Minister for Digital Strategy and Development
supporting industrial transformation. By of Hesse and VDE the Association for Electrical, Electronic and
bringing together 200 researchers from Information Technologies launched a plan in May 2021 to
establish a nationwide ‘AI Quality and Testing Hub ’. The
universities, research organisations and
aim of the initiative is to make the quality of AI systems
companies, ANITI is expected to develop a verifiable. At the same time, the ‘AI made in Hessen’
new generation of artificial intelligence trademark is also to be strengthened. Research and
called hybrid AI, combining data-driven development as well as standardisation and certification will
machine- learning techniques with be put under one roof.
symbolic and formal methods. Hybrid AI is The four pillars of the AI Quality & Testing Hub will be:
expected to provide better guarantees in
terms of reliability, robustness and the • Testing and checking: test and simulation
environments and testing laboratories will be put in place
ability to explain and interpret the results
including research and further development of
of the algorithms used, while ensuring traceability, robustness and security.
social acceptability and economic viability. • Legal framework for AI: AI applications are to be
The institute will also look into acceptability developed and tested under real conditions in order to
and fair representativeness of data for AI prepare them to work also in practice.
by integrating AI algorithms from social, • Competencies: this pillar includes the promotion of
economical, legal or ethical points of view. knowledge transfer between science and supervisory
authorities as well as the development of AI quality
The research to be conducted will also
competence among customers and operators in business
propose new ways of handling data to and administration.
address data bottlenecks and biases that • Change: the last pillar reflects the transformative
can hamper AI systems. character of AI and includes support for companies,
events and communication campaigns are also organised
on how to foster the quality of AI applications.

Source: VDE, Germany, picture from Freepik

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

Section 3

3. Policy considerations

The potential in AI, robotics and other related In the implementation of the latest national AI
digital technologies can unlock unprecedented strategies new action plans could be created
opportunities not just to increase industrial following some existing examples, as presented
competitiveness but also to address key societal above, to consider the impact of collaborative
challenges. In parallel with investing in AI/robotics systems on the workplace.
technological development and fostering industrial Governance models can be put in place not just to
uptake, policymakers and industrial stakeholders manage change but also to monitor the short-term
need to be aware of the risks and look for ways to and long-term effects.
support positive change. Realistic expectations
Although self-governance of tech companies (as
need to be created about new technologies being
stressed by the World Economic Forum) will be
implemented through collaborative human-
crucial to foster a positive industrial
machine interactions. Automated systems can
transformation, policy will need to carefully guide
carry out not only physical tasks but also a variety
the process and mitigate associated risks.
of cognitive tasks, such as assisting financial and
legal work or medical diagnoses. AI-based systems will empower various industries
including manufacturing, agriculture, healthcare
Beyond the advantages, human-machine systems
and services, but will also change the content of
can result in more sedentary work, less variation
these jobs and tasks to be performed. The new
of tasks, but also cognitive underload and other
challenges that arise in the area of occupational
forms of performance pressure. Risk factors such
safety and health (OSH) need to be considered
as isolation and lack of interaction with peers can
more broadly. Current research conducted by
have a negative impact on teamwork and potential
various OSH agencies should not stay at the level
psychosocial consequences.
of assessment, but should be integrated into AI
In this short review of policy challenges and and technological policies.
existing policy measures, the following
Skills development is high on the agenda both at
observations can be made to inspire further
European and national/local levels. The current
policymaking:
debate is very much centred around the
The assessment of socio-economic impact and technology challenge and embedding
ethical, human-centred practices should be technology/AI training into various curricula and
integrated throughout the technological value education. Nevertheless, a too narrow
chain and in the policymaking lifecycle. technological focus might misunderstand,
misstate or simply miss the potential in a
Firstly, right from the start technology experts and
collaborative AI/robotics setting. Skills policies
final users (including future employees) need to
could be ‘inspired’ to become more human-
work together and jointly design the AI algorithms
machine focused, where digital ergonomics and
or robotics solutions to ensure the results are fit-
ethical considerations are taken seriously in
for-purpose (‘fitness check’). Besides raising
parallel with enhancing technology knowledge.
awareness of the beneficial effects of collaborative
The future workforce should not only learn how to
development and issuing guidelines that foster
use technology, but also how to critically assess
such a practice, policymakers can also foster
the advantages/disadvantages, and how to make
human-centred design by integrating
positive decisions for the benefit of our society.
transparency requirements into AI-based
products.

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

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 Policy brief – Collaborative robots, human-AI systems and the role for policy

About the ‘Advanced Technologies for Industry’ project

The EU’s industrial policy strategy promotes the creation of a competitive European industry. In order
to properly support the implementation of policies and initiatives, a systematic monitoring of
technological trends and reliable, up-to-date data on advanced technologies is needed. To this end, the
Advanced Technologies for Industry (ATI) project has been set up. The project provides policymakers,
industry representatives and academia with:
• Statistical data on the production and use of advanced technologies including enabling
conditions such as skills, investment or entrepreneurship
• Analytical reports such as on technological trends, sectoral insights and products
• Analyses of policy measures and policy tools related to the uptake of advanced technologies
• Analysis of technological trends in competing economies such as in the US, China or Japan
• Access to technology centres and innovation hubs across EU countries

You can find more information about the 16 technologies here: https://ati.ec.europa.eu.
The project is undertaken on behalf of the European Commission, Directorate-General for Internal
Market, Industry, Entrepreneurship and SMEs and the European Innovation Council and SMEs Executive
Agency (EISMEA) by IDC, Technopolis Group, Capgemini, Fraunhofer, IDEA Consult and NESTA.

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 EA-01-21-136-EN-N
Policy brief – Collaborative robots, human-AI systems and the role for policy