MARIE SKŁODOWSKA-CURIE ACTIONS ARTIFICIAL INTELLIGENCE CLUSTER

Policy Notes

I. BackgroundArtificial Intelligence (AI) has been and remains on top of the European Commission’s agenda. Many initiatives have been launched to shape a European approach to AI and ensure that EU is ahead of technological developments and encourages uptake by the public and private sectors, prepares itself for socio-economic changes brought about by AI, while also establishing an appropriate ethical and legal framework. Moreover, Von Der Leyen Commission, through the mandate of its Vice-President Margrethe Vestager, has been entrusted with coordinating the work to further define the European approach on AI, including its human and ethical implications.

The Research Executive Agency (REA) manages a large portfolio of H2020 projects and one of its roles is to provide programme and policy feedback on the funded actions to the European Commission. REA’s four Marie Skłodowska-Curie Action (MSCA) units have joined forces with Directorate General Education, Youth, Sport and Culture (DG EAC) to organise a joint project cluster event on Artificial Intelligence (AI). The event aims at: (a) showcasing the contribution of MSCA projects to AI research and innovation; (b) promoting discussion and collect information to provide coordinated input to the relevant EU policy-making services; (c) enhancing synergies among projects and creating, or reinforcing networking opportunities, particularly for MSCA fellows.

MSCA has funded more than 7.000 projects in all domains of research and innovation. Over 300 projects deal with AI applications in various areas such as agriculture, architecture, cultural heritage, cyber-security, digital communication, digital Identity, disaster management, energy efficiency, environment, resources and sustainability, health, industrial technologies, intelligent robotics, cybernetics, Internet of Things, management of natural disasters, transport, etc.

For this joint cluster, three domains of AI applications have been selected for their relevance to European Commission policies, namely digital world, health and environment. Around 40 MSCA projects have been invited to take part in six panel discussions and poster sessions relevant for both scientific and policy aspects of AI. Topics to be addressed include robotics applications in learning and education, solutions for the cyber-protection of minors, smart devices and solutions for health, and environmental applications, including water and natural disasters. Moreover, one panel will bring together EC perspectives on ethics, education and security and discuss how EU could position itself as frontrunner in setting trustworthy and ethical by-design AI.

Policy officers from various European Commission services have shared their input on relevant policy questions and topics which will be discussed at the cluster event, in view of collecting feedback and contributions from the invited researchers. We are thankful to Directorates General for Education,

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Youth, Sport, and Culture (DG EAC), Research and Innovation (DG RTD), Migration and Home Affairs (DG HOME), Communications Networks, Content and Technology (DG CNECT), Justice and Consumers (DG JUST), Environment (DG ENV), Internal Market, Industry, Entrepreneurship and SMEs (DG GROW) and the Joint Research Centre (JRC) for their input.

II. Policy questionsBelow is a list of policy questions grouped per panel (AI applications in Digital domain, Health and Environment), as well as a series of general questions on AI, ethics of AI, AI and Education and on projects and fellows. These topics are expected to be addressed during the cluster event (panel discussions but not only), so your input would be highly appreciated.

1. General AI How to structure the AI community in Europe, in particular involving the scientific

community, to make Europe "the place to be" for AI experts, and make the best of such technology, for the benefit of our citizens, society, environment, and economy?

How do we build trust in AI so make sure it is not only perceived as a threat, but as an opportunity?

Do you expect we can make progress on the transparency /accountability of deep learning algorithms, and if not should we abandon them in favour of (potentially less efficient) AI methods producing results that are more explainable to citizens?

Do you see a trade-off between AI adoption and trust in public administrations? Is there a risk that the replacement of humans by AI agents for rationalization purposes will actually increase distrust from citizens, especially among less digital-literate people?

Across the different topics (projects) can we perceive or are we influenced by different AIs dimensions between European research and non-European research (e.g. the USA or China initiatives) or are we aiming at the same Eldorado?

Is European research a kaleidoscope of AIs national initiatives or are we already observing a European flavour?

What is the main hurdle for uptake of AI in Europe? How can EU citizens benefit from AI? How could AI be used in the context of EU Institutions? How can we provide a balanced assessment of challenges and opportunities offered by AI for

Law Enforcement in Europe? In which ways can AI help increase security of European citizens? What are possible side effects of AI technological solutions in the domain of security?

2. AI Digital Applications How do you deal with “black boxes” vs explainability: is it an important issue in your case,

how do you address it? What are the major S&T roadblocks in AI and robotics we should address in Europe – and on

which we should join forces? What is the importance of interdisciplinarity in your project – how could we facilitate this? How do you address acceptability if the use-cases you are addressing (some of them quite

sensitive)?

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What is the documentation that developers and deplorers generally already maintain in the process of setting up and using an AI application, either because this is needed to develop and use the application or because it is required by law (like for instance Data Protection Impact Assessments)

What is the cost of providing for the possibility that AI applications can be tested by third parties (input-output observations, so called black box testing). Since testing is a normal part of software development, would the obligation to allow 3rd party testing not be overly invasive in the design of the software?

Many discussions on AI regulation focus on automated decision making (ADM). Are there cases of automation that would be perceived as ADM but still raise concerns?

3. AI applications in HEALTH Which are the recent deployments of ML algorithms in healthcare? Which are the technical challenges in developing and deploying machine learning (ML)

algorithms in medicine? Are there new risks involved in using ML medicine e.g. for diagnosis or treatment of

patients? Deep learning – which are the pros and cons in medicine? “Locked” versus “adaptive” AI in medicine. Which are the risks and challenges? The non-binary medical decisions and challenges in ML predictions: which are the challenges

of developing such algorithms for these cases? Different forms of learning (supervised, unsupervised); and their impacts on designing ML

algorithms in healthcare: which are the pros and cons? ML medical systems: provider of services or medical products? What about the interaction between doctor/ML system/patient? Any risk on the

relationship? How has AI changed your specific health domain? How AI will transform your health domain in the next 3-5 years? What does your health domain needs to take full advantage of AI? What are the key skills needed to fully exploit AI? Which role has data/bias/labelling in health?

4. AI applications in Environment What are the policy needs at European level that will facilitate the development of Green AI

solutions? How can AI help to deliver on the European Green Deal? What are the policy needs at European level that will facilitate the development of green AI

solutions? What is the status of AI in advanced energy storage systems? What is the status of AI in energy-focused start-ups.

5. AI and Ethics: Are you aware of the Ethics Guidelines (see Reference Documents) for trustworthy AI by the

High-Level Expert Group on AI? o If so, have you considered the questions of the Trustworthy AI Assessment List (pilot

version), in order to operationalise Trustworthy AI requirements?o Are there other considerations you would like to see included, in order to determine

whether an AI application can be considered “trustworthy”?

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o How would it be possible to ensure that the list is tailored to specific use cases and the context in which the AI system operates?

Which concrete measure are you applying in order to avoid a biased outcome of the decision-making process of the AI systems you use?

o Which complications do you encounter in checking the data training set for potential bias?

o Is it possible to address potential bias in the algorithmic design of the AI system? If so, how and which practical issues do you encounter?

In how far would potential binding requirements for ethical standards negatively affect innovation possibilities?

o How could transparency requirements for developers to disclose the design parameters of AI systems, metadata of datasets used for training and on conducted audits look like and which effect would they have on innovation capacities?

o How could explainabiliy requirements look like to allow for users and citizens to understand how a automated decision-making system is reaching its decision? Is it possible at all to ensure explainability in this context? If only “explainable” AI was allowed to be used, how would this affect innovation, for example in the health sector?

o How could requirements for the design of algorithms to protect fundamental rights look like and which effect would they have on innovation capacities?

o How could requirements for data quality used to train AI look like and which effect would they have on innovation capacity?

One of the main policy recommendations (Recommendation 9) of the HLEG to the European Commission is to adopt a risk-based governance approach to AI. In this respect, how would you see a framework to operate to effectively decide, which AI systems are to be considered high-risk and which are not (in particular in the light of avoiding over-regulation)?

o For example, the German Data Ethics Commission proposed a risk-based approach in its report of October 2019 (link to the full report (in DE) here). Would this be a blueprint for a European approach?

How to ensure that entities across Europe can partake in the development of Trustworthy AI and what is Europe’s current direction when it comes to the ethics development and deployment of AI?

What are the main ethical issues do you see in your application / use-case? What are the ethical considerations that need to be further thought through in future

projects? What should be done from the policy perspective? Are there any empirical evidences on situations at risk for fundamental rights? Where does

AI or algorithms cause or increase a risk? Where AI or algorithms are a challenge for the effective enforcement of applicable

fundamental rights related EU law? Would the involved persons be able to foresee, prevent, or document the abovementioned

risks in the relevant situations? Are there any specific needs of authorities when it comes to fulfilling their mandate in

situations that involve AI and algorithms?

6. AI and Education Our European perspective on AI pays much more attention to the ethical aspects than other

main actors do. Can the EU still lead the AI innovation while making prevail our ethical principles, esp. in Education?

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Can the European policies promote an AI to help students in their personal and social development?

Can a European AI support the creation of capabilities through education relevant for wellbeing?

Can the EU policy action foster an ethical AI market to tackle special needs education (dyslexia, dyscalculia, attentional problems…)?

If AI systems are constantly to predict our next moves, human growth and development may be increasingly difficult. How can EU educational policies help our European students to become authentic and responsible agents in our societies, i.e. to empower human agency?

7. General questions for projects/fellows How do you see these projects contributing to make Europe attractive for talents in AI (e.g –

what is your concrete experience with European industrial doctorates?) What can we do to maximise exchanges of information between the programmes and

projects? How can we help you maximising the impact of your projects – what can be done for re-use

of your research results by other researchers – to continuously improve the technology How do you ensure exploitation of your research results by industry? Are the grantees planning to stay in Europe and continue their research either in the private

sector or in university (academic career)? If not, why? If they do not know which framework conditions would be needed?

What additional skills and competences (if any) are the fellows missing to continue their research in this field? Or collaboration with other scientific fields?

How do you maximise the visibility of your project? How do you explain the added value? How do you address the concerns/fears?

III. Reference documentsThe following EU publications can be used to further support the discussions

Questions? Contact us REA-MSCA-CLUSTER@ec.europa.eu

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