On 19 February 2020, the European Commission published a White Paper 'On Artificial Intelligence: A European approach to excellence and trust'.

The purpose of this White Paper on artificial intelligence (AI) is to discuss policy options on how to achieve two objectives: promoting the uptake of AI and addressing the risks associated with certain uses of AI. Europe aspires to become a 'global leader in innovation in the data economy and its applications', and would like to develop an AI ecosystem that brings the benefits of that technology to citizens, business and public interest.

The European Commission identifies two key components that will allow such an AI ecosystem to develop in a way that benefits EU society as a whole: excellence and trust, and it highlights the EU's Ethics Guidelines for Trustworthy Artificial Intelligence of April 2019 as a core element that is relevant for both of those components.

Like with many White Papers, however, the practical implications appear far off in the future. We have therefore included a few notes ('Did you know?') with additional information to illustrate them or show what already exists, and conclude with some guidance on what you can already do today.

  • 1. Ecosystem of excellence

    The European Commission identifies several key aspects that will help create an ecosystem of excellence in relation to artificial intelligence:

    • Funding: Investment in research in innovation in Europe is but a fraction of investment in other parts of the world (3.2 billion EUR in Europe in 2016 versus 12.1 billion EUR in North America and 6.5 billion EUR in Asia). As a result, the European Commission aims to help significantly increase this level of investment in artificial intelligence projects, with an objective of 20 billion EUR per year over the next decade.
      [Did you know? You can already find certain funding opportunities about artificial intelligence on the European Commission's website.]
    • Research & innovation: The White Paper highlights the issue of fragmentation of centres of competence. The European Commission wishes to counter this by encouraging synergies and networks between research centres and greater coordination of efforts.
    • Skills: Creating AI solutions requires skillsets that are currently underdeveloped, and the deployment of AI solutions leads to a transformation of the workplace. Upskilling will therefore be important, notably through greater awareness of AI among all citizens and a greater focus on AI (including the Ethics Guidelines) at the level of higher education and universities. The European Commission specifically mentions the importance of increasing the number of women trained and employed in this area, as well as the need to involve social partners in ensuring a human-centred approach to AI at work.
      [Did you know? A recent Gartner survey revealed that lack of skills was the top challenge to adopting AI for respondents (56%), followed by understanding AI use cases (42%) and concerns over data scope or quality (34%). In Belgium, the KU Leuven has been offering a Master's degree in Artificial Intelligence since 1988.]
    • Adoption across sectors and organisation sizes: The White Paper then discusses various topics such as SMEs and start-ups, partnerships with the private sector and public-sector use of AI. The essence thereof is that private and public sector must both be involved – and both be encouraged to adopt AI solutions. Specifically in relation to SMEs and start-ups, the European Commission recognises access to (i) the technology and (ii) funding as key challenges, and suggests the strengthening of digital innovation hubs in each Member State to foster collaboration between SMEs.
    • Access to data and computing infrastructures: Without data, says the European Commission, "the development of AI and other digital applications is not possible". This approach to AI therefore comes in addition to the European data strategy, which aims at "setting up a true European data space, a single market for data, to unlock unused data, allowing it to flow freely within the European Union and across sectors for the benefit of businesses, researchers and public administrations".
  • 2. Ecosystem of trust

    Where AI is developed and deployed, it must address concerns that citizens might have in relation to e.g. unintended effects, malicious use, lack of transparency. In other words, it must be trustworthy. In this respect, the White Paper refers to the (non-binding) Ethics Guidelines, and in particular the seven key requirements for AI that were identified in those guidelines: 

    • Human agency and oversight
    • Technical robustness and safety
    • Privacy and data governance
    • Transparency
    • Diversity, non-discrimination and fairness
    • Societal and environmental wellbeing
    • Accountability

    Yet this is no legal framework.

    a) Existing laws & AI
    There is today no specific legal framework aimed at regulating AI. However, AI solutions are subject to a range of laws, as with any other product or solution: legislation on fundamental rights (e.g. data protection, privacy, non-discrimination), consumer protection, product safety and liability rules.
    [Did you know? AI-powered chatbots used for customer support are not rocket-science in legal terms, but the answers they provide are deemed to stem from the organisation, and can thus make the organisation liable. Because such a chatbot needs initial data to understand how to respond, organisations typically "feed" them previous real-life customer support chats and telephone exchanges, but the use of those chats and conversations is subject to data protection rules and rules on the secrecy of electronic communications.]

    According to the European Commission, however, the current legislation may sometimes be difficult to enforce in relation to AI solutions, for instance because of the AI's opaqueness ('black box-effect'), complexity, unpredictability and partially autonomous behaviour. As such, the White Paper highlights the need to examine whether any legislative adaptations or even new laws are required.

    The main risks identified by the European Commission are (i) risks for fundamental rights (in particular data protection, due to the large amounts of data being processed, and non-discrimination, due to bias within the AI); and (ii) risks for safety and the effective functioning of the liability regime. On the latter, the White Paper highlights safety risks, such as an accident that an autonomous car might cause by wrongly identifying an object on the road. According to the European Commission, "[a] lack of clear safety provisions tackling these risks may, in addition to risks for the individuals concerned, create legal uncertainty for businesses that are marketing their products involving AI in the EU."
    [Did you know? Data protection rules do not prohibit e.g. AI-powered decision processes or data collection for machine learning, but certain safeguards must be taken into account – and it's easier to do so at the design stage.]

    The European Commission recommends examining how legislation can be improved to take into account these risks and to ensure effective application and enforcement, despite AI's opaqueness. It also suggests that it may be necessary to examine and re-evaluate existing limitations of scope of legislation (e.g. general EU safety legislation only applies to products, not services), the allocation of responsibilities between different operators in the supply chain, the very concept of safety etc.

    b) A future regulatory framework for AI
    The White Paper includes lengthy considerations on what a new regulatory framework for AI might look like, from its scope (the definition of "AI") to its impact. A key element highlighted is the need for a risk-based approach (as in the GDPR), notably in order not to create a disproportionate burden, especially for SMEs. Such a risk-based approach, however, requires solid criteria to be able to distinguish high-risk AI solutions from others, which might be subject to fewer requirements.

    According to the Commission, an AI application should be considered high-risk where it meets the following two cumulative criteria:

    • Inherent risk based on sector: "First, the AI application is employed in a sector where, given the characteristics of the activities typically undertaken, significant risks can be expected to occur". This might include the healthcare, transport and energy sectors, for instance.
    • Solution-created risks: "Second, the AI application in the sector in question is, in addition, used in such a manner that significant risks are likely to arise". The White Paper uses the example of appointment scheduling systems in the healthcare sector, stating that they "will normally not pose risks of such significance as to justify legislative intervention". However, this example creates in our view precisely the level of uncertainty the White Paper aims to avoid, as it is not hard to see surgery appointments as often being crucial to the safety of patients.

    Yet the White Paper immediately lists certain exceptions that would irrespective of the sector be 'high-risk', stating that this would be relevant for certain 'exceptional instances'. In the absence of actual legislative proposals, the merit of this principle-exception combination is difficult to judge. However, it would not surprise us to see a broader sector-independent criterion for 'high-risk' AI solutions appear – situations that are high-risk irrespective of the sector due to their impact on individuals or organisations. 

    Those high-risk AI solutions would then likely be subject to specific requirements in relation to the following topics:

    • Training data: There could be requirements in relation to the safety of training data (i.e. data used to train the AI), the non-discriminatory nature thereof (e.g. sufficiently representative data sets) and its compliance with privacy and data protection rules.
    • Keeping of records & data: To verify compliance (e.g. allow decisions to be traced back and verified), there might be requirements to maintain accurate records of the characteristics and selection process of the training data, perhaps even the data itself, as well as documentation on the programming and training methodologies, taking into account the protection of confidential information, such as trade secrets.
    • Provision of information: There could be transparency requirements, such as the provision of information on the AI system's capabilities and limitations to the customer or person deploying the system, but also information to citizens whenever they are interacting with an AI system and not a human being if that is not immediately obvious.
    • Robustness & accuracy: There might be requirements that (i) AI systems are robust and accurate, or at least correctly reflect their level of accuracy; (ii) outcomes are reproducible, (iii) AI systems can adequately deal with errors or inconsistencies and (iv) AI systems are resilient against both overt attacks and more subtle attempts to manipulate data or algorithms themselves (with mitigation measures taken).
    • Human oversight: Some form of human oversight is deemed crucial to the creation of trustworthy, ethical and human-centric AI, in particular for high-risk AI solutions. Without prejudice to the data protection rules on automated decision-making, the White Paper states that different forms of human intervention might be appropriate, depending on the circumstances. In some cases, for instance, human review would be needed prior to any decision (e.g. rejection of someone's application for social security benefits); for others, it might merely be the ability to intervene afterwards or in the context of monitoring.
    • Facial recognition in public places & similar remote biometric identification: Specifically mentioning the deployment of facial recognition in public places as an illustration, the White Paper states that "AI can only be used for remote biometric identification purposes where such use is duly justified, proportionate and subject to adequate safeguards". The European Commission will in this context launch a 'broad European debate' on the topic, with a view to defining when this can be justified and common safeguards.

    In practice, these requirements would cover a range of aspects of the development and deployment cycle of an AI solution, and the requirements are therefore not meant solely for the developer or the person deploying the solution. Instead, according to the European Commission, "each obligation should be addressed to the actor(s) who is (are) best placed to address any potential risk". The question of liability might still be dealt with differently – under EU product liability law, "liability for defective products is attributed to the producer, without prejudice to national laws which may also allow recovery from other parties".

    Because the aim would be to impose such requirements on 'high-risk' AI solutions, the European Commission anticipates that a prior conformity assessment will be required, which could include procedures for testing, inspection or certification and checks of the algorithms and of the data sets used in the development phase. Some requirements (e.g. information to be provided) might not be included in such prior conformity assessment. Moreover, depending on the nature of the AI solution (e.g. if it evolves and learns from experience), it may be necessary to carry out repeated assessments throughout the lifetime of the AI solution.

    The Commission also wishes to open up the possibility for AI solutions that are not 'high-risk' to benefit from voluntary labelling, to show voluntary compliance with (some or all of) those requirements.

  • 3. In practice: anticipating future rules

    The White Paper sets out ambitious objectives, but also gives an idea of the direction in which the legal framework applicable to AI might evolve in the coming years.

    We do feel it is important to stress that this future framework should not be viewed as blocking innovation. Too many organisations have the impression already that the GDPR prevents them from processing data, when it is precisely a tool that allows better and more responsible processing of personal data. The framework described by the European Commission in relation to AI appears to be similar in terms of its aim: these rules would help organisations build better AI solutions or use AI solutions more responsibly.

    In this context, organisations working today on AI solutions would do well to consider building the recommendations of the White Paper already into their solutions. While there is no legal requirement to do so now, anticipating those requirements might give those organisations a frontrunner status and a competitive edge when the rules materialise.

    If you need any suggestions on how to do so, NautaDutilh's cross-practice and Benelux-wide Tech team is here to help. 

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