Mapping the Regulatory Learning Space for the EU AI Act

• URL: http://arxiv.org/abs/2503.05787v1
• Date: Thu, 27 Feb 2025 12:46:30 GMT
• Title: Mapping the Regulatory Learning Space for the EU AI Act
• Authors: Dave Lewis, Marta Lasek-Markey, Delaram Golpayegani, Harshvardhan J. Pandit,
• Abstract summary: The EU's AI Act represents the world first transnational AI regulation with concrete enforcement measures.<n>It builds upon existing EU mechanisms for product health and safety regulation, but extends it to protect fundamental rights.<n>These extensions introduce uncertainties in terms of how the technical state of the art will be applied to AI system certification and enforcement actions.<n>We argue that these uncertainties, coupled with the fast changing nature of AI and the relative immaturity of the state of the art in fundamental rights risk management require the implementation of the AI Act to place a strong emphasis on comprehensive and rapid regulatory learning.
• Score: 0.8987776881291145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The EU's AI Act represents the world first transnational AI regulation with concrete enforcement measures. It builds upon existing EU mechanisms for product health and safety regulation, but extends it to protect fundamental rights and by addressing AI as a horizontal technology that is regulated across multiple vertical application sectors. These extensions introduce uncertainties in terms of how the technical state of the art will be applied to AI system certification and enforcement actions, how horizontal technical measures will map into vertical enforcement responsibilities and the degree to which different fundamental rights can be protected across EU Member States. We argue that these uncertainties, coupled with the fast changing nature of AI and the relative immaturity of the state of the art in fundamental rights risk management require the implementation of the AI Act to place a strong emphasis on comprehensive and rapid regulatory learning. We define parameterised axes for the regulatory learning space set out in the Act and describe a layered system of different learning arenas where the population of oversight authorities, value chain participants and affected stakeholders may interact to apply and learn from technical, organisational and legal implementation measures. We conclude by exploring how existing open data policies and practices in the EU can be adapted to support regulatory learning in a transparent manner that supports the development of trust in and predictability of regulated AI. We discuss how the Act may result in a regulatory turn in the research of AI fairness, accountability and transparency towards investigations into implementations of and interactions between different fundamental rights protections and reproducible and accountable models of metrology for AI risk assessment and treatment.

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