Mapping the Regulatory Learning Space for the EU AI Act
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912399864365056 |
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| author | Lewis, Dave Lasek-Markey, Marta Golpayegani, Delaram Pandit, Harshvardhan J. |
| author_facet | Lewis, Dave Lasek-Markey, Marta Golpayegani, Delaram Pandit, Harshvardhan J. |
| contents | The EU AI Act represents the world's first transnational AI regulation with concrete enforcement measures. It builds on existing EU mechanisms for regulating health and safety of products but extends them to protect fundamental rights and to address AI as a horizontal technology across multiple application sectors. We argue that this will lead to multiple uncertainties in the enforcement of the AI Act, which coupled with the fast-changing nature of AI technology, will require a strong emphasis on comprehensive and rapid regulatory learning for the Act. We define a parametrised regulatory learning space based on the provisions of 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 rapid and effective regulatory learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mapping the Regulatory Learning Space for the EU AI Act Lewis, Dave Lasek-Markey, Marta Golpayegani, Delaram Pandit, Harshvardhan J. Computers and Society The EU AI Act represents the world's first transnational AI regulation with concrete enforcement measures. It builds on existing EU mechanisms for regulating health and safety of products but extends them to protect fundamental rights and to address AI as a horizontal technology across multiple application sectors. We argue that this will lead to multiple uncertainties in the enforcement of the AI Act, which coupled with the fast-changing nature of AI technology, will require a strong emphasis on comprehensive and rapid regulatory learning for the Act. We define a parametrised regulatory learning space based on the provisions of 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 rapid and effective regulatory learning. |
| title | Mapping the Regulatory Learning Space for the EU AI Act |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2503.05787 |