MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act
The EU AI Act positions regulation as part of the infrastructure for safe, trustworthy and market-ready innovation. Realising this ambition requires regulatory learning: the evidence generated during implementation must be translated into governance and legal knowledge that supports consistent interpretation, effective oversight, and adaptation as technologies evolve. Yet the actors who produce this evidence and those who rely on it operate in different professional worlds. This paper proposes MARLA (Map, Assess, Report, Learn, Adapt), a conceptual scaffold organising regulatory learning as a five-stage cycle centred on the implementation of legal requirements into socio-technical practic...
| Authors | Alessio Buscemi, Tom Deckenbrunnen, Imane Hmiddou et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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