提出MARLA框架，助法律与技术方协同推进AI监管学习
MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act
- 构建五阶段循环框架，将法规落地为可执行的技术实践
- 通过案例验证，实现从本地到欧洲的监管知识传递
- 为技术与法律团队提供统一语言，促进跨领域协作
欧盟《人工智能法案》将监管视为安全、可信且具备市场可行性的创新基础设施。实现这一目标需要监管学习：在实施过程中生成的证据必须转化为支持一致解释、有效监督和随技术演进而适应的治理与法律知识。然而，生成证据的主体与依赖这些证据的主体处于不同的专业领域。本文提出MARLA（Map, Assess, Report, Learn, Adapt）概念框架，将监管学习组织为五个阶段的循环，聚焦于法律要求在社会技术实践中落地，涵盖欧盟人工智能治理架构中的地方、国家和欧洲层面。该框架不具强制性，旨在为技术和法律利益相关方提供共同语言；前三个阶段各自产出可记录的监管学习成果。通过两个试点案例和一个国家至欧洲的前瞻性示例加以说明。
原文摘要 · Abstract (English)
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 practices, situated at the Local, National and European levels of the AI Act's governance architecture. Deliberately non-prescriptive, MARLA gives technical and legal stakeholders a shared vocabulary in which each of the first three stages generates its own documentable form of regulatory learning. We illustrate the scaffold with two piloted case studies and a prospective National-to-European illustration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。