发表机构
University of Bologna; European Commission（博洛尼亚大学; 欧洲委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对欧盟人工智能法案下监管学习的跨主体协作需求，本文提出MARLA五阶段概念框架，并结合案例说明其应用。
AI 中文摘要
欧盟人工智能法案将监管定位为安全、可信且市场化创新基础设施的一部分。实现这一目标需要监管学习：实施过程中产生的证据必须转化为治理和法律知识，以支持一致的解释、有效的监督及技术发展下的调整。然而，产生证据的主体与依赖证据的主体分属不同专业领域。本文提出MARLA（Map, Assess, Report, Learn, Adapt，即映射、评估、报告、学习、适应），这是一个将监管学习组织为五阶段循环的概念框架，核心是将法律要求落实到社会技术实践中，涵盖欧盟人工智能法案治理架构的地方、国家和欧洲层面。MARLA刻意不具规定性，为技术和法律利益相关者提供共同术语，前三个阶段各产生可记录形式的监管学习。我们通过两个试点案例研究及一个国家到欧洲层面的示例来说明该框架。
英文摘要
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.
