The EU AI Act adopts a horizontal and adaptive approach to govern AI technologies characterised by rapid development and unpredictable emerging capabilities. To maintain relevance, the Act embeds provisions for regulatory learning. However, these provisions operate within a complex network of actors and mechanisms that lack a clearly defined technical basis for scalable information flow. This paper addresses this gap by establishing a theoretical model of the regulatory learning space defined by the AI Act, decomposed into micro, meso, and macro levels. Drawing from this functional perspective of this model, we situate the diverse stakeholders -- ranging from the EU Commission at the macro level to AI developers at the micro level -- within the transitions of enforcement (macro-micro) and evidence aggregation (micro-macro). We identify AI Technical Sandboxes (AITSes) as the essential engine for evidence generation at the micro level, providing the necessary data to drive scalable learning across all levels of the model. By providing an extensive discussion of the requirements and challenges for AITSes to serve as this micro-level evidence generator, we aim to bridge the gap between legislative commands and technical operationalisation, thereby enabling a structured discourse between technical and legal experts.

翻译：欧盟《人工智能法案》采用横向且适应性的方法来治理具有快速发展与不可预测新兴能力特征的人工智能技术。为保持法规的相关性，该法案嵌入了监管学习条款。然而，这些条款运作于一个复杂的行动者与机制网络中，缺乏可扩展信息流所需的明确定义的技术基础。本文通过建立一个由《人工智能法案》定义的监管学习空间理论模型来弥补这一空白，该模型被分解为微观、中观和宏观三个层级。基于该模型的功能视角，我们将从宏观层级的欧盟委员会到微观层级的人工智能开发者等多元利益相关者，置于执法（宏观-微观）与证据聚合（微观-宏观）的转换过程中进行定位。我们识别出人工智能技术沙箱（AITSes）作为微观层面证据生成的核心引擎，为驱动模型所有层级的可扩展学习提供必要数据。通过对AITSes作为微观层面证据生成器所需满足的要求及面临的挑战进行广泛讨论，我们旨在弥合立法指令与技术操作化之间的鸿沟，从而促成技术专家与法律专家之间结构化的对话。