Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rapid development and emergent properties, policymakers across the globe rely on high-level principles and abstract legal requirements. Yet, while this flexibility supports future-proofing human-centred regulations and aligning them with socio-ethical values, it also causes legal uncertainty downstream as developers, companies, and auditors struggle with translating these abstract requirements into verifiable technical requirements. Using the AI Act as an example, this paper draws on Coleman's bathtub to analyse the regulatory learning space in AI governance. It argues that legal uncertainty cannot be fully reduced ex ante and that, within reasonable bounds, it is also necessary for regulatory learning because it creates the space in which boundary negotiation over socio-technical meaning can occur. Building on this analysis, the paper shows how boundary objects and boundary negotiating artifacts help explain the translation of legal requirements into operational practice. By examining technical sandbox frameworks, it further identifies concrete properties that technical infrastructures must possess to function effectively as boundary negotiation artifacts in AI assessment. The paper concludes that legal certainty remains the long-term aim, but that premature closure of regulatory instruments risks undermining the learning processes needed for adaptive governance.

翻译：对人工智能（AI）的有效监管是一项关键的政策挑战，其驱动力源于AI已融入社会的方方面面。为应对其快速发展和涌现特性，全球政策制定者依赖高层级原则和抽象的法律要求。然而，尽管这种灵活性支持制定面向未来的、以人为本的法规，并使其与社会伦理价值保持一致，但它也导致了下游的法律不确定性，因为开发者、公司和审计人员在将这些抽象要求转化为可验证的技术要求时遇到了困难。本文以《人工智能法案》为例，借鉴科尔曼的浴缸模型来分析AI治理中的规制学习空间。本文认为，法律不确定性无法完全事先消除，并且在合理的范围内，它对于规制学习也是必要的，因为它创造了可以就社会技术意义进行边界协商的空间。基于此分析，本文展示了边界对象和边界协商人工制品如何帮助解释法律要求向操作实践的转化。通过考察技术沙盒框架，本文进一步识别出技术基础设施必须拥有的具体属性，才能有效作为AI评估中的边界协商人工制品发挥作用。本文的结论是，法律确定性仍是长期目标，但过早地固化监管工具可能会危及适应性治理所需的学习过程。