Bathtubs, Boundaries, and Sandboxes: AI Regulatory Learning Under Legal Uncertainty

Abstract

Effective regulation of AI is a defining policy challenge, driven by its integration into all aspects of society. To remain responsive to the rapid development and emergent properties of AI systems, policymakers across the globe rely on high-level principles and abstract legal requirements. While this flexibility supports future-proofing human-centred regulations and aligning them with socio-ethical values, it causes legal uncertainty downstream as developers, companies, and auditors struggle with translating these abstract requirements into verifiable technical properties. 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.