# Bathtubs, Boundaries, and Sandboxes: AI Regulatory Learning under Legal Uncertainty

arXiv: https://arxiv.org/abs/2601.04094 · HTML: https://mlsanity.com/paper/2601.04094/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework](https://mlsanity.com/paper/2606.18532/) (2026)
- [Governance-as-Code: Translating EU AI Act Technical Requirements into Executable Compliance Pipelines for Generative AI Systems](https://mlsanity.com/paper/2609.20016/) (2026)
- [Designing a Boundary Negotiating Artifact for Collaborative Socio-Technical Sense-Making in AI Regulatory Sandboxes](https://mlsanity.com/paper/2609.37109/) (2026)
- [The AI Assessment Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes](https://mlsanity.com/paper/2610.01539/) (2026)

## Abstract (arXiv)

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.