A paper argues that AI regulatory sandboxes need shared operational artifacts, not just more guidance

A paper argues that AI regulatory sandboxes need shared operational artifacts, not just more guidance

A paper on AI Regulatory Sandboxes (AIRS) frames their central challenge as shared sense-making among AI innovators, technical experts, and regulators. Its findings suggest that additional documentation or guidance alone is not enough to resolve the information asymmetry between stakeholders; the paper instead identifies a need for artifacts that help them negotiate meaning.

The proposed prototype is designed as a shared working layer. It includes a curated, extensible catalog of AI tests, controls, and datasets; maps regulatory requirements to assessment metrics; supports use-case-specific test selection; enables collaborative test execution and result visualization; and generates structured assessment reports.

The accompanying AIRS engagement framework spans pre-application, participant selection, onboarding, test execution, final evaluation and interpretation, and post-participation knowledge sharing. In the proposed workflow, participants can align on system information, sandbox plans, relevant tests, datasets, and controls; record iterations through tamper-proof logs; review results collaboratively; and use generated reports and feedback to carry knowledge beyond the participation period.

For enterprise governance teams, the practical implication is a decision framework; evaluate whether a sandbox creates reusable records of system purpose, data governance, human oversight, testing choices, results, residual risks, and stakeholder feedback. Those artifacts may be more operationally useful than guidance considered in isolation, but the paper does not establish that the prototype is production-ready or that the proposed structure improves sandbox outcomes.