Between the theory and practice of human oversight: Towards specialized AI regulatory sandboxes

Abstract

Human oversight has become a flagship safeguard in AI governance frameworks, yet its real‑world performance is uneven. Empirical studies show that humans supervising automated systems are vulnerable to automation bias and organizational incentives that reduce oversight to a box‑ticking exercise. More fundamentally, humans and machines reason in profoundly different ways, making their integration far from straightforward. EU secondary law – most notably the GDPR and the AI Act – requires ‘meaningful’ and ‘effective’ human oversight, but offers limited operational guidance on how to achieve it. This paper argues that specialized AI regulatory sandboxes for human oversight can bridge this theory-practice gap. After mapping the legal obligations and practical shortcomings of human oversight, the paper argues how the AI Act’s regulatory sandbox regime can be leveraged to test concrete oversight models, metrics, and workflows in controlled conditions. It further provides recommendations for the governance of human oversight-specific sandboxes. The paper thus offers a policy proposal to convert open‑textured legislative mandates on human oversight into evidence‑based practices, reduce fragmentation across Member States, and strengthen both innovation and fundamental‑rights protection in the EU’s AI governance.