The Bathtub of European AI Governance: Identifying Technical Sandboxes as the Micro-Foundation of Regulatory Learning

2026-01-07

arXiv.org

AI Summary

This study addresses the European Union’s Artificial Intelligence Act’s limited adaptability to rapidly evolving AI ecosystems due to the absence of a scalable regulatory learning infrastructure. To bridge this gap, the paper proposes a triadic regulatory learning space model encompassing micro-, meso-, and macro-level layers. It systematically positions AI regulatory sandboxes as the core mechanism at the micro level, designed to generate structured evidence and foster synergies between legal frameworks and technical development. Through multi-level systems modeling, integrated analysis of regulatory architectures, and sandbox functional design, the research establishes the AI sandbox as a pivotal engine for regulatory learning. This approach offers a concrete technical pathway and an interdisciplinary dialogue framework to enhance the EU’s AI governance capacity.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Human-in-the-loop Machine Learning
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The EU AI Act adopts a horizontal and adaptive approach to govern AI technologies characterised by rapid development and unpredictable emerging capabilities. To maintain relevance, the Act embeds provisions for regulatory learning. However, these provisions operate within a complex network of actors and mechanisms that lack a clearly defined technical basis for scalable information flow. This paper addresses this gap by establishing a theoretical model of the regulatory learning space defined by the AI Act, decomposed into micro, meso, and macro levels. Drawing from this functional perspective of this model, we situate the diverse stakeholders -- ranging from the EU Commission at the macro level to AI developers at the micro level -- within the transitions of enforcement (macro-micro) and evidence aggregation (micro-macro). We identify AI Technical Sandboxes (AITSes) as the essential engine for evidence generation at the micro level, providing the necessary data to drive scalable learning across all levels of the model. By providing an extensive discussion of the requirements and challenges for AITSes to serve as this micro-level evidence generator, we aim to bridge the gap between legislative commands and technical operationalisation, thereby enabling a structured discourse between technical and legal experts.

Problem

• AI governance
• regulatory learning
• technical sandbox
• EU AI Act
• evidence generation

Innovation

• AI Technical Sandboxes
• Regulatory Learning
• EU AI Act
• Micro-foundation
• Evidence Aggregation