AI-Mediated Explainable Regulation for Justice

2026-03-31

AI Summary

Current regulatory decisions often undermine fairness and legitimacy due to their static nature, lack of interpretability, and susceptibility to dominant interest groups. This work proposes a regulatory recommendation system that integrates distributed artificial intelligence with value-sensitive design, enabling independent modeling of diverse stakeholder preferences and their dynamic aggregation through interpretable and verifiable mechanisms. By adaptively responding to evolving factual and normative contexts, the approach uniquely combines distributed AI with value-sensitive principles to significantly enhance the transparency, equity, and social acceptability of regulatory recommendations. Consequently, it strengthens the perceived legitimacy, compliance, and public trust in regulatory processes.

Abstract

Present practice of deciding on regulation faces numerous problems that make adopted regulations static, unexplained, unduly influenced by powerful interest groups, and stained with a perception of illegitimacy. These well-known problems with the regulatory process can lead to injustice and have substantial negative effects on society and democracy. We discuss a new approach that utilizes distributed artificial intelligence (AI) to make a regulatory recommendation that is explainable and adaptable by design. We outline the main components of a system that can implement this approach and show how it would resolve the problems with the present regulatory system. This approach models and reasons about stakeholder preferences with separate preference models, while it aggregates these preferences in a value sensitive way. Such recommendations can be updated due to changes in facts or in values and are inherently explainable. We suggest how stakeholders can make their preferences known to the system and how they can verify whether they were properly considered in the regulatory decision. The resulting system promises to support regulatory justice, legitimacy, and compliance.

Problem

• regulation
• justice
• explainability
• legitimacy
• stakeholder preferences

Innovation

• Explainable AI
• Distributed AI
• Value-sensitive design
• Regulatory justice
• Preference modeling