AI-Mediated Explainable Regulation for Justice

TL;DR

This paper proposes an AI-driven, explainable, and adaptable regulatory system that models stakeholder preferences to improve justice, legitimacy, and responsiveness in decision-making.

Contribution

It introduces a novel AI-based framework for regulatory recommendations that are transparent, flexible, and stakeholder-aware, addressing current regulatory shortcomings.

Findings

The system models and reasons about stakeholder preferences separately.

Preferences are aggregated in a value-sensitive manner.

Recommendations can be updated with new facts or values.

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.