AI-Mediated Explainable Regulation for Justice
This addresses the issue of unjust and illegitimate regulations for society and democracy, but it is incremental as it builds on existing AI and regulatory concepts without introducing a fundamentally new paradigm.
The paper tackles the problems of static, unexplained, and biased regulatory decision-making by proposing a distributed AI system that generates explainable and adaptable regulatory recommendations based on stakeholder preferences. The result is a system designed to enhance regulatory justice, legitimacy, and compliance, though no concrete performance numbers are provided.
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.