AI-Mediated Explainable Regulation for Justice
Summary: arXiv:2604.00237v1 Announce Type: cross
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.
Introduction
The regulatory landscape is plagued by various inefficiencies and shortcomings that hinder effective governance. Traditional regulatory practices often lack transparency, adaptability, and inclusiveness. As a result, regulations may become stale, failing to address the dynamic needs of society. This article discusses an innovative approach that leverages distributed artificial intelligence (AI) to develop a framework for regulatory recommendations that is both explainable and adaptable.
The Need for Change
Current regulatory systems are often criticized for:
- Static Nature: Regulations tend to remain unchanged despite evolving societal values and facts.
- Lack of Explanation: Many regulations are implemented without clear reasoning, leading to confusion and distrust among stakeholders.
- Influence of Powerful Interests: Regulatory decisions can be swayed by those with significant lobbying power, leaving marginalized voices unheard.
- Perception of Illegitimacy: The disconnect between regulatory authorities and the public fosters a belief that regulations do not reflect the true needs of society.
The Proposed AI Approach
To combat these issues, we propose an AI-mediated regulatory framework that incorporates the following key components:
- Distributed Artificial Intelligence: Utilizing AI allows for the modeling of diverse stakeholder preferences through separate preference models. This ensures that a wide array of perspectives is considered in the regulatory process.
- Value-Sensitive Aggregation: The framework aggregates stakeholder preferences in a value-sensitive manner, ensuring that the resulting recommendations reflect the collective interests of society.
- Dynamic Updates: The system is designed to adapt to changes in factual circumstances or societal values, allowing for continuously relevant regulations.
- Explainability: Each regulatory recommendation is inherently explainable, providing stakeholders with insight into how their preferences were integrated into the decision-making process.
Stakeholder Engagement
Engaging stakeholders is crucial for the success of this AI-mediated approach. The framework allows stakeholders to:
- Express Preferences: Stakeholders can submit their preferences directly into the system, making their voices heard.
- Verify Consideration: The system will provide mechanisms for stakeholders to verify that their inputs were appropriately considered in the regulatory decisions.
Conclusion
The proposed AI-mediated explainable regulation framework offers a promising solution to the challenges facing traditional regulatory systems. By ensuring transparency, adaptability, and inclusivity, this approach has the potential to foster regulatory justice, enhance legitimacy, and improve compliance. As we continue to evolve our understanding of AI capabilities, it is imperative to integrate these technologies into our regulatory frameworks for a more just and equitable society.