用AI让监管规则可解释、可调整，减少利益集团影响。
AI-Mediated Explainable Regulation for Justice
- 用分布式AI构建独立偏好模型，分别处理各方诉求。
- 决策可随事实或价值观变化动态更新，且全程可解释。
- 适合关注公平监管与制度透明的政策研究者和公众。
当前监管决策存在静态、不可解释、易受强势利益集团影响及合法性不足等问题，导致社会不公并损害民主。本文提出一种基于分布式人工智能的新方法，使监管建议从设计上具备可解释性和适应性。系统通过独立建模各利益相关方的偏好，并以价值敏感方式聚合，确保推荐结果可随事实或价值变迁动态更新，且过程透明可验证。文中阐明了系统架构与实现路径，说明其如何解决现有监管体系弊端。该方法有助于提升监管的公正性、合法性与公众遵从度。
原文摘要 · Abstract (English)
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.
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