用逻辑推理和最优传输消除敏感信息及代理，实现可证明的公平性。
From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems
- 基于本体构建敏感属性与代理的完整结构
- 通过最优传输生成与偏见完全独立的表示
- 适合需要可验证公平性的高风险决策场景
本文提出一种可证明公平的AI框架，突破现有偏差缓解方法的局限。通过OWL 2 QL本体工程，形式化定义敏感属性并逻辑推导其代理，构建捕获所有偏见模式的σ代数G。利用Delbaen-Majumdar最优传输，生成与G完全独立且最小化L2距离的公平表示，确保真正独立而非仅去相关。该方法将偏见建模为σ代数间的依赖关系，将本体知识编译为可测结构，并以最优传输作为唯一公平变换，实现贷款审批等任务中的完全公平性，例如邮政编码作为种族代理的情形。结果是可认证、数学严格可信的AI系统。
原文摘要 · Abstract (English)
This paper presents a framework for provably fair AI that overcomes the limits of current bias mitigation methods by systematically removing all sensitive information and its proxies. Using ontology engineering in OWL 2 QL, it formally defines sensitive attributes and infers their proxies through logical reasoning, constructing a sigma algebra G that captures the full structure of biased patterns. Fair representations are then obtained via Delbaen Majumdar optimal transport, which generates variables independent of G while minimizing L2 distance to preserve accuracy. This guarantees true independence rather than mere decorrelation. By modeling bias as dependence between sigma algebras, compiling ontological knowledge into measurable structures, and using optimal transport as the unique fair transformation, the approach ensures complete fairness in tasks like loan approval, where proxies such as ZIP code reveal race. The result is a certifiable and mathematically grounded method for trustworthy AI.
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