提出可公开审计的AI公平性框架，防范算法偏见。
Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI Systems
- 引入第三方审计机制，采用透明白盒分析方法
- 工具开源可查，能系统检验模型是否违背平等机会标准
- 适合监管者、开发者及公众评估AI决策公正性
随着AI快速融入决策流程，其潜在偏见可能引发不公平结论。例如，美国司法系统中用于评估再犯风险的COMPAS系统被发现对少数族裔不利，违反了平等机会（equalized odds）公平标准。为此，本文提出一种由第三方审计方与系统提供方协作的公平性审计框架，并开发了开源工具，支持对AI系统的系统性审查。不同于传统黑箱方法，本框架采用透明白盒与统计分析相结合的方式，使第三方审计员、AI开发者或公众均可参考该工具判断模型公平性，推动可解释、可问责的AI应用。
原文摘要 · Abstract (English)
With the rapid advancement of AI, there is a growing trend to integrate AI into decision-making processes. However, AI systems may exhibit biases that lead decision-makers to draw unfair conclusions. Notably, the COMPAS system used in the American justice system to evaluate recidivism was found to favor racial majority groups; specifically, it violates a fairness standard called equalized odds. Various measures have been proposed to assess AI fairness. We present a framework for auditing AI fairness, involving third-party auditors and AI system providers, and we have created a tool to facilitate systematic examination of AI systems. The tool is open-sourced and publicly available. Unlike traditional AI systems, we advocate a transparent white-box and statistics-based approach. It can be utilized by third-party auditors, AI developers, or the general public for reference when judging the fairness criterion of AI systems.
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