用新指标评估机器学习偏见，区分歧视性测试与系统性差异。
Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning
- 提出客观公平指数，融合法律标准与上下文测试。
- 在COMPAS数据上发现算法偏见，验证指标实用性。
- 适合法律科技、算法伦理研究者参考。
基于现行法律标准，我们从边际收益和客观测试的角度定义偏见，提出新型度量指标“客观公平指数”（Objective Fairness Index）。该指标结合了客观测试的上下文细节与度量稳定性，提供了一种法律上一致且可靠的评估方式。利用该指数，我们对敏感的机器学习应用（如COMPAS再犯预测）进行了新的分析，揭示了其在实践与理论上的重要价值。客观公平指数能够有效区分歧视性测试与系统性差异，为算法公平性评估提供了新工具。
原文摘要 · Abstract (English)
Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric "Objective Fairness Index". This index combines the contextual nuances of objective testing with metric stability, providing a legally consistent and reliable measure. Utilizing the Objective Fairness Index, we provide fresh insights into sensitive machine learning applications, such as COMPAS (recidivism prediction), highlighting the metric's practical and theoretical significance. The Objective Fairness Index allows one to differentiate between discriminatory tests and systemic disparities.
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