论文标题
算法公平性的可能性：校准和相等的错误率是否可以核对？
A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?
论文作者
论文摘要
决策者越来越多地依靠算法风险评分来确定获得保释，贷款和医疗干预等二元治疗的机会。在这些设置中，我们调和了以前证明处于冲突的两个公平标准：校准和错误率平等。特别是，我们为存在的校准分数提供了必要和足够的条件，这些分数在任何给定的盲丝阈值下都产生了相等的错误率。然后，我们提出了一种算法，该算法搜索受校准和最小错误率差异的最准确分数。应用于Compas刑事风险评估工具，我们表明我们的方法可以在维持校准的同时消除错误差异。在单独的信贷贷款申请中，我们将我们的程序与省略敏感功能进行了比较，并表明它既可以提高利润和信誉良好的人获得贷款的可能性。
Decision makers increasingly rely on algorithmic risk scores to determine access to binary treatments including bail, loans, and medical interventions. In these settings, we reconcile two fairness criteria that were previously shown to be in conflict: calibration and error rate equality. In particular, we derive necessary and sufficient conditions for the existence of calibrated scores that yield classifications achieving equal error rates at any given group-blind threshold. We then present an algorithm that searches for the most accurate score subject to both calibration and minimal error rate disparity. Applied to the COMPAS criminal risk assessment tool, we show that our method can eliminate error disparities while maintaining calibration. In a separate application to credit lending, we compare our procedure to the omission of sensitive features and show that it raises both profit and the probability that creditworthy individuals receive loans.
