Over the last ten years the literature in computer science and philosophy has formulated different criteria of algorithmic fairness. One of the most discussed, classification parity, requires that the erroneous classifications of a predictive algorithm occur with equal frequency for groups picked out by protected characteristics. Despite its intuitive appeal, classification parity has come under attack. Multiple scenarios can be imagined in which - intuitively - a predictive algorithm does not treat any individual unfairly, and yet classification parity is violated. To make progress, we turn to a related principle, equal protection, originally developed in the context of criminal justice. Key to equal protection is equalizing the risks of erroneous classifications (in a sense to be specified) as opposed to equalizing the rates of erroneous classifications. We show that equal protection avoids many of the counterexamples to classification parity, but also fails to model our moral intuitions in a number of common scenarios, for example, when the predictor is causally downstream relative to the protected characteristic. To address these difficulties, we defend a novel principle, causal equal protection, that models the fair allocation of the risks of erroneous classification through the lenses of causality.

过去十年来，计算机科学和哲学领域的文献提出了不同的算法公平标准。其中最受关注的分类平等要求预测算法的错误分类在由受保护特征定义的群体中以相等的频率发生。然而，分类平等面临批评。为了取得进展，我们转向一个相关原则，即平等保护，最初在刑事司法领域发展起来。平等保护的关键是将错误分类的风险(在特定意义上)均等化，而不是将错误分类的比例均等化。我们展示了平等保护避免了许多违反分类平等的反例，但在许多常见情景中未能模拟我们的道德直觉，例如，在预测器相对于受保护特征是因果下游时。为了解决这些困难，我们提出了一项新的原则，即因果平等保护，通过因果性的视角模拟了对错误分类风险的公正分配。

因果均等保护作为算法公平性