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.

翻译：过去十年间，计算机科学和哲学领域的文献提出了多种算法公平性标准。其中讨论最为广泛的标准之一——分类均等性——要求预测算法的错误分类在受保护特征所划分的群体中发生频率相等。尽管分类均等性具有直观吸引力，但它已受到质疑。人们可以设想多种场景：直觉上，预测算法并未对任何个体实施不公平对待，但分类均等性却遭到违背。为推进研究，我们转向一个相关原则——平等保护，该原则最初源于刑事司法领域。平等保护的关键在于均衡错误分类的风险（将在特定意义上加以界定），而非均衡错误分类的比率。我们证明，平等保护避免了分类均等性的诸多反例，但在若干常见场景中仍无法完全契合我们的道德直觉——例如，当预测因子在因果关系上处于受保护特征的下游时。为应对这些困难，我们提出一项新原则——因果平等保护，通过因果视角对错误分类的风险公平分配进行建模。