考虑特征约束时，公平决策需依赖不含敏感特征的合理解释。
Fairness of Classifiers in the Presence of Constraints between Features
- 用受约束条件影响的最小析取项解释判定公平性
- 忽略约束可能完全改变决策的公平性判断
- 适合关注模型可解释性与合规性的研究人员
在机器学习中，分类器的公平性通常定义为决策不应依赖于敏感特征（如性别）。然而，当特征间存在约束时，这种依赖关系可能被掩盖。为解决此问题，我们提出：若决策具有不含敏感特征的公平解释，则该决策是公平的。公平解释定义为在考虑约束条件下不包含敏感特征的最小析取项原因。令人惊讶的是，即使敏感特征与非敏感特征间无直接约束，忽略约束仍可能完全改变决策的公平性判断。本文探讨了三种公平性定义之间的关系：(1) 所有决策仅有公平解释；(2) 至少存在一个公平解释；(3) 改变敏感特征不改变结果。并研究了测试分类器公平性的计算复杂性。
原文摘要 · Abstract (English)
In Machine Learning, an accepted definition of fairness of a decision taken by a classifier is that it should not depend on protected features, such as gender. Unfortunately, when constraints exist between features, such dependencies can be obscured by the constraints. To avoid this problem, we propose that a decision be considered fair if it has a fair explanation. We define a fair explanation as a prime-implicant reason for the decision that does not contain any protected feature (where the constraints are taken into account in the definition of prime-implicant). Surprisingly, ignoring constraints can completely change the fairness of a decision (according to this definition) even in the absence of constraints between protected and unprotected features. Three possible definitions of fairness of a classifier are that for all its decisions (1) there are only fair explanations, (2) there is at least one fair explanation, or (3) changing protected features does not change the outcome. We identify the relationships between these different definitions of fairness and study the computational complexity of testing fairness of classifiers.
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