论文标题
反事实公平和拟议的修改的缺点
Shortcomings of Counterfactual Fairness and a Proposed Modification
论文作者
论文摘要
在本文中，我认为反事实公平并不构成算法是公平的必要条件，然后建议如何修改约束以弥补这一缺点。为此，我讨论了一种假设的情况，在这种情况下，反事实公平和对公平性的直觉判断分开了。然后，我提出一个问题，如何可以阐明歧视的概念，以便更详细地研究反事实公平的缺点，这是算法公平的必要条件。然后，我将这种分析的见解纳入了一种新颖的公平约束，因果关系公平，这是对反事实公平约束的修改，似乎可以避免其缺点。
In this paper, I argue that counterfactual fairness does not constitute a necessary condition for an algorithm to be fair, and subsequently suggest how the constraint can be modified in order to remedy this shortcoming. To this end, I discuss a hypothetical scenario in which counterfactual fairness and an intuitive judgment of fairness come apart. Then, I turn to the question how the concept of discrimination can be explicated in order to examine the shortcomings of counterfactual fairness as a necessary condition of algorithmic fairness in more detail. I then incorporate the insights of this analysis into a novel fairness constraint, causal relevance fairness, which is a modification of the counterfactual fairness constraint that seems to circumvent its shortcomings.
