论文标题
穿上鞋子一英里：机器学习的新公平标准
Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning
论文作者
论文摘要
古老的善解人意的格言是``穿鞋行走一英里'，''问一个人想象别人可能会面临的困难。这表明基于\ textit {group}级别的新的ML反事实公平标准：如果他们的组受到某些受保护的组的条件，则非保护小组票价的成员将如何？如果他是黑人，则不要问一个特定的白种人定罪者会接受什么判决，而是将该概念带给整个团体。例如如果所有白人罪犯的平均句子是黑人，但具有相同的白色特征，例如相同数量的先前定罪？我们将问题构成问题，并以经验研究不同的数据集。我们的方法也是解决与敏感属性协变量相关问题的解决方案。
The old empathetic adage, ``Walk a mile in their shoes,'' asks that one imagine the difficulties others may face. This suggests a new ML counterfactual fairness criterion, based on a \textit{group} level: How would members of a nonprotected group fare if their group were subject to conditions in some protected group? Instead of asking what sentence would a particular Caucasian convict receive if he were Black, take that notion to entire groups; e.g. how would the average sentence for all White convicts change if they were Black, but with their same White characteristics, e.g. same number of prior convictions? We frame the problem and study it empirically, for different datasets. Our approach also is a solution to the problem of covariate correlation with sensitive attributes.
