Fairness in decision-making has been a long-standing issue in our society. Despite the increasing number of research activities on unfairness mitigation in machine learning models, there is little research focusing on mitigating unfairness in human decisions. Fairness in human decisions is as important as, if not more important than, fairness in machine learning models since there are processes where humans make the final decisions and machine learning models can inherit bias from the human decisions they were trained on. As a result, this work aims to detect unfairness in human decisions, the very first step of solving the unfair human decision problem. This paper proposes to utilize the existing machine learning fairness detection mechanisms to detect unfairness in human decisions. The rationale behind this is, while it is difficult to directly test whether a human makes unfair decisions, with current research on machine learning fairness, it is now easy to test, on a large scale at a low cost, whether a machine learning model is unfair. By synthesizing unfair labels on four general machine learning fairness datasets and one image processing dataset, this paper shows that the proposed approach is able to detect (1) whether or not unfair labels exist in the training data and (2) the degree and direction of the unfairness. We believe that this work demonstrates the potential of utilizing machine learning fairness to detect human decision fairness. Following this work, research can be conducted on (1) preventing future unfair decisions, (2) fixing prior unfair decisions, and (3) training a fairer machine learning model.
翻译:决策的公平一直是我们社会中长期存在的一个问题。尽管关于减少机器学习模式中的不公平现象的研究活动越来越多,但很少有研究侧重于减少人类决策中的不公平现象。人类决策的公平性与机器学习模式的公平性同样重要,即使不是更重要,也比机器学习模式的公平性重要,因为有各种过程,人类可以作出最终决定,机器学习模式可以继承他们所培训的人类决策中的偏见。因此,这项工作的目的是发现人类决策中的不公平现象,这是解决不公平人类决策问题的第一步。本文件提议利用现有机器学习公平性检测机制,以发现人类决策中的不公平现象。 这样做的理由是,虽然很难直接检验人类是否作出不公平的决定,而目前关于机器学习公平性的研究则比机器学习模式的公平性重要,但现在很容易在大规模上测试机器学习模式是否不公平。通过将四个普通机器学习模型的不公平标签合成,这是解决不公平的人类决策问题的第一步。本文件建议采用的方法可以检测:(1) 是否存在不公平的标签,在培训数据中是否存在不公平的标签。(2) 我们相信,在研究之前可以进行这种公平性研究。