CRII: SHF: Testing Fairness in Human Decisions with Algorithmic Bias

人类决策中的不公平一直是我们社会的一个长期问题，不断威胁着历史上代表性不足的群体的平等权。这个问题在决策过程中加剧了，在决策过程中，机器学习软件从潜在的不公平的人类决策中学习，并做出潜在的不公平预测来指导未来的人类决策。一个这样的例子是人才招聘流程，人力资源人员筛选由机器学习软件排序的候选人简历列表，以决定谁值得面试。人类决策的公平性在这种情况下尤其重要，因为(1)机器学习软件在对不公平的人类决策进行培训时将继承偏见；(2)即使使用无偏见的机器学习软件，只要人力资源人员有偏见，最终决策仍然可能是有偏见的。该项目将从不同的方向处理这一具有历史挑战性的问题。它学习用机器学习软件对人类决策进行建模，并利用该软件的算法偏差来检测人类决策中的偏差。这项工作可能会推动许多决策活动的公平进程，如人才招聘、信用卡审批和学校招生。与现有的大多数关注提高算法公平性的研究不同，本工作的目的是分离从训练数据继承的算法偏差(以人类决策为因变量)作为人类决策不公平性的指标。为了做到这一点，该项目将使用一种新技术来测试人类决策的公平性。具体地说，机器学习模型根据测试中的人类决策进行训练，并在每个人口统计组中重新平衡类别分布，然后使用比较判断的回归测试套件来测试学习的模型。如果模型没有通过回归测试，它所训练的人类决策将被认为是不公平的。虽然很难直接测试一个人是否有偏见，但使用比较判断来测试机器学习模型是否做出有偏见的预测更容易。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Unfairness in human decisions has been a long-standing issue in our society that constantly threatens the equality rights of historically underrepresented groups. This issue has been exacerbated in decision making processes where a machine learning software learns from potentially unfair human decisions and makes potentially unfair predictions to guide future human decisions. One such example is the talent hiring process where a human resource person screens a list of candidate resumes ranked by a machine learning software to decide who deserves an interview. Human decision fairness is especially important in this scenario since (1) the machine learning software will inherit the bias when trained on unfair human decisions; (2) even with unbiased machine learning software, the final decisions can still be biased as long as the human resource person is biased. This project will approach this historically challenging issue from a different direction. It learns to model the human decisions with a machine learning software and utilizes the algorithmic bias of that software to detect bias in the human decisions. This work could potentially advance the equity process in many decision making activities such as talent hiring, credit card approval, and school admission. In contrast to most of the existing research focusing on improving algorithmic fairness, this work aims to isolate the algorithmic bias inherited from the training data (with human decisions as dependent variables) as an indicator for human decision unfairness. To do this, the project will use a novel technique to test fairness in human decisions. Specifically, machine learning models are trained on the human decisions under test with re-balanced class distribution in each demographic group, then tests the learned model with a regression test suite of comparative judgements. If the model fails the regression test, the human decisions it was trained on will be considered as unfair. While it is difficult to directly test whether a human has bias, it is easier to test whether a machine learning model makes biased predictions using comparative judgements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.