What Kind of Fairness Is Fair? A Comment on Conway and Roberts
何种公平才算公平？对 Conway 和 Roberts 的评论AI 解读 ↓
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何种公平才算公平？对 Conway 和 Roberts 的评论
- 研究问题
- 直接回归与反向回归对应的两种公平观为何可能冲突？评价就业歧视是否必须同时采用两种公平标准，是评论针对的核心争点。
- 方法与思路
- 作者围绕直接回归以报酬为因变量、反向回归以资历为因变量的设定，评论测量偏误讨论及两种回归关联的公平观。
- 材料陈述的贡献
- 摘要认可两种回归对应不同公平观，且判断可能不一致；作者质疑必须同时采用两者评价公平，以及判准冲突造成严重困难的主张。
- 阅读关注点
- 阅读全文应区分测量误差与公平标准这两层问题，核对作者反对同时采用两种判准的理由，以及判准冲突在何种情形下出现。
- 阅读边界
- 所给摘要主要交代争论背景与评论立场，未给出后续反驳细节或实证证据；不能据此认定某种回归已解决测量偏误或公平争议。
原文摘要
In his previous work, Roberts (1979,1980) emphasized problems of measurement and possible bias involved in using direct regression, where reward is the dependent variable, and the independent variables consist of a vector of qualifications. He also suggested that these problems could, to a large extent, be solved by using reverse regression, where reward is the independent variable, and the dependent variable consists of a measure of qualifications. The debate on these issues is continuing. (See Blattenberger and Michelson 1980, Finkelstein 1980, Madden 1982, and Blau and Kahn 1983.) In Reverse Regression, Fairness, and Employment Discrimination, however, Conway and Roberts (1983) put primary emphasis on the fact that there are two distinct concepts of fairness associated with direct and reverse regression and that these different perspectives are basic to understanding discrimination. They also point out, quite correctly, that these two concepts will only occasionally be in agreement. Up to this point, their exposition of these issues is clear and helpful. It is when they go on to claim that both concepts are needed to evaluate fairness, and that contradictions between the two pose serious problems for lawyers, statisticians, and judges alike that their conclusions need to be challenged.