Critical analysis of the statistical and ethical implications of various definitions of test bias.

By J. Hunter, Frank L. Schmidt. 1976

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “For example, since the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance (at school or work) [16, 120, 24, 33, 104, 57, 81, 58].”
50 Years of Test (Un)fairness: Lessons for Machine Learning (2018, methodology): “(3) Building from earlier insights of 1970s researchers [19, 34, 44] to incorporate quantitative factors for the balance between fairness goals and other goals, such as a value system or a system of ethics.”
Determinants, Detection and Amelioration of Adverse Impact in Personnel Selection Procedures: Issues, Evidence and Lessons Learned (2001, unknown): “Psychometrically, given mean subgroup differences combined with top-down selection (or the same test score cutoff in fixed selection), all models except the Cleary model are unlikely to be satisfied without some form of score adjustment within subgroups (Hunter and Schmidt 1976), which the 1991 Civi”
Algorithmic Bias in Education (2021, background): “Questions were raised about applying different fairness metrics in contexts of competing goals and values (Hunter & Schmidt, 1976).”
Pairwise Fairness for Ranking and Regression (2019, methodology): “Fair Regression Deﬁning fairness metrics in a regression setting is a challenging task, and has been studied for many years in the context of standardized testing [e.g. Hunter and Schmidt, 1976].”