ANOTHER LOOK AT “CULTURAL FAIRNESS”1

By Richard B. Darlington. 1971

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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, background): “ thread of work highlights that different fairness criteria encode different value systems [34], and that quantitative techniques alone cannot answer the question of which to use. In 1971, Darlington [19] urges that the concept of “cultural fairness” be replaced by “cultural optimality”, which takes”
The Implicit Fairness Criterion of Unconstrained Learning (2018, background): “…back the 1970s confirm the observed tension between calibration and other classification criteria, including the aforementioned criterion of separation, which is related to the disparity in false positive rates (Darlington, 1971; Chouldechova, 2017; Kleinberg et al., 2017; Barocas et al., 2018).”
Fairness in Machine Learning: A Survey (2020, background): “[260, 242]), impossibility conditions between different fairness conditions ([77]), and fairness quantification based on metrics (e.”
Algorithmic Bias in Education (2021, background): “…use of fairness metrics manifest in several “impossibility” results (Chouldechova, 2017; Kleinberg et al., 2017; Berk et al., 2018; Loukina et al., 2019; Lee & Kizilcec, 2020, Darlington, 1971), where satisfaction of one statistical criterion of fairness makes “impossible” satisfaction of another.”