A COMPARISON OF THREE MODELS FOR DETERMINING TEST FAIRNESS

By Mary A. Lewis. 1978

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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) [20, 21, 121, 28, 36, 106, 60, 79, 61].”
Aligning AI With Shared Human Values (2020, background): “Research in algorithmic fairness initially began with simple statistical constraints (Lewis, 1978; Dwork et al., 2011; Hardt et al., 2016; Zafar et al., 2017), but these constraints were found to be mutually incompatible (Kleinberg et al., 2017) and inappropriate in many situations (CorbettDavies…”
Algorithmic Fairness: Choices, Assumptions, and Definitions (2021, background): “…the 1960s, psychometricians have studied the fairness of educational tests based on their ability to predict performance (at school or work) (Cleary 1966, Darlington 1971, Einhorn & Bass 1971, Thorndike 1971, Hunter & Schmidt 1976, Petersen & Novick 1976, Lewis 1978, Hutchinson & Mitchell 2019).”