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Ensuring fairness is crucial in developing modern algorithms and tests. To address potential biases and discrimination in algorithmic decision making, researchers have drawn insights from the test fairness literature, notably the work on differential algorithmic functioning (DAF) by Suk and Han. Nevertheless, the exploration of intersectionality in fairness investigations, within both test fairness and algorithmic fairness fields, is still relatively new. In this paper, we propose an extension of the DAF framework to include the concept of intersectionality. Similar to DAF, the proposed notion for intersectionality, which we term “interactive DAF,” leverages ideas from test fairness and algorithmic fairness. We also provide methods based on the generalized Mantel–Haenszel test, generalized logistic regression, and regularized group regression to detect DAF, interactive DAF, or other subtypes of DAF. Specifically, we employ regularized group regression with three different penalties and examine their performance via a simulation study. Finally, we demonstrate our intersectional DAF framework in real-world applications on grade retention and conditional cash transfer programs in education.
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A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning
As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to propose a new framework for algorithmic fairness based on differential item functioning (DIF), which has been commonly used to measure item fairness in psychometrics. Our fairness notion, which we call differential algorithmic functioning (DAF), is defined based on three pieces of information: a decision variable, a “fair” variable, and a protected variable such as race or gender. Under the DAF framework, an algorithm can exhibit uniform DAF, nonuniform DAF, or neither (i.e., non-DAF). For detecting DAF, we provide modifications of well-established DIF methods: Mantel–Haenszel test, logistic regression, and residual-based DIF. We demonstrate our framework through a real dataset concerning decision-making algorithms for grade retention in K–12 education in the United States.
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- Award ID(s):
- 2225321
- PAR ID:
- 10412872
- Publisher / Repository:
- DOI PREFIX: 10.3102
- Date Published:
- Journal Name:
- Journal of Educational and Behavioral Statistics
- Volume:
- 49
- Issue:
- 2
- ISSN:
- 1076-9986
- Format(s):
- Medium: X Size: p. 151-172
- Size(s):
- p. 151-172
- Sponsoring Org:
- National Science Foundation
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