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A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning

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  • Youmi Suk

    (Teachers College, Columbia University)

  • Kyung T. Han

    (Graduate Management Admission Council)

Abstract

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.

Suggested Citation

  • Youmi Suk & Kyung T. Han, 2024. "A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 151-172, April.
    • Handle: RePEc:sae:jedbes:v:49:y:2024:i:2:p:151-172
    DOI: 10.3102/10769986231171711
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    References listed on IDEAS

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    4. Richard Berk & Hoda Heidari & Shahin Jabbari & Michael Kearns & Aaron Roth, 2021. "Fairness in Criminal Justice Risk Assessments: The State of the Art," Sociological Methods & Research, , vol. 50(1), pages 3-44, February.
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