دورية أكاديمية
A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning
| العنوان: | A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning |
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| اللغة: | English |
| المؤلفون: | Youmi Suk (ORCID |
| المصدر: | Journal of Educational and Behavioral Statistics. 2024 49(2):151-172. |
| الإتاحة: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 22 |
| تاريخ النشر: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 2225321 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Education Level: | Elementary Secondary Education |
| Descriptors: | Psychometrics, Ethics, Decision Making, Algorithms, Artificial Intelligence, Test Items, Item Analysis, Bias, Grade Repetition, Elementary Secondary Education |
| DOI: | 10.3102/10769986231171711 |
| تدمد: | 1076-9986 1935-1054 |
| مستخلص: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| رقم الأكسشن: | EJ1415808 |
| قاعدة البيانات: | ERIC |
Full Text Finder | تدمد: | 1076-9986 1935-1054 |
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| DOI: | 10.3102/10769986231171711 |