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| Autor/inn/en | Huggins-Manley, A. Corinne; Booth, Brandon M.; D'Mello, Sidney K. |
|---|---|
| Titel | Toward Argument-Based Fairness with an Application to AI-Enhanced Educational Assessments |
| Quelle | (2022), (42 Seiten)
PDF als Volltext |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Educational Assessment; Persuasive Discourse; Validity; Artificial Intelligence; Computer Assisted Testing; Screening Tests; Job Applicants; Employment Interviews; Test Bias |
| Abstract | The field of educational measurement places validity and fairness as central concepts of assessment quality (AERA, APA, NCME, 2014). Prior research has proposed embedding fairness arguments within argument-based validity processes, particularly when fairness is conceived as comparability in assessment properties across groups (Chapelle, 2021; Xi, 2010). However, we argue that a more flexible approach to fairness arguments that occurs outside of and complementary to validity arguments is required to address many of the views on fairness that a set of assessment stakeholders may hold. Accordingly, we focus this manuscript on two contributions: (a) introducing the argument-based fairness approach to complement argument-based validity for both traditional and artificial intelligence (AI)-enhanced assessments; and (b) applying it in an illustrative AI assessment of perceived hireability in automated video interviews used to pre-screen job candidates. We conclude with recommendations for further advancing argument-based fairness approaches. [This is the online first version of a paper that will be published in "Journal of Educational Measurement."] (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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