Toward Argument‐Based Fairness with an Application to AI‐Enhanced Educational Assessments
Toward Argument‐Based Fairness with an Application to AI‐Enhanced Educational Assessments
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基于辩论的公平及其在人工智能增强的教育评估中的应用
DOI：
10.1111/jedm.12334
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发表时间：
2022-06
影响因子：
1.3
通讯作者：
A. Huggins-Manley;Brandon M. Booth;S. D’Mello
中科院分区：
文献类型：
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作者：
A. Huggins-Manley;Brandon M. Booth;S. D’Mello
教育测量领域将有效性和公平性作为评估质量的核心概念。先前的研究已经提出在基于论证的有效性过程中嵌入公平性论证，特别是当公平性被认为是跨群体评估属性的可比性时。然而，我们认为，为了解决一组评估利益相关者可能持有的许多关于公平性的观点，需要一种更灵活的方法来解决有效性论点之外的公平性论点并与之互补。因此，我们将本文的重点放在两个方面：(a)引入基于论证的公平性方法，以补充传统评估和人工智能（AI）增强评估的基于论证的有效性；(b)将其应用于用于预筛选求职者的自动视频面试中感知可雇佣性的说明性AI评估。最后，我们提出了进一步推进基于论证的公平方法的建议。
The field of educational measurement places validity and fairness as central concepts of assessment quality. Prior research has proposed embedding fairness arguments within argument‐based validity processes, particularly when fairness is conceived as comparability in assessment properties across groups. 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 prescreen job candidates. We conclude with recommendations for further advancing argument‐based fairness approaches.