[Submitted on 20 Jul 2026 (v1), last revised 9 Oct 2026 (this version, v2)]
Abstract:This is an invited commentary on the Psychometrika focus article “Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?” by Ying Cheng (2026, doi:https://doi.org/10.1017/psy.2026.10110). Cheng offers a systematic comparison between long-standing test fairness and modern algorithmic fairness. Her mapping of the entire testing workflow onto the AI/ML fairness paradigm, rather than only the final selection stage, is a crucial contribution to interdisciplinary fairness research. This commentary extends her discussion by examining two conceptual issues: the distinction between equality and equity, and the role of causality in fairness research. The focus article and this commentary point to directions for future fairness research across the psychometrics and AI/ML communities.
Submission history
From: Youmi Suk [view email]
[v1] Mon, 20 Jul 2026 08:30:06 UTC (14 KB)
[v2] Fri, 9 Oct 2026 12:26:26 UTC (14 KB)