Cheng offers a systematic comparison between long-standing test fairness and modern algorithmic fairness, and points to directions for future fairness research across the psychometrics and AI/ML communities.
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: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. Together, the focus article and this commentary point to directions for future fairness research across the psychometrics and AI/ML communities.
INTRODUCTION
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