AI Insight
This commentary responds to a focus article comparing fairness approaches in psychometric testing and artificial intelligence/machine learning systems. The author extends Cheng's analysis by examining two key conceptual issues: how equality differs from equity in fairness contexts, and how causal reasoning should be incorporated into fairness research. The work emphasizes the importance of applying fairness considerations throughout the entire testing and algorithmic workflow, not just at final decision points.
Why it matters
This analysis bridges two historically separate fields—psychological testing and AI fairness—to develop more comprehensive frameworks for evaluating bias and discrimination in automated systems. The conceptual clarifications around equality versus equity and the role of causality could improve how researchers and practitioners design fair assessment systems across education, hiring, and other high-stakes applications.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
-cross
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. The focus article and this commentary point to directions for future fairness research across the psychometrics and AI/ML communities.
Source: Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)