Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
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.
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BibTeX
@misc{suk2026equality,
title = {{Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)}},
author = {Youmi Suk},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.17679},
eprint = {2607.17679},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.17679},
}
RIS
TY - UNPB
TI - Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
AU - Youmi Suk
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2607.17679
UR - https://arxiv.org/abs/2607.17679
AB - 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.
ER -