arXiv：cs.LG· Youmi Suk·· 13 小时前
公平性研究中的平等、公平与因果关系：对 Cheng（2026）的评论
Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
AI 导读
这是一篇针对 Ying Cheng（2026）在 Psychometrika 聚焦文章《Fairness Issues and Evaluation in Psychometrics and AI/ML》的受邀评论。
正文
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.
| Comments: | Invited commentary on Cheng (2026), doi:https://doi.org/10.1017/psy.2026.10110%3B accepted in Psychometrika |
| Subjects: | Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2607.17679 [stat.ME] |
| (or arXiv:2607.17679v2 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2607.17679 arXiv-issued DOI via DataCite |
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)
来源：arXiv：cs.LG · arxiv.org