比较心理测量与算法公平，厘清平等与公平差异及因果作用。
Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
- 将测试全流程映射到AI公平性框架，突破仅关注结果的局限。
- 指出平等重结果一致，公平重资源分配差异，本质不同。
- 强调因果推理对理解偏见根源的关键作用，适合跨领域研究者。
本文是对Ying Cheng（2026）在《Psychometrika》上发表的专题文章《心理测量与人工智能/机器学习中的公平性问题与评估：我们能从各自领域学到什么？》的特邀评论。Cheng系统比较了长期存在的测验公平性与现代算法公平性，其将整个测试流程映射至人工智能/机器学习公平性范式，而非仅关注最终选拔阶段，是跨学科公平研究的重要贡献。本文进一步探讨两个概念性问题：平等与公平的区别，以及因果关系在公平研究中的作用。二者共同指明了心理测量学与人工智能/机器学习领域未来公平性研究的方向。
原文摘要 · Abstract (English)
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.
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