发表机构
Teachers College, Columbia University（哥伦比亚大学教师学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
对程关于心理测量学与人工智能/机器学习公平性比较的文章作评论，探讨平等与公平区别及因果关系在公平性研究中的作用，扩展其讨论，为心理测量学和人工智能/机器学习社区未来公平性研究指明方向。
AI 中文摘要
这是一篇对英·程（2026年，doi: https://doi.org/10.1017/psy.2026.10110 ）发表于《心理测量学》的重点文章《心理测量学与人工智能/机器学习中的公平性问题与评估：我们能从每个领域学到什么？》的特邀评论。程对长期存在的测试公平性与现代算法公平性进行了系统比较。她将整个测试工作流程映射到人工智能/机器学习公平性范式，而非仅最终选择阶段，这对跨学科公平性研究至关重要。本评论通过审视平等与公平的区别以及因果关系在公平性研究中的作用来扩展她的讨论。重点文章和本评论共同指出了心理测量学和人工智能/机器学习社区未来公平性研究的方向。
英文摘要
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.
CommentsInvited commentary on Cheng (2026), Psychometrika, doi:10.1017/psy.2026.10110; submitted to Psychometrika
