Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)
2026-07-20 • Machine Learning
Machine Learning
AI summaryⓘ
The authors discuss Ying Cheng's article that compares fairness in traditional testing with fairness in AI and machine learning. Cheng's important work looks at the entire testing process, not just the final decisions, to understand fairness better. The commentary adds to this by exploring ideas about fairness, including the difference between treating everyone exactly the same (equality) versus giving people what they need to be fair (equity), and how cause-and-effect understanding fits in. Together, these works suggest new ways to study fairness across both psychometrics and AI fields.
Test fairnessAlgorithmic fairnessPsychometricsAI/ML fairnessEqualityEquityCausalityTesting workflowInterdisciplinary researchFairness evaluation
Authors
Youmi Suk
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. Together, the focus article and this commentary point to directions for future fairness research across the psychometrics and AI/ML communities.