Hostname: page-component-89b8bd64d-5bvrz Total loading time: 0 Render date: 2026-05-10T17:11:24.577Z Has data issue: false hasContentIssue false
  • English
  • Français
Accepted manuscript

Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?

Published online by Cambridge University Press:  14 April 2026

Ying Cheng Open the ORCID record for Ying Cheng [Opens in a new window]
Rights & Permissions [Opens in a new window]

Accessibility of Accepted Manuscripts

Accepted Manuscripts are early, peer-reviewed versions that have not yet been copyedited, typeset, or formally published and may not meet all accessibility standards. A fully formatted accessible version will follow.

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

Type
Theory & Methods – Focus Article
Information
Psychometrika , Accepted manuscript , pp. 1 - 55
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of Psychometric Society