Position: The Categorization of Race in ML is a Flawed Premise

Miriam Doh, Benedikt Höltgen, Piera Riccio, Nuria M Oliver
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:81211-81232, 2025.

Abstract

This position paper critiques the reliance on rigid racial taxonomies in machine learning, exposing their U.S.-centric nature and lack of global applicability—particularly in Europe, where race categories are not commonly used. These classifications oversimplify racial identity, erasing the experiences of mixed-race individuals and reinforcing outdated essentialist views that contradict the social construction of race. We suggest research agendas in machine learning that move beyond categorical variables to better address discrimination and social inequality.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-doh25a, title = {Position: The Categorization of Race in {ML} is a Flawed Premise}, author = {Doh, Miriam and H\"{o}ltgen, Benedikt and Riccio, Piera and Oliver, Nuria M}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {81211--81232}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/doh25a/doh25a.pdf}, url = {https://proceedings.mlr.press/v267/doh25a.html}, abstract = {This position paper critiques the reliance on rigid racial taxonomies in machine learning, exposing their U.S.-centric nature and lack of global applicability—particularly in Europe, where race categories are not commonly used. These classifications oversimplify racial identity, erasing the experiences of mixed-race individuals and reinforcing outdated essentialist views that contradict the social construction of race. We suggest research agendas in machine learning that move beyond categorical variables to better address discrimination and social inequality.} }
Endnote
%0 Conference Paper %T Position: The Categorization of Race in ML is a Flawed Premise %A Miriam Doh %A Benedikt Höltgen %A Piera Riccio %A Nuria M Oliver %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-doh25a %I PMLR %P 81211--81232 %U https://proceedings.mlr.press/v267/doh25a.html %V 267 %X This position paper critiques the reliance on rigid racial taxonomies in machine learning, exposing their U.S.-centric nature and lack of global applicability—particularly in Europe, where race categories are not commonly used. These classifications oversimplify racial identity, erasing the experiences of mixed-race individuals and reinforcing outdated essentialist views that contradict the social construction of race. We suggest research agendas in machine learning that move beyond categorical variables to better address discrimination and social inequality.
APA
Doh, M., Höltgen, B., Riccio, P. & Oliver, N.M.. (2025). Position: The Categorization of Race in ML is a Flawed Premise. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:81211-81232 Available from https://proceedings.mlr.press/v267/doh25a.html.

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