2024-11-25-rutherford24a.md

title	abstract	openreview	layout	series	publisher	issn	id	month	tex_title	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling	Reference classes in healthcare establish healthy norms, such as pediatric growth charts of height and weight, and are used to chart deviations from these norms which represent potential clinical risk. How the demographics of the reference class influence clinical interpretation of deviations is unknown. Using normative modeling, a method for building reference classes, we evaluate the fairness (racial bias) in reference models of structural brain images that are widely used in psychiatry and neurology. We test whether including “race” in the model creates fairer models. We predict self-reported race using the deviation scores from three different reference class normative models to better understand bias in an integrated, multivariate sense. Across all these tasks, we uncover racial disparities that are not easily addressed with existing data or commonly used modeling techniques. Our work suggests that deviations from the norm could be due to demographic mismatch with the reference class, and assigning clinical meaning to these deviations should be done with caution. Our approach also suggests that acquiring more representative samples is an urgent research priority.	mako7lcwWF	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	rutherford24a	0	To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling	false	Rutherford, Saige and Wolfers, Thomas and Fraza, Charlotte and Harnett, Nathaniel G. and Beckmann, Christian and Ruhe, Henricus G. and Marquand, Andre	given family Saige Rutherford given family Thomas Wolfers given family Charlotte Fraza given family Nathaniel G. Harnett given family Christian Beckmann given family Henricus G. Ruhe given family Andre Marquand	2024-11-25		Proceedings of the 9th Machine Learning for Healthcare Conference	252	inproceedings	date-parts 2024 11 25	https://raw.githubusercontent.com/mlresearch/v252/main/assets/rutherford24a/rutherford24a.pdf