Perspective: Machine Learning for Health Should Consider Social Drivers of Health

Neha Srivathsa, Sherri Rose
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1637-1644, 2026.

Abstract

Clinical machine learning (ML) algorithms can exacerbate a wide range of injustices across multiple domains and levels of society. These harms are often underemphasized and differentially distributed, with minoritized communities disproportionately experiencing the harms and not the benefits of health ML algorithms. By proposing a correspondence between prominent algorithmic harm and social drivers of health (SDOH) frameworks, we show that a range of algorithmic harms ultimately impact human health through SDOH factors, especially structural factors. This presents an inherent tension in the development of ML for health, where the harms of algorithms may lead to the worsening of health inequities. We recommend the consideration of SDOH throughout the pipeline of ML system development for examining algorithmic harms to health. Effectively considering SDOH necessitates developing competencies in structural analysis and community-engaged approaches. Accounting for SDOH could illuminate pathways toward equity-promoting algorithms, although we highlight that, in many cases, an equity-promoting algorithm may not exist. Thus, we also emphasize the need for interventions on the root causes of health inequities.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-srivathsa26a, title = {Perspective: Machine Learning for Health Should Consider Social Drivers of Health}, author = {Srivathsa, Neha and Rose, Sherri}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1637--1644}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/srivathsa26a/srivathsa26a.pdf}, url = {https://proceedings.mlr.press/v297/srivathsa26a.html}, abstract = {Clinical machine learning (ML) algorithms can exacerbate a wide range of injustices across multiple domains and levels of society. These harms are often underemphasized and differentially distributed, with minoritized communities disproportionately experiencing the harms and not the benefits of health ML algorithms. By proposing a correspondence between prominent algorithmic harm and social drivers of health (SDOH) frameworks, we show that a range of algorithmic harms ultimately impact human health through SDOH factors, especially structural factors. This presents an inherent tension in the development of ML for health, where the harms of algorithms may lead to the worsening of health inequities. We recommend the consideration of SDOH throughout the pipeline of ML system development for examining algorithmic harms to health. Effectively considering SDOH necessitates developing competencies in structural analysis and community-engaged approaches. Accounting for SDOH could illuminate pathways toward equity-promoting algorithms, although we highlight that, in many cases, an equity-promoting algorithm may not exist. Thus, we also emphasize the need for interventions on the root causes of health inequities.} }
Endnote
%0 Conference Paper %T Perspective: Machine Learning for Health Should Consider Social Drivers of Health %A Neha Srivathsa %A Sherri Rose %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-srivathsa26a %I PMLR %P 1637--1644 %U https://proceedings.mlr.press/v297/srivathsa26a.html %V 297 %X Clinical machine learning (ML) algorithms can exacerbate a wide range of injustices across multiple domains and levels of society. These harms are often underemphasized and differentially distributed, with minoritized communities disproportionately experiencing the harms and not the benefits of health ML algorithms. By proposing a correspondence between prominent algorithmic harm and social drivers of health (SDOH) frameworks, we show that a range of algorithmic harms ultimately impact human health through SDOH factors, especially structural factors. This presents an inherent tension in the development of ML for health, where the harms of algorithms may lead to the worsening of health inequities. We recommend the consideration of SDOH throughout the pipeline of ML system development for examining algorithmic harms to health. Effectively considering SDOH necessitates developing competencies in structural analysis and community-engaged approaches. Accounting for SDOH could illuminate pathways toward equity-promoting algorithms, although we highlight that, in many cases, an equity-promoting algorithm may not exist. Thus, we also emphasize the need for interventions on the root causes of health inequities.
APA
Srivathsa, N. & Rose, S.. (2026). Perspective: Machine Learning for Health Should Consider Social Drivers of Health. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1637-1644 Available from https://proceedings.mlr.press/v297/srivathsa26a.html.

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