A new perspective on how this social theory relates to fair machine learning.
Reducing health disparities in clinical decision support with machine learning
We are developing algorithms that can adapt to changing hospital environments in real time and make predictions that are equally accurate among patient subpopulations. We investigate these algorithms for clinical decision support in emergency medicine and other fields.

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🎉 La Cava and Lett’s fair ML tool, Interfair, won first place in the 2023 NIH Challenge, Bias Detection Tools for Clinical Decision Making.
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Selected Papers
Fair admission risk prediction with proportional multicalibration
Proceedings of the Conference on Health, Inference, and Learning
Translating intersectionality to fair machine learning in health sciences
Nature Machine Intelligence