Clinical healthcare has been a natural application domain for ML with a few modest success stories of practical deployment. Inequity and healthcare disparity has long been a concern in clinical and public health for decades. However, the challenges of fair and equitable care using ML in health has largely remained unexplored. While a few works have attempted to highlight potential concerns and pitfalls in recent years, there are massive gaps in academic ML literature in this context. The goal of this workshop is to investigate issues around fairness that are specific to ML based healthcare. We hope to investigate a myriad of questions via the workshop.
Shalmali Joshi (Vector Institute)
Irene Y Chen (MIT)
Irene is a PhD student at MIT focusing on applications on health care and fairness. She did her undergrad at Harvard where I studied applied math and computational engineering. Before starting at MIT, she worked for two years at Dropbox as a data scientist and machine learning engineer.
Ziad Obermeyer (UC Berkeley School of Public Health)
Shems Saleh (Vector Institute)
Sendhil Mullainathan (University of Chicago)
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