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)
Sendhil Mullainathan (University of Chicago)
More from the Same Authors
2019 Workshop: Machine Learning for Health (ML4H): What makes machine learning in medicine different? »
Andrew Beam · Tristan Naumann · Brett Beaulieu-Jones · Madalina Fiterau · Irene Y Chen · Samuel Finlayson · Emily Alsentzer · Adrian Dalca · Matthew McDermott
2018 Workshop: Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare »
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca · Irene Y Chen
2018 Workshop: Workshop on Ethical, Social and Governance Issues in AI »
Chloe Bakalar · Sarah Bird · Tiberio Caetano · Edward W Felten · Dario Garcia · Isabel Kloumann · Finnian Lattimore · Sendhil Mullainathan · D. Sculley