Despite the importance of human health, we do not fundamentally understand what it means to be healthy. Health is unlike many recent machine learning success stories—e.g., games or driving—because there are no agreed-upon, well-defined objectives. In this talk, Marzyeh Ghassemi discusses the role of machine learning in health, argues that the demand for model interpretability is dangerous, and explains why models used in health settings must also be “healthy.” She focuses on a progression of work that encompasses prediction, time series analysis, and representation learning.
Talk title
“Don’t expl-AI-n yourself: exploring ‘healthy’ models in machine learning for health”
Recommended readings
M. Ghassemi et al., “A Review of Challenges and Opportunities in Machine Learning for Health.” AMIA Summits on Translational Science Proceedings, 2020
H. Suresh et al., “Clinical Intervention Prediction and Understanding Using Deep Networks.” Machine Learning for Healthcare Conference, 2017. pp. 322-337.
G. Liu et al., “Clinically Accurate Chest X-Ray Report Generation.” Machine Learning for Healthcare Conference, 2019. pp. 249-269.
I. Y. Chen et al., “Can AI Help Reduce Disparities in General Medical and Mental Health Care?” AMA Journal of Ethics, 21(2), 167-179. 2019.
H. Zhang et al., “Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings.” Proceedings of the ACM Conference on Health, Inference, and Learning, 2020. pp. 110-120.
M. Ghassemi et al., “ClinicalVis: Supporting Clinical Task-Focused Design Evaluation.” Google Brain Demo, 2018.
About Marzyeh Ghassemi
Marzyeh Ghassemi is an assistant professor at the University of Toronto in computer science and medicine, and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She will be moving to MIT's EECS/IMES in July 2021.
She has served as a NeurIPS 2019/2020 Workshop Co-Chair, and General Chair for the ACM Conference on Health, Inference and Learning (ACM CHIL). Previously, she was a Visiting Researcher with Alphabet's Verily and a post-doc with Peter Szolovits at MIT. Prior to her PhD in computer science at MIT, Ghassemi received an MSc degree in biomedical engineering from Oxford University as a Marshall Scholar, and BS degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.
Her work has been featured in popular press such as MIT News, NVIDIA, and Huffington Post. She was also recently named one of MIT Tech Review’s 35 Innovators Under 35.
About the SRI Seminar Series
The SRI Seminar Series brings together the Schwartz Reisman community and beyond for a robust exchange of ideas that advance scholarship at the intersection of technology and society. Seminars are led by a leading or emerging scholar and feature extensive discussion.
Each week, a featured speaker will present for 45 minutes, followed by 45 minutes of discussion. Registered attendees will be emailed a Zoom link approximately one hour before the event begins. The event will be recorded and posted online.
Marzyeh Ghassemi