Title: F-AI-Rest of Them All
Abstract: While clinical AI and medical risk scores have received much attention for their potential to achieve above-human performance, there are many concerns about their ability to mimic societal bias. In this talk, Dr. Ghassemi explores the difficulty of making state-of-the-art machine learning models behave as we say, not as we do, and how technical choices that seems natural in other settings may not work well in health.
Bio: Dr. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She holds a Herman L. F. von Helmholtz Career Development Professorship, and was also named one of MIT Tech Review’s 35 Innovators Under 35. Previously, she was a Visiting Researcher with Alphabet’s Verily and an Assistant Professor at University of Toronto. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.
Professor Ghassemi currently serves as a NeurIPS Workshop Co-Chair, and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). Professor Ghassemi has a well-established academic track record across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, EMBC, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as MIT News, NVIDIA, Huffington Post.