Towards Responsibly Deploying Machine Learning in Healthcare
Abstract Machine learning is a promising tool for making healthcare cheap, fast, and accessible. But despite growing health data and some high-performance models, current methods remain surprisingly biased, fragile, and impractical. This talk will cover some of my recent work on filling these important gaps, taking steps towards machine learning that can be broadly and responsibly deployed in healthcare. First, we will discuss detecting, mitigating, and leveraging implicit bias in large language models to enable their equitable use. Second, we will discuss model editing, an exciting path towards keeping large, expensively-trained models up-to-date in quickly-changing environments without retraining.
Tom Hartvigsen is an Assistant Professor of Data Science at the University of Virginia. He works to make machine learning trustworthy, robust, and socially responsible enough for deployment in high-stakes, dynamic settings. Tom’s research has been published at many major peer-reviewed venues in Machine Learning, Natural Language Processing, and Data Mining. He is active in the machine learning community, serving as the General Chair for the Machine Learning for Health Symposium in 2023, helping organize the 2023 Conference on Health, Informatics, and Learning, and co-chairing workshops on time series and generative AI at NeurIPS’22 and ICML’23.
Prior to joining UVA, Tom was a Postdoctoral Associate at MIT’s Computer Science and Artificial Intelligence Laboratory. He holds a Ph.D. and M.S. in Data Science from Worcester Polytechnic Institute and a B.A. in Applied Math from SUNY Geneseo.