The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute Health Equity Interest Group, and the Department of Statistical Science at University College London.

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We are excited to announce the next instalment of our webinar series with speaker Professor Irene Chen from University of California, Berkeley.

Specific details on the topic are as below:

Topic: 

LLMs, Bias, and the Implications for Equitable Healthcare

Abstract:

Large language models (LLMs) have demonstrated impressive capabilities, but also exhibit concerning biases that can perpetuate harm. This is especially problematic in high-stakes domains like healthcare, where biases can severely impact equitable access and quality of care. In this talk, Irene will discuss two recent projects examining the implications of LLMs on equitable healthcare. First, she will present work designing guiding principles for LLMs in maternal health through participatory design with healthcare workers, women, and birthing people. Next, Irene will demonstrate how LLMs can provide insight into medication switching for contraceptives using clinical notes from UCSF. The talk concludes with a discussion about the implications of LLMs in the existing landscape of bias in medical AI.

Speaker Biography:

Professor Irene Chen studies machine learning for equitable healthcare. Her research focuses on two main areas: 1) developing machine learning methods for equitable clinical care, and 2) auditing and addressing algorithmic bias.

Irene Chen is an Assistant Professor in UC Berkeley and UCSF’s Computational Precision Health program with a joint appointment in Berkeley EECS, and she is a member of Berkeley AI Research (BAIR). She received her PhD from MIT EECS and her joint AB/SM in Applied Math from Harvard University. She has worked at Dropbox and Microsoft Research.

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Please direct any questions about the webinar series to Dr Brieuc Lehmann at b.lehmann@ucl.ac.uk.