Irene Chen

Irene Chen is an Assistant Professor at UC Berkeley and UCSF, where she develops methods for trustworthy and reliable AI. Her research spans clinical decision-making, fairness and inequality in machine learning, and rigorous evaluation of increasingly advanced AI systems. Prof. Chen’s work has been recognized with best paper and best poster awards, honors from Google and Apple, and Rising Star awards in EECS, Machine Learning, and Data Science. Her research has been published in leading machine learning conferences, including NeurIPS, ICML, and AAAI, and medical journals, including Nature Medicine, NEJM AI, and The Lancet Digital Health. Her work has also been covered by MIT Technology Review, NPR/WGBH, and STAT. She received her PhD in EECS from MIT and her joint AB/SM in Applied Mathematics from Harvard.

AI2050 Project

The danger of AI used in important decisions is not only that it may fail, but that it may work just well enough that its mistakes are easy to miss. Chen’s project focuses on how to find these hidden failures by studying what happens when people use AI in the real world: when they correct it, question it, work around it, or report that something has gone wrong.