AAII Seminar: January 15th at 12pm in E2-215
We kick off the New Year with a seminar by at 12pm in E2-215 or
by Zoom given by:
Speaker: Rich Caruana (Microsoft)
Title: Friends Don’t Let Friends Use Black-Box Models: The Importance of Interpretability in Machine Learning
Abstract: Every data set is flawed, often in ways that are unanticipated and difficult to detect. If you can’t understand what your model learned from the data, your model probably is less accurate than it could be, and might even be risky to deploy. Unfortunately, historically there has been a tradeoff between accuracy and intelligibility: accurate models such as deep neural nets, boosted tress and random forests are not very intelligible, and intelligible models such as linear regression and decision trees usually are less accurate. In mission-critical domains such as healthcare, where being able to understand, validate, edit and ultimately trust a model is important, one often had to choose less accurate models. But this is changing. We have developed a learning method based on generalized additive models with pairwise interactions (GA2Ms) that is as accurate as full complexity models, yet even more interpretable than linear regression. In this talk I’ll show how interpretable, high-accuracy machine learning is helping us discover what our models learned and uncover flaws lurking in our data. I’ll also show how we’re using these models to uncover bias where fairness and transparency are important. Code for GA2Ms is available at https://github.com/interpretML.
Bio: Rich Caruana is a Senior Principal Researcher at Microsoft. His research focus is on intelligible/transparent modeling, machine learning for medical decision making and computational ecology, and deep learning. Before joining Microsoft, Rich was on the faculty in Computer Science at Cornell, at UCLA’s Medical School, and at CMU’s Center for Learning and Discovery. Rich’s Ph.D. is from CMU, where he worked with Tom Mitchell and Herb Simon. His thesis on Multitask Learning helped create interest in the subfield of machine learning called Transfer Learning. Rich received an NSF CAREER Award in 2004 (for Meta Clustering), best paper awards in 2005 (with Alex Niculescu-Mizil), 2007 (with Daria Sorokina), and 2014 (with Todd Kulesza, Saleema Amershi, Danyel Fisher, and Denis Charles), and co-chaired KDD in 2007 with Xindong Wu.