Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning is coming at 03/02/2020 - 4:00pm
Linus Pauling Science Center 125 Mon, 03/02/2020 - 4:00pm Rich Caruana Senior Principal Researcher , Microsoft Abstract: In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible, and the most intelligible models usually are less accurate. This often limits the accuracy of models that can safely be deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a model is important. We have developed a learning method based on generalized additive models (GAMs) that is as accurate as full complexity models, but even more intelligible than linear models. This makes it easy to understand what a model has learned and to edit the model when it learns inappropriate things. In the talk I’ll present several case studies where these high-accuracy GAMs discover surprising patterns in the data that would have made deploying a black-box model risky. I’ll also show how we’re using these models to uncover bias in models where fairness and transparency are important. Every data set is flawed in surprising ways --- you need intelligibility. Bio: Read more: http://eecs.oregonstate.edu/colloquium/friends-don%E2%80%99t-let-friends... [1] [1] http://eecs.oregonstate.edu/colloquium/friends-don%E2%80%99t-let-friends-deploy-black-box-models-importance-intelligibility-machine
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