Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning is coming at 02/11/2019 - 4:00pm
Weniger Hall 151 Mon, 02/11/2019 - 4:00pm Rich Caruana Principal Researcher, Microsoft Abstract: In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models (deep nets, boosted trees and random forests) usually are not very intelligible, and the most intelligible models (logistic regression, small trees and decision lists) usually are less accurate. This tradeoff limits the accuracy of models that can be safely deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a learned model is important. We have developed a learning method based on generalized additive models (GA2Ms) that is as accurate as full complexity models, but more intelligible than linear models. In this talk I'll present a case study where intelligibility is critical to uncover 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 detect bias in domains where fairness and transparency are paramount, and how these models can be used to understand what is learned by black-box models such as deep nets. Bio: Read more: http://eecs.oregonstate.edu/colloquium/friends-dont-let-friends-deploy-b... [1] [1] http://eecs.oregonstate.edu/colloquium/friends-dont-let-friends-deploy-black-box-models-importance-intelligibility-machine
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