Rich Caruana – Friends Don’t Let Friends Deploy Black-Box Models

Rich Caruana – Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning

Keynote Lecture at the ML in PL Conference 2021 (https://conference2021.mlinpl.org/)

ML in PL Association (https://mlinpl.org) is a non-profit organization devoted to fostering the machine learning community in Poland and promoting a deep understanding of ML methods. Even though ML in PL is based in Poland, it seeks to provide opportunities for international cooperation.

In machine learning often tradeoffs must be made between accuracy, privacy and intelligibility: the most accurate models usually are not very intelligible or private, and the most intelligible models usually are less accurate. This can limit the accuracy of models that can safely be deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and trust models is important. EBMs (Explainable Boosting Machines) are a recent learning method based on generalized additive models (GAMs) that are as accurate as full complexity models, more intelligible than linear models, and which can be made differentially private with little loss in accuracy. EBMs make 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 case studies where EBMs discover surprising patterns in data that would have made deploying black-box models risky. I’ll also show how we’re using these models to uncover and mitigate bias in models where fairness and transparency are important.