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Activity Number: 316 - Late-Breaking Session: Statistical Issues in Application of Machine Learning to High-Stakes Decisions
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: JSM Partner Societies
Abstract #333068
Title: Interpretable Machine Learning for High-Stakes Decisions
Author(s): Cynthia Rudin*
Companies: Duke University

What if computation was not an obstacle to constructing sparse, accurate models? With new optimization-based techniques for interpretable machine learning, it is not as much of an obstacle as before. I will discuss construction of optimal rule lists (decision trees), scoring systems, and matching algorithms. I will show that it is possible to create interpretable models that are just as accurate as black box models used throughout the criminal justice system for predicting recidivism.

Authors who are presenting talks have a * after their name.

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