1.4k citations · 3.1k across the 30 of their papers we have counts for
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3 papers · 1 filter
stat.ML2017★ 2 cited
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
Andrew Gordon Wilson, Jason Yosinski, Patrice Simard +2
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017
stat.ML2017★ 124 cited
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
Sarah Tan, Rich Caruana, Giles Hooker +1
Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit suc…
cs.AI2017★ 30 cited
Interpretable & Explorable Approximations of Black Box Models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana +1
We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneou…