Generalized SHAP: Generating multiple types of explanations in machine
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Generalized SHAP: Generating multiple types of explanations in machine learning

12 June 2020

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Papers citing "Generalized SHAP: Generating multiple types of explanations in machine learning"

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Title
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring Hué Sullivan Hurlin Christophe Pérignon Christophe Saurin Sébastien 72 0 0 12 Dec 2022
Generating Interpretable Poverty Maps using Object Detection in Satellite Images Kumar Ayush Burak Uzkent Marshall Burke David B. Lobell Stefano Ermon 56 82 0 05 Feb 2020
Explainable AI for Trees: From Local Explanations to Global Understanding Scott M. Lundberg G. Erion Hugh Chen A. DeGrave J. Prutkin B. Nair R. Katz J. Himmelfarb N. Bansal Su-In Lee FAtt 97 290 0 11 May 2019
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,090 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 203 3,883 0 10 Apr 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,708 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 206 1,996 0 11 Dec 2014
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination Amit Datta Michael Carl Tschantz Anupam Datta 82 735 0 27 Aug 2014

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