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We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality.
A value for n-person games
L. S. Shapley · 1953
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The complexity of enumeration and reliability problems
Leslie G. Valiant · 1979
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Fast algorithms for mining association rules in large databases
Rakesh Agrawal and Ramakrishnan Srikant · 1994
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Responsibility and blame: A structural-model approach
Hana Chockler and Joseph Y. Halpern · 2004
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Causes and explanations: A structural-model approach. Part I: Causes
Joseph Y Halpern and Judea Pearl · 2005
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Causality: Models, Reasoning and Inference
Judeea Pearl · 2009
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The complexity of causality and responsibility for query answers and non-answers
Alexandra Meliou, Wolfgang Gatterbauer, Katherine F. Moore, and Dan Suciu · 2010
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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From causes for database queries to repairs and model-based diagnosis and back
Leopoldo E. Bertossi and Babak Salimi · 2017
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A roadmap for a rigorous science of interpretability
Finale Doshi-Velez and Been Kim · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Please stop explaining black box models for high stakes decisions
Cynthia Rudin · 2018
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Towards automatic concept-based explanations
A. Ghorbani, J. Wexler, J. Y. Zou, and B. Kim · 2019
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Explaining classifiers with causal concept effect (cace)
Yash Goyal, Uri Shalit, and Been Kim · 2019
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Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Oluwasanmi Koyejo · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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The Shapley Value of Tuples in Query Answering
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Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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An interpretable model with globally consistent explanations for credit risk
Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, and Tong Wang · 2018
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The mythos of model interpretability
Zachary C. Lipton · 2018
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Ester Livshits, Leopoldo Bertossi, Benny Kimelfeld, and Moshe Sebag · 2020
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From local explanations to global understanding with explainable AI for trees
Scott M Lundberg et al · 2020
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