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Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence.
A value for n-person games
Lloyd S Shapley · 1953
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Causation
David Lewis · 1974
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Norm theory: Comparing reality to its alternatives
Daniel Kahneman and Dale T Miller · 1986
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Apportioning causal responsibility
Elliott Sober · 1988
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Causal diagrams for empirical research
Judea Pearl · 1995
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Crediting causality
Barbara A Spellman · 1997
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Analysis of regression in game theory approach
Stan Lipovetsky and Michael Conklin · 2001
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Chain graph models and their causal interpretations
Steffen L Lauritzen and Thomas S Richardson · 2002
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A causal-model theory of conceptual representation and categorization
Bob Rehder · 2003
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Causal models: How people think about the world and its alternatives
Steven Sloman · 2005
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Noisy Newtons: Unifying process and dependency accounts of causal attribution
Tobias Gerstenberg, Noah Goodman, David Lagnado, and Joshua Tenenbaum · 2012
Earlier work this paper cites.
The reciprocal interaction between obesity and obstructive sleep apnoea
Chong Weng Ong, Denise M O’Driscoll, Helen Truby, Matthew T Naughton, and Garun S Hamilton · 2013
Earlier work this paper cites.
Event labeling combining ensemble detectors and background knowledge
Hadi Fanaee-T and Joao Gama · 2014
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
Cited alongside, same era.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
Cited alongside, same era.
“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
EU General Data Protection Regulation (GDPR): Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/EC (General Data Protection Regulation), OJ 2016 L 119/1, 2016
European Union · 2016
Cited alongside, same era.
Slave to the algorithm: Why a right to an explanation is probably not the remedy you are looking for
The explanation game: Explaining machine learning models with cooperative game theory
Luke Merrick and Ankur Taly · 2019
Later among the works it cites.
Explaining explanations in AI
Brent Mittelstadt, Chris Russell, and Sandra Wachter · 2019
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Kjersti Aas, Martin Jullum, and Anders Løland · 2019
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Causal calculus in the presence of cycles, latent confounders and selection bias
Patrick Forré and Joris M Mooij · 2019
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Asymmetric Shapley values: Incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Ilya Feige, and Colin Rowat · 2019
Later among the works it cites.
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Lilian Edwards and Michael Veale · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Causal explanation
Tania Lombrozo and Nadya Vasilyeva · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
Cited alongside, same era.
Kjersti Aas, Martin Jullum, and Anders Løland · 2019
Cited alongside, same era.
Asymmetric Shapley values: Incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Ilya Feige, and Colin Rowat · 2019
Cited alongside, same era.
Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley · 2020
Closest in time.
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing, Lenon Minorics, and Patrick Blöbaum · 2020
Closest in time.
Interpreting interpretability: Understanding data scientists’ use of interpretability yools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2020
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Problems with Shapley-value-based explanations as feature importance measures
I Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
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From local explanations to global understanding with explainable AI for trees
Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee · 2020
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Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing, Lenon Minorics, and Patrick Blöbaum · 2020
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Challenges and opportunities with causal discovery algorithms: Application to Alzheimer’s pathophysiology
Xinpeng Shen, Sisi Ma, Prashanthi Vemuri, and Gyorgy Simon · 2020
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