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Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model.
Shubham Rathi. 2019 · 1906
Earlier work this paper cites.
Generalized Integrated Gradients: A practical method for explaining diverse ensembles
John W L Merrill, Geoff M Ward, Sean J Kamkar, Jay Budzik, and Douglas C Merrill. 2019 · 1909
Earlier work this paper cites.
Notes on the n-Person Game-II: The Value of an n-Person Game
Lloyd Stowell Shapley. 1951 · 1951
Earlier work this paper cites.
The Shapley Value: Essays in Honor of Lloyd S. Shapley
Alvin E. Roth. 1988 · 1988
Earlier work this paper cites.
Carlos Fernández-Loría, Foster Provost, and Xintian Han. 2021 · 2001
Earlier work this paper cites.
True to the Model or True to the Data?. In ICML ’20 Workshop on Human Interpretability
Hugh Chen, Joseph D Janizek, Scott Lundberg, and Su-In Lee. 2020 · 2006
Earlier work this paper cites.
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards
Masoud Hashemi and Ali Fathi. 2020 · 2008
Earlier work this paper cites.
Modeling wine preferences by data mining from physicochemical properties
Paulo Cortez, António Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009 · 2009
Earlier work this paper cites.
An Efficient Explanation of Individual Classifications using Game Theory
Erik Strumbelj and Igor Kononenko. 2010 · 2010
Earlier work this paper cites.
Counterfactual Explanations for Machine Learning: A Review
Sahil Verma, Arthur Ai, John Dickerson, and Keegan Hines. 2020 · 2010
Earlier work this paper cites.
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In Proceedings of the 30th International Conference on International Conference on Machine Learning, ICML . I–115–I–123
James Bergstra, Daniel Yamins, and David Cox. 2013 · 2013
Earlier work this paper cites.
XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD . 785–794
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
"Why Should I Trust You?". In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD . 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
Supplementary Material to a Unified Approach to Interpreting Model Predictions: The Monotonicity Axiom Implies the Symmetry Axiom for Shapley Values
Scott Lundberg. 2017 · 2017
Earlier work this paper cites.
A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems, NeurIPS . 4768––4777
Scott M Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada. 2018 · 2018
Earlier work this paper cites.
Local Rule-Based Explanations of Black Box Decision Systems
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, and Fosca Giannotti. 2018 · 2018
Earlier work this paper cites.
Interpretation of Kernel SHAP and Its Hyperparameters - Issue #23 https://github.com/slundberg/shap
GitHub Issues. 2018 · 2018
Earlier work this paper cites.
Model Agnostic Supervised Local Explanations. In Advances in Neural Information Processing Systems, NeurIPS . 2520––2529
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar. 2018 · 2018
Earlier work this paper cites.
12 CFR Part 1002 - Equal Credit Opportunity Act (Regulation B)
U.S. Congress. 2018 · 2018
Earlier work this paper cites.
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2018 · 2018
Earlier work this paper cites.
Ethics Guidelines for Trustworthy AI
High-Level Expert Group on Artificial Intelligence European Commission. 2019 · 2019
Earlier work this paper cites.
Explainable Machine Learning Challenge
FICO Community. 2019 · 2019
Cited alongside, same era.
A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2019 · 2019
Cited alongside, same era.
Lending Club Loan Data
Kaggle. 2019 · 2019
Cited alongside, same era.
Faithful and Customizable Explanations of Black Box Models. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, FAccT . 131–138
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec. 2019 · 2019
Cited alongside, same era.
The Dangers of Post-Hoc Interpretability: Unjustified Counterfactual Explanations. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI . 2801–2807
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2019 · 2019
Cited alongside, same era.
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses. In Advances in Neural Information Processing Systems, NeurIPS . 12187–12198
Kaivalya Rawal and Himabindu Lakkaraju. 2020 · 2020
Later among the works it cites.
CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES . 166–172
Shubham Sharma, Jette Henderson, and Joydeep Ghosh. 2020 · 2020
Later among the works it cites.
The Many Shapley Values for Model Explanation. In Proceedings of the 37th International Conference on Machine Learning . 9269–9278
Mukund Sundararajan and Amir Najmi. 2020 · 2020
Later among the works it cites.
Explaining Individual Predictions When Features Are Dependent: More Accurate Approximations to Shapley Values
Kjersti Aas, Martin Jullum, and Anders Løland. 2021 · 2021
Closest in time.
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Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller. 2019 · 2019
Cited alongside, same era.
Efficient Search for Diverse Coherent Explanations. In Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, FAccT . 20–28
Chris Russell. 2019 · 2019
Cited alongside, same era.
Actionable Recourse in Linear Classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAccT . 10–19
Berk Ustun, Alexander Spangher, and Yang Liu. 2019 · 2019
Cited alongside, same era.
Measurable Counterfactual Local Explanations for Any Classifier. In Proceedings of the 24th European Conference on Artificial Intelligence, ECAI . 2529–2535
Adam White and Artur d’Avila Garcez. 2019 · 2019
Cited alongside, same era.
Relation-Based Counterfactual Explanations for Bayesian Network Classifiers. In Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI . 451–457
Emanuele Albini, Antonio Rago, Pietro Baroni, and Francesca Toni. 2020 · 2020
Cited alongside, same era.
The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAccT . 80–89
Solon Barocas, Andrew D Selbst, and Manish Raghavan. 2020 · 2020
Cited alongside, same era.
Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2020 · 2020
Cited alongside, same era.
Emanuele Albini, Antonio Rago, Pietro Baroni, and Francesca Toni. 2021 · 2021
Closest in time.
FIMAP: Feature Importance by Minimal Adversarial Perturbation
Matt Chapman-Rounds, Umang Bhatt, Erik Pazos, Marc-Andre Schulz, and Konstantinos Georgatzis. 2021 · 2021
Closest in time.
Argumentative XAI: A Survey. In Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI , Vol. 5. 4392–4399
Kristijonas Čyras, Antonio Rago, Emanuele Albini, Pietro Baroni, and Francesca Toni. 2021 · 2021
Closest in time.
Text Counterfactuals via Latent Optimization and Shapley-Guided Search. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 5578–5593
Xiaoli Fern and Quintin Pope. 2021 · 2021
Closest in time.
Shapley Explainability on the Data Manifold. In Proceedings of the 9th International Conference on Learning Representations (ICLR) . 14
Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, and Ilya Feige. 2021 · 2021
Closest in time.
Algorithmic Recourse: from Counterfactual Explanations to Interventions. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT . 353––362
Amir-Hossein Karimi, Eth Zürich, Switzerland Bernhard Schölkopf, and Isabel Valera. 2021 · 2021
Closest in time.
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques. In Proceeding of the 30th International Joint Conference on Artificial Intelligence, IJCAI . 4466–4474
Mark T Keane, Eoin M Kenny, Eoin Delaney, and Barry Smyth. 2021 · 2021
Closest in time.
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, AIES . 652–663
R. K. Mothilal, Divyat Mahajan, Chenhao Tan, and Amit Sharma. 2021 · 2021
Closest in time.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms. In Benchmark & Data Sets Track at the 36th Conference on Neural Information Processing Systems, NeurIPS
Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel, Tobias Richter, and Gjergji Kasneci. 2021 · 2021
Closest in time.
Tabular Data: Deep Learning Is Not All You Need
Ravid Shwartz-Ziv and Amitai Armon. 2021 · 2021
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Barry Smyth and Mark T. Keane. 2021 · 2021
Closest in time.
Thomas Spooner, Danial Dervovic, Jason Long, Jon Shepard, Jiahao Chen, and Daniele Magazzeni. 2021 · 2021
Closest in time.
A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martin Pereira-Farina. 2021 · 2021
Closest in time.
The Case for Interpretable Models in Credit Underwriting
Agus Sudjianto and Scott Zoldi. 2021 · 2021
Closest in time.
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. In Proocedings of the 24th International Conference on Artificial Intelligence and Statistics, AISTATS
Jiaxuan Wang, Jenna Wiens, and Scott Lundberg. 2021 · 2021
Closest in time.
On the Fairness of Causal Algorithmic Recourse. In Proceedings of the 36th AAAI Conference on Artificial Intelligence
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Schölkopf. 2022 · 2022
Closest in time.
Rational Shapley Values. In Proceedings of the 2022 Conference on Fairness, Accountability, and Transparency, FAccT
David S. Watson. 2022 · 2022
Closest in time.