{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:21:25Z","timestamp":1774628485383,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":93,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,8]]},"DOI":"10.1145\/3600211.3604676","type":"proceedings-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T18:41:37Z","timestamp":1693334497000},"page":"411-431","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2964-4638","authenticated-orcid":false,"given":"Emanuele","family":"Albini","sequence":"first","affiliation":[{"name":"J.P. Morgan AI Research, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0763-6223","authenticated-orcid":false,"given":"Shubham","family":"Sharma","sequence":"additional","affiliation":[{"name":"J.P. Morgan AI Research, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2469-2294","authenticated-orcid":false,"given":"Saumitra","family":"Mishra","sequence":"additional","affiliation":[{"name":"J.P. Morgan AI Research, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-561X","authenticated-orcid":false,"given":"Danial","family":"Dervovic","sequence":"additional","affiliation":[{"name":"J.P. Morgan AI Research, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1934-3447","authenticated-orcid":false,"given":"Daniele","family":"Magazzeni","sequence":"additional","affiliation":[{"name":"J.P. Morgan AI Research, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103502"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533168"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/63"},{"key":"e_1_3_2_1_4_1","volume-title":"Influence-Driven Explanations for Bayesian Network Classifiers. In PRICAI 2021: Trends in Artificial Intelligence(Lecture Notes in Computer Science). Springer International Publishing, 88\u2013100","author":"Albini Emanuele","year":"2021","unstructured":"Emanuele Albini, Antonio Rago, Pietro Baroni, and Francesca Toni. 2021. Influence-Driven Explanations for Bayesian Network Classifiers. In PRICAI 2021: Trends in Artificial Intelligence(Lecture Notes in Computer Science). Springer International Publishing, 88\u2013100."},{"key":"e_1_3_2_1_5_1","first-page":"317","article-title":"Weighted Voting Doesn\u2019t Work: A Mathematical Analysis","volume":"19","author":"Banzhaf John","year":"1964","unstructured":"John F.\u00a0III Banzhaf. 1964. Weighted Voting Doesn\u2019t Work: A Mathematical Analysis. Rutgers Law Review 19 (1964), 317.","journal-title":"Rutgers Law Review"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372830"},{"key":"e_1_3_2_1_7_1","volume-title":"Adult Dataset: Extract of 1994 U.S. Income Census.","author":"Becker Barry","year":"1994","unstructured":"Barry Becker. 1994. Adult Dataset: Extract of 1994 U.S. Income Census."},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning. PMLR, 115\u2013123","author":"Bergstra James","year":"2013","unstructured":"James Bergstra, Daniel Yamins, and David Cox. 2013. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In Proceedings of the 30th International Conference on Machine Learning. PMLR, 115\u2013123."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42786-020-00020-3"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI). 3016\u20133022","author":"Bhatt Umang","unstructured":"Umang Bhatt, Adrian Weller, and Jos\u00e9 M.\u00a0F. Moura. 2020. Evaluating and Aggregating Feature-based Model Explanations. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI). 3016\u20133022."},{"key":"e_1_3_2_1_11_1","unstructured":"U.S. Consumer Financial Protection\u00a0Bureau CFPB. 2018. Equal Credit Opportunity Act (Regulation B) 12 CFR Part 1002."},{"key":"e_1_3_2_1_12_1","volume-title":"ICML Workshop on Human Interpretability in Machine Learning. arxiv:2006","author":"Chen Hugh","year":"2020","unstructured":"Hugh Chen, Joseph\u00a0D. Janizek, Scott Lundberg, and Su-In Lee. 2020. True to the Model or True to the Data?. In ICML Workshop on Human Interpretability in Machine Learning. arxiv:2006.16234"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_15_1","volume-title":"Surveys & Meta-Analyses Workshop.","author":"Covert C","year":"2020","unstructured":"Ian\u00a0C Covert, Scott Lundberg, and Su-In Lee. 2020. Feature Removal Is A Unifying Principle For Model Explanation Methods. In NeurIPS ML-Retrospectives, Surveys & Meta-Analyses Workshop."},{"key":"e_1_3_2_1_16_1","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI. Vol.\u00a012269","author":"Dandl Susanne","unstructured":"Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl. 2020. Multi-Objective Counterfactual Explanations. In Parallel Problem Solving from Nature \u2013 PPSN XVI. Vol.\u00a012269. Springer International Publishing, 448\u2013469."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01753239"},{"key":"e_1_3_2_1_18_1","volume-title":"Political and Related Models","author":"Deegan John","unstructured":"John Deegan and Edward\u00a0W. Packel. 1983. To the (Minimal Winning) Victors Go the (Equally Divided) Spoils: A New Power Index for Simple n-Person Games. In Political and Related Models. Springer, 239\u2013255."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219198917500128"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33607-3_10"},{"key":"e_1_3_2_1_21_1","unstructured":"FICO Community. 2019. Explainable Machine Learning Challenge."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 9th International Conference on Learning Representations (ICLR).","author":"Frye Christopher","year":"2021","unstructured":"Christopher Frye, Damien de Mijolla, Tom Begley, Laurence Cowton, Megan Stanley, and Ilya Feige. 2021. Shapley Explainability on the Data Manifold. In Proceedings of the 9th International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3119110"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3458455"},{"key":"e_1_3_2_1_25_1","unstructured":"GDPR. 2016. 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\u00a095\/46\/EC (General Data Protection Regulation)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013681"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"e_1_3_2_1_28_1","volume-title":"Counterfactual Explanations and How to Find Them: Literature Review and Benchmarking. Data Mining and Knowledge Discovery","author":"Guidotti Riccardo","year":"2022","unstructured":"Riccardo Guidotti. 2022. Counterfactual Explanations and How to Find Them: Literature Review and Benchmarking. Data Mining and Knowledge Discovery (2022)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Joseph\u00a0Y. Halpern. 2016. Actual Causality.","DOI":"10.7551\/mitpress\/10809.001.0001"},{"key":"e_1_3_2_1_30_1","unstructured":"Goerge\u00a0Charles Harsanyi. 1958. A Bargaining Model for the Cooperatiove N-Person Game. Ph.\u00a0D. Dissertation."},{"key":"e_1_3_2_1_31_1","unstructured":"Manfred\u00a0J. Holler. 1978. A Priori Party Party Power and Government Formation: Esimerkkin\u00e4 Suomi."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01283881"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 2907\u20132916","author":"Janzing Dominik","year":"2020","unstructured":"Dominik Janzing, Lenon Minorics, and Patrick Bloebaum. 2020. Feature Relevance Quantification in Explainable AI: A Causal Problem. In Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 2907\u20132916."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0088-2"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR","author":"Kanamori Kentaro","year":"2022","unstructured":"Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Yuichi Ike. 2022. Counterfactual Explanation Trees: Transparent and Consistent Actionable Recourse with Decision Trees. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 1846\u20131870."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR, 969\u2013979","author":"Karczmarz Adam","year":"2022","unstructured":"Adam Karczmarz, Tomasz Michalak, Anish Mukherjee, Piotr Sankowski, and Piotr Wygocki. 2022. Improved Feature Importance Computation for Tree Models Based on the Banzhaf Value. In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR, 969\u2013979."},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the 23rd International Conference on. Artificial Intelligence and Statistics (AISTATS).","author":"Karimi Amir-Hossein","year":"2020","unstructured":"Amir-Hossein Karimi, G. Barthe, B. Balle, and Isabel Valera. 2020. Model-Agnostic Counterfactual Explanations for Consequential Decisions. In Proceedings of the 23rd International Conference on. Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_1_38_1","volume-title":"A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations. Comput. Surveys 55, 5","author":"Karimi Amir-Hossein","year":"2022","unstructured":"Amir-Hossein Karimi, Gilles Barthe, Bernhard Sch\u00f6lkopf, and Isabel Valera. 2022. A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations. Comput. Surveys 55, 5 (2022), 95:1\u201395:29."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3495747"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/609"},{"key":"e_1_3_2_1_41_1","volume-title":"Rank Correlation Methods","author":"Kendall Maurice","unstructured":"Maurice Kendall and Jean\u00a0D. Gibbons. 1990. Rank Correlation Methods (fifth ed.). A Charles Griffin Title."},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence 35","author":"M.","year":"2021","unstructured":"Eoin\u00a0M. Kenny and Mark\u00a0T. Keane. 2021. On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 13 (2021), 11575\u201311585."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Satyapriya Krishna Tessa Han Alex Gu Javin Pombra Shahin Jabbari Steven Wu and Himabindu Lakkaraju. 2022. The Disagreement Problem in Explainable Machine Learning: A Practitioner\u2019s Perspective. arxiv:2202.01602","DOI":"10.21203\/rs.3.rs-2963888\/v1"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525447"},{"key":"e_1_3_2_1_45_1","volume-title":"Combining Counterfactuals With Shapley Values To Explain Image Models. In ICML 2022 Workshop on Responsible Decision Making in Dynamic Environments. arXiv. arxiv:2206","author":"Lahiri Aditya","year":"2022","unstructured":"Aditya Lahiri, Kamran Alipour, Ehsan Adeli, and Babak Salimi. 2022. Combining Counterfactuals With Shapley Values To Explain Image Models. In ICML 2022 Workshop on Responsible Decision Making in Dynamic Environments. arXiv. arxiv:2206.07087"},{"key":"e_1_3_2_1_46_1","unstructured":"Jana Lang Martin Giese Winfried Ilg and Sebastian Otte. 2022. Generating Sparse Counterfactual Explanations For Multivariate Time Series. arxiv:2206.00931"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/388"},{"key":"e_1_3_2_1_48_1","unstructured":"Lending Club. 2019. Lending Club Loans."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"e_1_3_2_1_50_1","volume-title":"Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS). 4768\u20134777","author":"Lundberg M","year":"2017","unstructured":"Scott\u00a0M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS). 4768\u20134777."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103655"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-57321-8_2"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387166"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462597"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_2_1_57_1","volume-title":"Osborne and Ariel Rubinstein","author":"J.","year":"1994","unstructured":"Martin\u00a0J. Osborne and Ariel Rubinstein. 1994. A Course in Game Theory. MIT Press."},{"key":"e_1_3_2_1_58_1","unstructured":"Ioannis Papantonis and Vaishak Belle. 2022. Principled Diverse Counterfactuals in Multilinear Models. arxiv:2201.06467"},{"key":"e_1_3_2_1_59_1","volume-title":"Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR, 809\u2013818","author":"Pawelczyk Martin","year":"2020","unstructured":"Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci. 2020. On Counterfactual Explanations under Predictive Multiplicity. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR, 809\u2013818."},{"key":"e_1_3_2_1_60_1","volume-title":"Proceedings of the 11th International Conference on Learning Representations (ICLR)","author":"Pawelczyk Martin","year":"2022","unstructured":"Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse. In Proceedings of the 11th International Conference on Learning Representations (ICLR) 2023. arxiv:2203.06768"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.2307\/2981392"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-69291-1"},{"key":"e_1_3_2_1_63_1","volume-title":"Proceedings of the British Machine Vision Conference (BMVC). arXiv. arxiv:1806","author":"Petsiuk Vitali","year":"2018","unstructured":"Vitali Petsiuk, Abir Das, and Kate Saenko. 2018. RISE: Randomized Input Sampling for Explanation of Black-box Models. In Proceedings of the British Machine Vision Conference (BMVC). arXiv. arxiv:1806.07421"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375850"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_66_1","volume-title":"Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. In 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE, 1036\u20131045","author":"Rodriguez Pau","year":"2021","unstructured":"Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin, and David Vazquez. 2021. Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. In 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE, 1036\u20131045."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"crossref","unstructured":"Fabrizio Russo and Francesca Toni. 2022. Causal Discovery and Injection for Feed-Forward Neural Networks. arxiv:2205.09787","DOI":"10.3233\/FAIA230495"},{"key":"e_1_3_2_1_68_1","volume-title":"Brain Informatics(Lecture Notes in Computer Science)","author":"Sarica Alessia","unstructured":"Alessia Sarica, Andrea Quattrone, and Aldo Quattrone. 2022. Introducing the Rank-Biased Overlap as Similarity Measure for Feature Importance in Explainable Machine Learning: A Case Study on Parkinson\u2019s Disease. In Brain Informatics(Lecture Notes in Computer Science). Springer International Publishing, 129\u2013139."},{"key":"e_1_3_2_1_69_1","article-title":"The Intuitive Appeal of Explainable Machines","volume":"87","author":"Selbst D.","year":"2018","unstructured":"Andrew\u00a0D. Selbst and Solon Barocas. 2018. The Intuitive Appeal of Explainable Machines. Fordham Law Review 87, 1085 (2018).","journal-title":"Fordham Law Review"},{"key":"e_1_3_2_1_70_1","volume-title":"U.S. Air Force","author":"Shapley Lloyd\u00a0Stowell","year":"1951","unstructured":"Lloyd\u00a0Stowell Shapley. 1951. Notes on the N-Person Game-II: The Value of an n-Person Game. Project Rand, U.S. Air Force (1951)."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.2307\/1951053"},{"key":"e_1_3_2_1_72_1","unstructured":"Shubham Sharma Alan\u00a0H. Gee Jette Henderson and Joydeep Ghosh. 2022. FASTER-CE: Fast Sparse Transparent and Robust Counterfactual Explanations. arxiv:2210.06578"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375812"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.11.011"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.2307\/1422689"},{"key":"e_1_3_2_1_77_1","volume-title":"Counterfactual Explanations for Arbitrary Regression Models. In ICML\u201921 Workshop on Algorithmic Recourse. arxiv:2106","author":"Spooner Thomas","year":"2021","unstructured":"Thomas Spooner, Danial Dervovic, Jason Long, Jon Shepard, Jiahao Chen, and Daniele Magazzeni. 2021. Counterfactual Explanations for Arbitrary Regression Models. In ICML\u201921 Workshop on Algorithmic Recourse. arxiv:2106.15212"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3051315"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1756007"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00022"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525797"},{"key":"e_1_3_2_1_82_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning (ICML). PMLR, 3319\u20133328","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning (ICML). PMLR, 3319\u20133328."},{"key":"e_1_3_2_1_83_1","volume-title":"Towards Robust and Reliable Algorithmic Recourse. NeurIPS 2021 Poster","author":"Upadhyay Sohini","year":"2021","unstructured":"Sohini Upadhyay, Shalmali Joshi, and Himabindu Lakkaraju. 2021. Towards Robust and Reliable Algorithmic Recourse. NeurIPS 2021 Poster (2021), 12."},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1007\/s003550050125"},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_40"},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxrep\/grab019"},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372876"},{"key":"e_1_3_2_1_89_1","unstructured":"Sahil Verma Varich Boonsanong Minh Hoang Keegan\u00a0E. Hines John\u00a0P. Dickerson and Chirag Shah. 2020. Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review. (2020)."},{"key":"e_1_3_2_1_90_1","unstructured":"Mattia Villani Joshua Lockhart and Daniele Magazzeni. 2022. Feature Importance for Time Series Data: Improving KernelSHAP."},{"key":"e_1_3_2_1_91_1","volume-title":"Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI). arxiv:2010","author":"von K\u00fcgelgen Julius","year":"2022","unstructured":"Julius von K\u00fcgelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Sch\u00f6lkopf. 2022. On the Fairness of Causal Algorithmic Recourse. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI). arxiv:2010.06529"},{"key":"e_1_3_2_1_92_1","volume-title":"Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR. SSRN Electronic Journal","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR. SSRN Electronic Journal (2017)."},{"key":"e_1_3_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1145\/1852102.1852106"},{"key":"e_1_3_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01769885"},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0305-z"}],"event":{"name":"AIES '23: AAAI\/ACM Conference on AI, Ethics, and Society","location":"Montr\u00e9al QC Canada","acronym":"AIES '23","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"]},"container-title":["Proceedings of the 2023 AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3600211.3604676","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3600211.3604676","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:39Z","timestamp":1750178259000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3600211.3604676"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,8]]},"references-count":93,"alternative-id":["10.1145\/3600211.3604676","10.1145\/3600211"],"URL":"https:\/\/doi.org\/10.1145\/3600211.3604676","relation":{},"subject":[],"published":{"date-parts":[[2023,8,8]]},"assertion":[{"value":"2023-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}