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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

8 October 2020

Gilles Barthe

Papers citing "A survey of algorithmic recourse: definitions, formulations, solutions, and prospects"

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Title
Augmenting The Weather: A Hybrid Counterfactual-SMOTE Algorithm for Improving Crop Growth Prediction When Climate Changes M. Temraz Mark T Keane 40 0 0 14 Nov 2025
CONFEX: Uncertainty-Aware Counterfactual Explanations with Conformal Guarantees Aman Bilkhoo Mehran Hosseini Milad Kazemi Nicola Paoletti 100 0 0 22 Oct 2025
Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality Chara Podimata 277 3 0 08 May 2025
Understanding Fixed Predictions via Confined Regions Connor Lawless Tsui-Wei Weng Berk Ustun Madeleine Udell 245 1 0 22 Feb 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav Evan Monroe Laufer Dan Boneh Kamalika Chaudhuri 431 1 0 06 Feb 2025
Feature Responsiveness Scores: Model-Agnostic Explanations for RecourseInternational Conference on Learning Representations (ICLR), 2024 Seung Hyun Cheon Anneke Wernerfelt Sorelle A. Friedler Berk Ustun FaML FAtt 447 5 0 29 Oct 2024
S-CFE: Simple Counterfactual ExplanationsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 Shpresim Sadiku Moritz Wagner Sai Ganesh Nagarajan Sebastian Pokutta 491 1 0 21 Oct 2024
HR-Bandit: Human-AI Collaborated Linear Recourse BanditInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 Junyu Cao Ruijiang Gao Esmaeil Keyvanshokooh 486 4 0 18 Oct 2024
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability Ioannis Emiris Eleni Psaroudaki G. Giannopoulos Dimitrios Gunopulos Nikolaos Theologitis ... Dimitrios Tomaras Kleopatra Markou Nikolaos Theologitis Dimitris Fotakis Konstantinos Tsopelas CML FAtt 279 5 0 29 May 2024
$No $D_{\text{train}}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning$ No $D_{\text{train}}$ : Model-Agnostic Counterfactual Explanations Using Reinforcement Learning Xiangyu Sun Raquel Aoki Kevin H. Wilson 198 1 0 28 May 2024
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels Patryk Wielopolski Oleksii Furman Łukasz Lenkiewicz Jerzy Stefanowski Maciej Ziȩba 323 5 0 27 May 2024
A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density Dimitris Sacharidis Dimitris Fotakis V. Kalogeraki Dimitrios Gunopulos CML 165 0 0 20 Apr 2024
Connecting Algorithmic Fairness to Quality Dimensions in Machine Learning in Official Statistics and Survey Production Patrick Oliver Schenk Christoph Kern FaML 248 4 0 14 Feb 2024
Generating Likely Counterfactuals Using Sum-Product NetworksInternational Conference on Learning Representations (ICLR), 2024 Jiri Nemecek Tomás Pevný Georgios Korpas TPM 499 4 0 25 Jan 2024
Online Algorithmic Recourse by Collective Action Elliot Creager Richard Zemel 142 5 0 29 Dec 2023
Personalized Path Recourse for Reinforcement Learning Agents Dat Hong Tong Wang 262 0 0 14 Dec 2023
Setting the Right Expectations: Algorithmic Recourse Over TimeConference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2023 Joao Fonseca Andrew Bell Carlo Abrate Francesco Bonchi Julia Stoyanovich 190 16 0 13 Sep 2023
Adaptive Adversarial Training Does Not Increase Recourse CostsAAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023 Ian Hardy Jayanth Yetukuri Yang Liu AAML 157 1 0 05 Sep 2023
Declarative Reasoning on Explanations Using Constraint Logic ProgrammingEuropean Conference on Logics in Artificial Intelligence (JELIA), 2023 Laura State Salvatore Ruggieri Franco Turini LRM 151 1 0 01 Sep 2023
Endogenous Macrodynamics in Algorithmic Recourse Patrick Altmeyer Giovan Angela Aleksander Buszydlik Karol Dobiczek A. V. Deursen Cynthia C. S. Liem 131 9 0 16 Aug 2023
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity ModelNeural Information Processing Systems (NeurIPS), 2023 Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel 409 13 0 02 Jun 2023
Reason to explain: Interactive contrastive explanations (REASONX) Laura State Salvatore Ruggieri Franco Turini LRM 262 2 0 29 May 2023
GLOBE-CE: A Translation-Based Approach for Global Counterfactual ExplanationsInternational Conference on Machine Learning (ICML), 2023 Dan Ley Saumitra Mishra Daniele Magazzeni LRM 298 23 0 26 May 2023
SketchXAI: A First Look at Explainability for Human SketchesComputer Vision and Pattern Recognition (CVPR), 2023 Zhiyu Qu Yulia Gryaditskaya Ke Li Kaiyue Pang Tao Xiang Yi-Zhe Song 149 13 0 23 Apr 2023
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement LearningAdaptive Agents and Multi-Agent Systems (AAMAS), 2023 Jasmina Gajcin Ivana Dusparic CML 103 6 0 08 Mar 2023
Causal Dependence PlotsNeural Information Processing Systems (NeurIPS), 2023 Joshua R. Loftus Lucius E.J. Bynum Sakina Hansen CML 124 5 0 07 Mar 2023
GAM Coach: Towards Interactive and User-centered Algorithmic RecourseInternational Conference on Human Factors in Computing Systems (CHI), 2023 Zijie J. Wang J. W. Vaughan R. Caruana Duen Horng Chau HAI 283 26 0 27 Feb 2023
Improvement-Focused Causal Recourse (ICR)AAAI Conference on Artificial Intelligence (AAAI), 2022 Gunnar Konig Timo Freiesleben Moritz Grosse-Wentrup CML 191 19 0 27 Oct 2022
Logic-Based Explainability in Machine Learning Sasha Rubin LRM XAI 408 57 0 24 Oct 2022
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and OpportunitiesACM Computing Surveys (ACM CSUR), 2022 Jasmina Gajcin Ivana Dusparic CML OffRL 334 16 0 21 Oct 2022
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP), 2022 Julia El Zini M. Awad AAML 144 2 0 17 Oct 2022
FEAMOE: Fair, Explainable and Adaptive Mixture of ExpertsInternational Joint Conference on Artificial Intelligence (IJCAI), 2022 Sanjay Kariyappa Jette Henderson Joydeep Ghosh FedML MoE 119 5 0 10 Oct 2022
Local and Regional Counterfactual Rules: Summarized and Robust Recourses Salim I. Amoukou Nicolas Brunel 186 0 0 29 Sep 2022
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual ExplanationsKnowledge Discovery and Data Mining (KDD), 2022 Vy Vo Trung Le Van Nguyen He Zhao Edwin V. Bonilla Gholamreza Haffari Dinh Q. Phung CML 236 15 0 27 Sep 2022
Robust Counterfactual Explanations for Tree-Based EnsemblesInternational Conference on Machine Learning (ICML), 2022 Sanghamitra Dutta Jason Long Saumitra Mishra Cecilia Tilli Daniele Magazzeni 293 66 0 06 Jul 2022
Framing Algorithmic Recourse for Anomaly DetectionKnowledge Discovery and Data Mining (KDD), 2022 Debanjan Datta F. Chen Naren Ramakrishnan 152 5 0 29 Jun 2022
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse ModelInternational Conference on Information and Knowledge Management (CIKM), 2022 Hangzhi Guo Feiran Jia Jinghui Chen Anna Squicciarini A. Yadav OOD 320 12 0 01 Jun 2022
Attribution-based Explanations that Provide Recourse Cannot be RobustJournal of machine learning research (JMLR), 2022 H. Fokkema R. D. Heide T. Erven FAtt 326 22 0 31 May 2022
On Tackling Explanation Redundancy in Decision TreesJournal of Artificial Intelligence Research (JAIR), 2022 Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 227 74 0 20 May 2022
Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that?Patterns (Patterns), 2022 Marko Tešić U. Hahn CML 109 6 0 12 May 2022
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract SettingConference on Fairness, Accountability and Transparency (FAccT), 2022 Ulrike Kuhl André Artelt Barbara Hammer 147 28 0 11 May 2022
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI Greta Warren Mark T. Keane R. Byrne CML 129 27 0 21 Apr 2022
Global Counterfactual Explanations: Investigations, Implementations and Improvements Dan Ley Saumitra Mishra Daniele Magazzeni LRM 152 13 0 14 Apr 2022
Causal Explanations and XAICLEaR (CLEaR), 2022 Sander Beckers CML XAI 232 44 0 31 Jan 2022
Counterfactual Plans under Distributional AmbiguityInternational Conference on Learning Representations (ICLR), 2022 N. Bui D. Nguyen Viet Anh Nguyen 262 25 0 29 Jan 2022
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial ContextsConference on Fairness, Accountability and Transparency (FAccT), 2022 Sebastian Bordt Michèle Finck Eric Raidl U. V. Luxburg AILaw 305 91 0 25 Jan 2022
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesisMachine-mediated learning (ML), 2022 Giovanni De Toni Bruno Lepri Baptiste Caramiaux CML 219 14 0 18 Jan 2022
On the Adversarial Robustness of Causal Algorithmic RecourseInternational Conference on Machine Learning (ICML), 2021 Ricardo Dominguez-Olmedo Amir-Hossein Karimi Bernhard Schölkopf 298 72 0 21 Dec 2021
Bayesian Persuasion for Algorithmic Recourse Keegan Harris Valerie Chen Joon Sik Kim Ameet Talwalkar Hoda Heidari Zhiwei Steven Wu 152 16 0 12 Dec 2021
DeDUCE: Generating Counterfactual Explanations Efficiently Benedikt Höltgen Lisa Schut J. Brauner Y. Gal CML 129 6 0 29 Nov 2021