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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Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality Chara Podimata 49 0 0 08 May 2025
Understanding Fixed Predictions via Confined Regions Connor Lawless Tsui-Wei Weng Berk Ustun Madeleine Udell 48 0 0 22 Feb 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav Evan Monroe Laufer Dan Boneh Kamalika Chaudhuri 85 0 0 06 Feb 2025
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse Seung Hyun Cheon Anneke Wernerfelt Sorelle A. Friedler Berk Ustun FaML FAtt 45 0 0 29 Oct 2024
S-CFE: Simple Counterfactual Explanations Shpresim Sadiku Moritz Wagner Sai Ganesh Nagarajan S. Pokutta 26 0 0 21 Oct 2024
HR-Bandit: Human-AI Collaborated Linear Recourse Bandit Junyu Cao Ruijiang Gao Esmaeil Keyvanshokooh 34 1 0 18 Oct 2024
Counterfactual Explanations for Multivariate Time-Series without Training Datasets Xiangyu Sun Raquel Aoki Kevin H. Wilson 19 1 0 28 May 2024
Generating Likely Counterfactuals Using Sum-Product Networks Jiri Nemecek Tomás Pevný Jakub Marecek TPM 73 0 0 25 Jan 2024
Endogenous Macrodynamics in Algorithmic Recourse Patrick Altmeyer Giovan Angela Aleksander Buszydlik Karol Dobiczek A. V. Deursen Cynthia C. S. Liem 19 7 0 16 Aug 2023
Explaining Black-Box Models through Counterfactuals Patrick Altmeyer A. V. Deursen Cynthia C. S. Liem CML LRM 31 2 0 14 Aug 2023
Reason to explain: Interactive contrastive explanations (REASONX) Laura State Salvatore Ruggieri Franco Turini LRM 27 1 0 29 May 2023
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations Dan Ley Saumitra Mishra Daniele Magazzeni LRM 30 16 0 26 May 2023
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning Jasmina Gajcin Ivana Dusparic CML 21 3 0 08 Mar 2023
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities Jasmina Gajcin Ivana Dusparic CML OffRL 32 8 0 21 Oct 2022
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts Shubham Sharma Jette Henderson Joydeep Ghosh FedML MoE 21 5 0 10 Oct 2022
Local and Regional Counterfactual Rules: Summarized and Robust Recourses Salim I. Amoukou Nicolas Brunel 23 0 0 29 Sep 2022
Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation Andrew Herren P. R. Hahn FAtt 19 9 0 21 Aug 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 42 18 0 31 May 2022
Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that? Marko Tešić U. Hahn CML 11 5 0 12 May 2022
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting Ulrike Kuhl André Artelt Barbara Hammer 32 24 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 25 22 0 21 Apr 2022
Causal Explanations and XAI Sander Beckers CML XAI 13 34 0 31 Jan 2022
Counterfactual Plans under Distributional Ambiguity N. Bui D. Nguyen Viet Anh Nguyen 54 24 0 29 Jan 2022
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts Sebastian Bordt Michèle Finck Eric Raidl U. V. Luxburg AILaw 26 77 0 25 Jan 2022
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis Giovanni De Toni Bruno Lepri Andrea Passerini CML 25 13 0 18 Jan 2022
On the Adversarial Robustness of Causal Algorithmic Recourse Ricardo Dominguez-Olmedo Amir-Hossein Karimi Bernhard Schölkopf 43 63 0 21 Dec 2021
Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation M. Temraz Markt. Keane 14 42 0 05 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations Saumitra Mishra Sanghamitra Dutta Jason Long Daniele Magazzeni AAML 9 58 0 30 Oct 2021
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 22 645 0 05 Oct 2021
Multi-Agent Algorithmic Recourse Andrew O'Brien Edward J. Kim 26 5 0 01 Oct 2021
Counterfactual Instances Explain Little Adam White Artur Garcez CML 27 5 0 20 Sep 2021
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects Julius von Kügelgen N. Agarwal Jakob Zeitler Afsaneh Mastouri Bernhard Schölkopf CML 12 2 0 22 Jun 2021
Rational Shapley Values David S. Watson 15 19 0 18 Jun 2021
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis Martin Pawelczyk Chirag Agarwal Shalmali Joshi Sohini Upadhyay Himabindu Lakkaraju AAML 11 51 0 18 Jun 2021
FairCanary: Rapid Continuous Explainable Fairness Avijit Ghosh Aalok Shanbhag Christo Wilson 11 20 0 13 Jun 2021
Optimal Counterfactual Explanations in Tree Ensembles Axel Parmentier Thibaut Vidal 17 54 0 11 Jun 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 21 184 0 15 May 2021
Consequence-aware Sequential Counterfactual Generation Philip Naumann Eirini Ntoutsi OffRL 15 24 0 12 Apr 2021
Strategic Classification Made Practical Sagi Levanon Nir Rosenfeld 34 54 0 02 Mar 2021
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 17 108 0 26 Feb 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 237 488 0 31 Dec 2020
Interpretability and Explainability: A Machine Learning Zoo Mini-tour Ricards Marcinkevics Julia E. Vogt XAI 16 119 0 03 Dec 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 27 62 0 11 Sep 2020
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data E. Ates Burak Aksar V. Leung A. Coskun AI4TS 43 65 0 25 Aug 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 28 178 0 11 Jun 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 55 93 0 05 Mar 2020
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 99 44 0 11 Jun 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,235 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 227 3,681 0 28 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017