mlsanityData to 7 Oct 2026

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

arXiv 2010.04050 ↗First posted 8 Oct 2020cs.LG cs.AI stat.MLcited by 118

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Abstract (arXiv)

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.

Key facts. Cited by 118 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Oct 2020.

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Citing papers per year: 2021: 40; 2022: 22; 2023: 24; 2024: 17; 2025: 10; 2026: 5.

118 papers.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  3. 2021CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networkscited by 118
  4. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  5. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  6. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  7. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  8. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  9. 2021NICE: An Algorithm for Nearest Instance Counterfactual Explanationscited by 62
  10. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  11. 2021Strategic Classification Made Practicalcited by 58
  12. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
38 more
  1. 2021Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactualscited by 55
  2. 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contextscited by 53
  3. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
  4. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  5. 2022Logic-Based Explainability in Machine Learningcited by 47
  6. 2021Local Explanations via Necessity and Sufficiency: Unifying Theory and Practicecited by 45
  7. 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guaranteescited by 45
  8. 2022On Tackling Explanation Redundancy in Decision Treescited by 44
  9. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 38
  10. 2021Optimal Counterfactual Explanations in Tree Ensemblescited by 36
  11. 2021Counterfactual Explanations in Sequential Decision Making Under Uncertaintycited by 36
  12. 2022Causal Explanations and XAIcited by 30
  13. 2021Conditional Generative Models for Counterfactual Explanationscited by 28
  14. 2023GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanationscited by 27
  15. 2021Amortized Generation of Sequential Algorithmic Recourses for Black-box Modelscited by 24
  16. 2022Attribution-based Explanations that Provide Recourse Cannot be Robustcited by 20
  17. 2022Improvement-Focused Causal Recourse (ICR)cited by 20
  18. 2021Counterfactual Explanations for Machine Learning: Challenges Revisitedcited by 19
  19. 2021A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Datacited by 19
  20. 2022Counterfactual Plans under Distributional Ambiguitycited by 19
  21. 2021Rational Shapley Valuescited by 18
  22. 2021Consequence-aware Sequential Counterfactual Generationcited by 17
  23. 2021CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanationscited by 17
  24. 2022Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAIcited by 16
  25. 2021Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learningcited by 15
  26. 2021Model-Based Counterfactual Synthesizer for Interpretationcited by 14
  27. 2021Bayesian Persuasion for Algorithmic Recoursecited by 14
  28. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  29. 2023Setting the Right Expectations: Algorithmic Recourse Over Timecited by 13
  30. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  31. 2022Statistical Aspects of SHAP: Functional ANOVA for Model Interpretationcited by 12
  32. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 12
  33. 2022Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Settingcited by 11
  34. 2022RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Modelcited by 11
  35. 2022Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanationscited by 11
  36. 2024Grounding Language Plans in Demonstrations Through Counterfactual Perturbationscited by 11
  37. 2021Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functionscited by 10
  38. 2023SketchXAI: A First Look at Explainability for Human Sketchescited by 10

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