mlsanityData to 7 Oct 2026

Robustness Implies Fairness in Causal Algorithmic Recourse

arXiv 2302.03465 ↗First posted 7 Feb 2023cs.LGcited by 12

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Abstract (arXiv)

Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations to empower beneficiaries to achieve a more favorable outcome. To ensure an effective remedy, suggested interventions must not only be low-cost but also robust and fair. This goal is accomplished by providing similar explanations to individuals who are alike. This study explores the concept of individual fairness and adversarial robustness in causal algorithmic recourse and addresses the challenge of achieving both. To resolve the challenges, we propose a new framework for defining adversarially robust recourse. The new setting views the protected feature as a pseudometric and demonstrates that individual fairness is a special case of adversarial robustness. Finally, we introduce the fair robust recourse problem to achieve both desirable properties and show how it can be satisfied both theoretically and empirically.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 7 Feb 2023.

Builds on

21 of 39 references.

References · 21

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  2. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  3. 2017Counterfactual Fairnesscited by 802
  4. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  5. 2018Actionable Recourse in Linear Classificationcited by 327
  6. 2020Multi-Objective Counterfactual Explanationscited by 157
  7. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 150
  8. 2017Fair Inference On Outcomescited by 143
  9. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 139
  10. 2020To be Robust or to be Fair: Towards Fairness in Adversarial Trainingcited by 137
  11. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  12. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
9 more
  1. 2020On Counterfactual Explanations under Predictive Multiplicitycited by 73
  2. 2020On the Fairness of Causal Algorithmic Recoursecited by 69
  3. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  4. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
  5. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
  6. 2019Equalizing Recourse across Groupscited by 51
  7. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  8. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 38
  9. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 2; 2026: 1.

12 papers.

  1. 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guaranteescited by 45
  2. 2024Robust Counterfactual Explanations in Machine Learning: A Surveycited by 29
  3. 2023Setting the Right Expectations: Algorithmic Recourse Over Timecited by 13
  4. 2024Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Changecited by 10
  5. 2024Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunitycited by 9
  6. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  7. 2023Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustnesscited by 3
  8. 2024A New Paradigm for Counterfactual Reasoning in Fairness and Recoursecited by 3
  9. 2025To Give or Not to Give? The Impacts of Strategically Withheld Recoursecited by 2
  10. 2024Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamicscited by 1
  11. 2025Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairnesscited by 0
  12. 2026Algorithmic Recourse of In-Context Learning for Tabular Datacited by 0