On the Fairness of Causal Algorithmic Recourse

Authors

  • Julius von Kügelgen Max Planck Institute for Intelligent Systems, Tübingen, Germany & University of Cambridge
  • Amir-Hossein Karimi Max Planck Institute for Intelligent Systems, Tübingen, Germany & ETH Zürich
  • Umang Bhatt University of Cambridge
  • Isabel Valera Saarland University
  • Adrian Weller University of Cambridge & The Alan Turing Institute
  • Bernhard Schölkopf Max Planck Institute for Intelligent Systems, Tübingen, Germany

DOI:

https://doi.org/10.1609/aaai.v36i9.21192

Keywords:

Philosophy And Ethics Of AI (PEAI), Machine Learning (ML), Humans And AI (HAI)

Abstract

Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individuals to remedy an unfavourable classification. We propose two new fair-ness criteria at the group and individual level, which—unlike prior work on equalising the average group-wise distance from the decision boundary—explicitly account for causal relationships between features, thereby capturing downstream effects of recourse actions performed in the physical world. We explore how our criteria relate to others, such as counterfactual fairness, and show that fairness of recourse is complementary to fairness of prediction. We study theoretically and empirically how to enforce fair causal recourse by altering the classifier and perform a case study on the Adult dataset. Finally, we discuss whether fairness violations in the data generating process revealed by our criteria may be better addressed by societal interventions as opposed to constraints on the classifier.

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Published

2022-06-28

How to Cite

Kügelgen, J. von, Karimi, A.-H., Bhatt, U., Valera, I., Weller, A., & Schölkopf, B. (2022). On the Fairness of Causal Algorithmic Recourse. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9584-9594. https://doi.org/10.1609/aaai.v36i9.21192

Issue

Section

AAAI Technical Track on Philosophy and Ethics of AI