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On the Adversarial Robustness of Causal Algorithmic Recourse
Ricardo Dominguez-Olmedo · Amir Karimi · Bernhard Schölkopf

Tue Jul 19 11:50 AM -- 11:55 AM (PDT) @ None

Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable outcomes made by automated decision-making systems. Recourse recommendations should ideally be robust to reasonably small uncertainty in the features of the individual seeking recourse. In this work, we formulate the adversarially robust recourse problem and show that recourse methods offering minimally costly recourse fail to be robust. We then present methods for generating adversarially robust recourse in the linear and in the differentiable case. Finally, we empirically show that regularizing the decision-making classifier to rely more strongly on actionable features facilitates the existence of adversarially robust recourse.

Author Information

Ricardo Dominguez-Olmedo (University of Tübingen)
Amir Karimi (MPI for Intelligent Systems, Tübingen, Germany)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

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