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

Mon Dec 13 01:15 PM -- 01:30 PM (PST) @
in Causal Inference & Machine Learning: Why now? »

Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable outcomes made by automated decision-making systems. The individual then exerts time and effort to positively change their circumstances. Recourse recommendations should ideally be robust to reasonably small changes in the circumstances of the individual seeking recourse. In this work, we formulate the adversarially robust recourse problem and show that methods that offer minimally costly recourse fail to be robust. We restrict ourselves to linear classifiers, and show that the adversarially robust recourse problem reduces to the standard recourse problem for some modified classifier with a shifted decision boundary. Finally, we derive bounds on the extra cost incurred by individuals seeking robust recourse, and discuss how to regulate this cost between the individual and the decision-maker.

Author Information

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

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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