On the Adversarial Robustness of Causal Algorithmic Recourse

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. To ensure that recourse is robust, individuals are asked to make more effort than they would have otherwise had to. In order to shift part of the burden of robustness from the decision-subject to the decision-maker, we propose a model regularizer that encourages the additional cost of seeking robust recourse to be low. We show that classifiers trained with our proposed model regularizer, which penalizes relying on unactionable features for prediction, offer potentially less effortful recourse.

[1]  Mark Alfano,et al.  The philosophical basis of algorithmic recourse , 2020, FAT*.

[2]  Ilya Shpitser,et al.  Fair Inference on Outcomes , 2017, AAAI.

[3]  Gjergji Kasneci,et al.  On Counterfactual Explanations under Predictive Multiplicity , 2020, UAI.

[4]  Matt Fredrikson,et al.  Consistent Counterfactuals for Deep Models , 2021, ICLR.

[5]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.

[6]  Neil D. Lawrence,et al.  Dataset Shift in Machine Learning , 2009 .

[7]  Pushmeet Kohli,et al.  Adversarial Robustness through Local Linearization , 2019, NeurIPS.

[8]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[9]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[10]  Bernhard Schölkopf,et al.  Domain Generalization via Invariant Feature Representation , 2013, ICML.

[11]  Suresh Venkatasubramanian,et al.  Equalizing Recourse across Groups , 2019, ArXiv.

[12]  Yang Liu,et al.  Actionable Recourse in Linear Classification , 2018, FAT.

[14]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[15]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[16]  Adrian Weller,et al.  On the Fairness of Causal Algorithmic Recourse , 2020, ArXiv.

[17]  Bernhard Schölkopf,et al.  Algorithmic recourse under imperfect causal knowledge: a probabilistic approach , 2020, NeurIPS.

[18]  R. Scheines,et al.  Interventions and Causal Inference , 2007, Philosophy of Science.

[19]  Himabindu Lakkaraju,et al.  Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses , 2020, ArXiv.

[20]  Shie Mannor,et al.  Robustness and Regularization of Support Vector Machines , 2008, J. Mach. Learn. Res..

[21]  Seyed-Mohsen Moosavi-Dezfooli,et al.  Robustness of classifiers: from adversarial to random noise , 2016, NIPS.

[22]  P. Bühlmann,et al.  Invariance, Causality and Robustness , 2018, Statistical Science.

[23]  Chris Russell,et al.  Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR , 2017, ArXiv.

[24]  Oluwasanmi Koyejo,et al.  Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems , 2019, ArXiv.

[25]  Seyed-Mohsen Moosavi-Dezfooli,et al.  DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Ying Daisy Zhuo,et al.  Robust Classification , 2019, INFORMS Journal on Optimization.

[27]  Yang Song,et al.  Improving the Robustness of Deep Neural Networks via Stability Training , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Kevin B. Korb,et al.  Varieties of Causal Intervention , 2004, PRICAI.

[29]  Himabindu Lakkaraju,et al.  Counterfactual Explanations Can Be Manipulated , 2021, NeurIPS.

[30]  Chirag Agarwal,et al.  Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis , 2021 .

[31]  Bernhard Schölkopf,et al.  A survey of algorithmic recourse: definitions, formulations, solutions, and prospects , 2020, ArXiv.

[32]  Gjergji Kasneci,et al.  CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms , 2021, ArXiv.

[33]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[34]  Bernhard Schölkopf,et al.  Algorithmic Recourse: from Counterfactual Explanations to Interventions , 2020, FAccT.

[35]  Francisco Herrera,et al.  A unifying view on dataset shift in classification , 2012, Pattern Recognit..

[37]  Himabindu Lakkaraju,et al.  Towards Robust and Reliable Algorithmic Recourse , 2021, NeurIPS.

[38]  M. Bethge,et al.  Shortcut learning in deep neural networks , 2020, Nature Machine Intelligence.