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As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post hoc techniques which provide recourse to affected individuals.
Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2002
Earlier work this paper cites.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, and Isabel Valera · 2006
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Using data mining to predict secondary school student performance
P. Cortez and A. Silva · 2008
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2010
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Chernoff-type bounds for the gaussian error function
Seok-Ho Chang, Pamela C Cosman, and Laurence B Milstein · 2011
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Probability in high dimension
Ramon van Handel · 2014
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"why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent D. Mittelstadt, and Chris Russell · 2017
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
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“should this loan be approved or denied?”: A large dataset with class assignment guidelines
Min Li, Amy Mickel, and Stanley Taylor · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D. Selbst, and Manish Raghavan · 2020
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Robust and stable black box explanations, 2020
Himabindu Lakkaraju, Nino Arsov, and Osbert Bastani · 2020
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On counterfactual explanations under predictive multiplicity, 2020
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
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Face: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
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Beyond individualized recourse: Interpretable and interactive summaries of actionable recourses
Kaivalya Rawal and Himabindu Lakkaraju · 2020
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Model agnostic contrastive explanations for structured data, 2019
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan, Pin-Yu Chen, Karthikeyan Shanmugam, and Ruchir Puri · 2019
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South german credit data: Correcting a widely used data set
U Grömping · 2019
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Model-agnostic counterfactual explanations for consequential decisions, 2019
Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera · 2019
Cited alongside, same era.
Interpretable counterfactual explanations guided by prototypes, 2019
Arnaud Van Looveren and Janis Klaise · 2019
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Failing loudly: An empirical study of methods for detecting dataset shift
Stephan Rabanser, Stephan Günnemann, and Zachary Lipton · 2019
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner
Cited in the paper.
Synthesizing robust adversarial examples
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok
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Can i still trust you?: Understanding the impact of distribution shifts on algorithmic recourses
Kaivalya Rawal, Ece Kamar, and Himabindu Lakkaraju · 2020
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The philosophical basis of algorithmic recourse
Suresh Venkatasubramanian and Mark Alfano · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
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