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As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2017
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Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Counterfactual explanations without opening the black box: automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
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Equalizing recourse across groups
Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, and Suresh Venkatasubramanian · 2019
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Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
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Divyat Mahajan, Chenhao Tan, and Amit Sharma · 2019
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Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Herve Jegou · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Y. Liu · 2019
Interpretable and interactive summaries ofactionable recourses
Kaivalya Rawal and Himabindu Lakkaraju · 2020
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A survey of privacy attacks in machine learning
Maria Rigaki and Sebastian Garcia · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
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On the fairness of causal algorithmic recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Schölkopf · 2020
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Getting a clue: A method for explaining uncertainty estimates
Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato · 2021
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Interpretable counterfactual explanations guided by prototypes
Arnaud Van Looveren and Janis Klaise · 2019
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Model extraction from counterfactual explanations
Ulrich Aïvodji, Alexandre Bolot, and Sébastien Gambs · 2020
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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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Gan-leaks: A taxonomy of membership inference attacks against generative models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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Label-only membership inference attacks
Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, and Nicolas Papernot · 2020
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Multi-objective counterfactual explanations
Susanne Dandl, Christoph Molnar, Martin Binder, and Bernd Bischl · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K. Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramèr · 2021
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On the adversarial robustness of causal algorithmic recourse
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Algorithmic recourse: from counterfactual explanations to interventions
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Carla: A python library to benchmark algorithmic recourse and counterfactual explanation algorithms
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Algorithmic recourse in the wild: Understanding the impact of data and model shifts
Kaivalya Rawal, Ece Kamar, and Himabindu Lakkaraju · 2021
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On the privacy risks of model explanations
Reza Shokri, Martin Strobel, and Yair Zick · 2021
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Counterfactual explanations can be manipulated
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Counterfactual explanations for arbitrary regression models
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, and Reza Shokri · 2021
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Algorithmic recourse in the face of noisy human responses
Martin Pawelczyk, Teresa Datta, Johannes van-den Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju · 2022
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