References
Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek,
Arie van Deursen, and Cynthia Liem. 2023. “Endogenous
Macrodynamics in Algorithmic
Recourse.” In First IEEE
Conference on Secure and
Trustworthy Machine
Learning.
Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José
Miguel Hernández-Lobato. 2020. “Getting a Clue: A
Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.
Grathwohl, Will, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud,
Mohammad Norouzi, and Kevin Swersky. 2020. “Your Classifier Is
Secretly an Energy Based Model and You Should Treat It Like One.”
In. https://openreview.net/forum?id=Hkxzx0NtDB.
Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and
Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and
Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.
Karimi, Amir-Hossein, Bernhard Schölkopf, and Isabel Valera. 2021.
“Algorithmic Recourse: From Counterfactual Explanations to
Interventions.” In Proceedings of the 2021 ACM
Conference on Fairness, Accountability,
and Transparency, 353–62.
Molnar, Christoph. 2020. Interpretable Machine Learning.
Lulu. com.
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji
Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically
Robust Recourse: Navigating the
Trade-Offs Between Costs and
Robustness in Algorithmic
Recourse.” arXiv Preprint arXiv:2203.06768.
Poyiadzi, Rafael, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and
Peter Flach. 2020. “FACE: Feasible and
Actionable Counterfactual Explanations.” In Proceedings of
the AAAI/ACM Conference on AI,
Ethics, and Society, 344–50.
Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan
Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable
Counterfactual Explanations By Implicit Minimisation of
Epistemic and Aleatoric Uncertainties.”
In International Conference on Artificial
Intelligence and Statistics, 1756–64.
PMLR.
Stutz, David, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, and Arnaud
Doucet. 2022. “Learning Optimal
Conformal Classifiers.” In. https://openreview.net/forum?id=t8O-4LKFVx.
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017.
“Counterfactual Explanations Without Opening the Black Box:
Automated Decisions and the GDPR.”
Harv. JL & Tech. 31: 841.