Fetching the paper…
Reading the bibliography…
Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions.
Data Networks (2nd Ed.)
Dimitri Bertsekas and Robert Gallager · 1992
Earlier work this paper cites.
United States Environment Protection Agency , 1994
Catalogue of standard toxicity tests for ecological risk assessment · 1994
Earlier work this paper cites.
Lof: Identifying density-based local outliers
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander · 2000
Earlier work this paper cites.
Performance incentive funding
Perry McGarry · 2012
Earlier work this paper cites.
Learning end-to-end video classification with rank-pooling
Basura Fernando and Stephen Gould · 2016
Earlier work this paper cites.
"why should I trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Earlier work this paper cites.
Stephen Gould, Basura Fernando, Anoop Cherian, Peter Anderson, Rodrigo Santa Cruz, and Edison Guo · 2016
Earlier work this paper cites.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
Earlier work this paper cites.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
Earlier work this paper cites.
The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 2018
Earlier work this paper cites.
Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Earlier work this paper cites.
Equalizing recourse across groups
Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, and Suresh Venkatasubramanian · 2019
Cited alongside, same era.
Interpretable Counterfactual Explanations Guided by Prototypes
Arnaud Van Looveren and Janis Klaise · 2019
Cited alongside, same era.
A comparative study of fairness-enhancing interventions in machine learning
Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth · 2019
Cited alongside, same era.
Optimal decision making under strategic behavior
Behzad Tabibian, Stratis Tsirtsis, Moein Khajehnejad, Adish Singla, Bernhard Schölkopf, and Manuel Gomez-Rodriguez · 2019
Cited alongside, same era.
Fairwashing: the risk of rationalization
Ulrich Aivodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, and Alain Tapp · 2019
Cited alongside, same era.
The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D. Selbst, and Manish Raghavan · 2020
Later among the works it cites.
Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
Later among the works it cites.
Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
Later among the works it cites.
On the fairness of causal algorithmic recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt, Isabel Valera, Adrian Weller, and Bernhard Schölkopf · 2020
Later among the works it cites.
Predictive multiplicity in classification
Charles T. Marx, F. Calmon, and Berk Ustun · 2020
Later among the works it cites.
Strategic classification with a light touch: Learning classifiers that incentivize constructive adaptation, 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2020
Cited alongside, same era.
Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models
Shubham Sharma, Jette Henderson, and Joydeep Ghosh · 2020
Cited alongside, same era.
Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley · 2020
Cited alongside, same era.
Model-agnostic counterfactual explanations for consequential decisions
A.-H. Karimi, G. Barthe, B. Balle, and I. Valera · 2020
Cited alongside, same era.
Face: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2020
Cited alongside, same era.
Can i still trust you?: Understanding the impact of distribution shifts on algorithmic recourses
Kaivalya Rawal, Ece Kamar, and Himabindu Lakkaraju · 2020
Cited alongside, same era.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
A.-H. Karimi*, J. von Kügelgen*, B. Schölkopf, and I. Valera · 2020
Cited alongside, same era.
Yatong Chen, Jialu Wang, and Yang Liu · 2020
Later among the works it cites.
Decisions, counterfactual explanations and strategic behavior
Stratis Tsirtsis and Manuel Gomez-Rodriguez · 2020
Later among the works it cites.
You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods
B. Dimanov, Umang Bhatt, M. Jamnik, and Adrian Weller · 2020
Later among the works it cites.
Gradient-based Analysis of NLP Models is Manipulable
Junlin Wang, Jens Tuyls, Eric Wallace, and Sameer Singh · 2020
Later among the works it cites.
Fairwashing explanations with off-manifold detergent
Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, and Pan Kessel · 2020
Later among the works it cites.
The age of secrecy and unfairness in recidivism prediction
Cynthia Rudin, Caroline Wang, and Beau Coker · 2020
Later among the works it cites.
A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martín Pereira-Fariña · 2021
Closest in time.
Algorithmic recourse: from counterfactual explanations to interventions
A.-H. Karimi, B. Schölkopf, and I. Valera · 2021
Closest in time.
The use and misuse of counterfactuals in ethical machine learning
Atoosa Kasirzadeh and Andrew Smart · 2021
Closest in time.