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Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models.
Model-Agnostic Counterfactual Explanations for Consequential Decisions
A.-H. Karimi, G. Barthe, B. Balle, and I. Valera. 2020a · 1905
Earlier work this paper cites.
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan, Chenhao Tan, and Amit Sharma. 2020 · 1912
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Counterfactuals
David Lewis. 1973 · 1973
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Causality: Models, Reasoning, and Inference
Judea Pearl. 2000 · 2000
Earlier work this paper cites.
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera. 2020c · 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. 2020d · 2006
Earlier work this paper cites.
The Rational Imagination: How People Create Alternatives to Reality
Ruth Byrne. 2008 · 2008
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Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
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Runaway Feedback Loops in Predictive Policing
Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramanian. 2017 · 2017
Earlier work this paper cites.
Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf. 2017 · 2017
Earlier work this paper cites.
Membership Inference Attacks against Machine Learning Models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 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. 2017 · 2017
Earlier work this paper cites.
Explanations Based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. In Proceedings of the 32nd International Conference on Neural Information Processing Systems
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das. 2018 · 2018
Cited alongside, same era.
Fairness Definitions Explained. In Proceedings of the International Workshop on Software Fairness
Sahil Verma and Julia Rubin. 2018 · 2018
Cited alongside, same era.
Ethical Dimensions of Visualization Research. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Michael Correll. 2019 · 2019
Cited alongside, same era.
Fairness-Aware Machine Learning
Jannik Dunkelau and Michael Leuschel. 2019 · 2019
Cited alongside, same era.
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miro Dudik, and Hanna Wallach. 2019 · 2019
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera. 2020b · 2020
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Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics. In Proceedings of the 37th International Conference on Machine Learning
Debjani Saha, Candice Schumann, Duncan Mcelfresh, John Dickerson, Michelle Mazurek, and Michael Tschantz. 2020 · 2020
Later among the works it cites.
Machine Learning for Recruiting and Hiring – 6 Current Applications
Kumba Sennaar. 2019 · 2020
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Machine Learning to Predict Credit Risk in Lending Industry
Saurav Singla. 2020 · 2020
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Courts Are Using AI to Sentence Criminals. That Must Stop Now
Jason Tashea. 2017 · 2020
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Cited alongside, same era.
Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin. 2019 · 2019
Cited alongside, same era.
Actionable Recourse in Linear Classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT)
Berk Ustun, Alexander Spangher, and Yang Liu. 2019 · 2019
Cited alongside, same era.
The Hidden Assumptions behind Counterfactual Explanations and Principal Reasons. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT)
Solon Barocas, Andrew D. Selbst, and Manish Raghavan. 2020 · 2020
Cited alongside, same era.
Machine Learning for Medical Diagnostics – 4 Current Applications
Daniel Faggella. 2020 · 2020
Cited alongside, same era.
MODEL LIFECYCLE TRANSFORMATION: HOW BANKS ARE UNLOCKING EFFICIENCIES
Gordon Garisch and Akber Merchant. 2019 · 2020
Cited alongside, same era.
A Survey of Learning Causality with Data: Problems and Methods
Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, and Huan Liu. 2020 · 2020
Cited alongside, same era.
Sahil Verma, John Dickerson, and Keegan Hines. 2020 · 2020
Later among the works it cites.
On Pearl’s Hierarchy and the Foundations of Causal Inference
Elias Bareinboim, Juan D. Correa, Duligur Ibeling, and Thomas Icard. 2020 · 2021
Closest in time.
Benchmarking and Survey of Explanation Methods for Black Box Models
Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, and Salvatore Rinzivillo. 2021 · 2021
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Counterfactual vs Contrastive Explanations in Artificial Intelligence
Amit Dhurandhar and Karthikeyan Shanmugam. 2020 · 2021
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Privacy Attacks on Machine Learning Models
Katharine Jarmul. 2019 · 2021
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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 · 2021
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