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Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings.
Counterfactual Reasoning for Fair Clinical Risk Prediction
Pfohl, S. R.; Duan, T.; Ding, D. Y.; and Shah, N. H. 2019 · 1907
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Use of ranks in one-criterion variance analysis
Kruskal, W. H.; and Wallis, W. A. 1952 · 1952
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Models, reasoning and inference
Pearl, J.; et al. 2000 · 2000
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A theory of justice
Rawls, J. 2004 · 2004
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Causal intersectionality for fair ranking
Yang, K.; Loftus, J. R.; and Stoyanovich, J. 2020 · 2006
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Discrimination-aware data mining
Pedreshi, D.; Ruggieri, S.; and Turini, F. 2008 · 2008
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Building classifiers with independency constraints
Calders, T.; Kamiran, F.; and Pechenizkiy, M. 2009 · 2009
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Fairness in machine learning: A survey
Caton, S.; and Haas, C. 2020 · 2010
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Certifying and removing disparate impact
Feldman, M.; Friedler, S. A.; Moeller, J.; Scheidegger, C.; and Venkatasubramanian, S. 2015 · 2015
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Zafar, M. B.; Valera, I.; Rodriguez, M. G.; and Gummadi, K. P. 2015 · 2015
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On the relation between accuracy and fairness in binary classification
Zliobaite, I. 2015 · 2015
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. 2016 · 2016
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The case for process fairness in learning: Feature selection for fair decision making
Grgic-Hlaca, N.; Zafar, M. B.; Gummadi, K. P.; and Weller, A. 2016 · 2016
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
Cited alongside, same era.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J.; Mullainathan, S.; and Raghavan, M. 2016 · 2016
Cited alongside, same era.
On formalizing fairness in prediction with machine learning
Gajane, P.; and Pechenizkiy, M. 2017 · 2017
Cited alongside, same era.
Kusner, M. J.; Loftus, J. R.; Russell, C.; and Silva, R. 2017 · 2017
Cited alongside, same era.
When worlds collide: integrating different counterfactual assumptions in fairness
Russell, C.; Kusner, M. J.; Loftus, J.; and Silva, R. 2017 · 2017
Cited alongside, same era.
Fairlearn: A toolkit for assessing and improving fairness in AI
Bird, S.; Dudík, M.; Edgar, R.; Horn, B.; Lutz, R.; Milan, V.; Sameki, M.; Wallach, H.; and Walker, K. 2020 · 2020
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FlipTest: fairness testing via optimal transport
Black, E.; Yeom, S.; and Fredrikson, M. 2020 · 2020
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Counterfactual risk assessments, evaluation, and fairness
Coston, A.; Mishler, A.; Kennedy, E. H.; and Chouldechova, A. 2020 · 2020
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The sensitivity of counterfactual fairness to unmeasured confounding
Kilbertus, N.; Ball, P. J.; Kusner, M. J.; Weller, A.; and Silva, R. 2020 · 2020
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Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
Chikahara, Y.; Sakaue, S.; Fujino, A.; and Kashima, H. 2021 · 2021
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The use and misuse of counterfactuals in ethical machine learning
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Transparency in fair machine learning: the case of explainable recommender systems
Abdollahi, B.; and Nasraoui, O. 2018 · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Corbett-Davies, S.; and Goel, S. 2018 · 2018
Cited alongside, same era.
Fairness in Decision-Making - The Causal Explanation Formula
Zhang, J.; and Bareinboim, E. 2018 · 2018
Cited alongside, same era.
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Bellamy, R. K.; Dey, K.; Hind, M.; Hoffman, S. C.; Houde, S.; Kannan, K.; Lohia, P.; Martino, J.; Mehta, S.; Mojsilović, A.; et al. 2019 · 2019
Cited alongside, same era.
Path-Specific Counterfactual Fairness
Chiappa, S. 2019 · 2019
Cited alongside, same era.
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z.; Powers, B.; Vogeli, C.; and Mullainathan, S. 2019 · 2019
Cited alongside, same era.
Counterfactual Fairness: Unidentification, Bound and Algorithm
Wu, Y.; Zhang, L.; and Wu, X. 2019 · 2019
Cited alongside, same era.
Kasirzadeh, A.; and Smart, A. 2021 · 2021
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Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
Kim, H.; Shin, S.; Jang, J.; Song, K.; Joo, W.; Kang, W.; and Moon, I.-C. 2021 · 2021
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Causal multi-level fairness
Mhasawade, V.; and Chunara, R. 2021 · 2021
Later among the works it cites.
Algorithmic fairness: Choices, assumptions, and definitions
Mitchell, S.; Potash, E.; Barocas, S.; D’Amour, A.; and Lum, K. 2021 · 2021
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Counterfactually fair automatic speech recognition
Sarı, L.; Hasegawa-Johnson, M.; and Yoo, C. D. 2021 · 2021
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Selection, Ignorability and Challenges With Causal Fairness
Fawkes, J.; Evans, R.; and Sejdinovic, D. 2022 · 2022
Closest in time.
Causal Conceptions of Fairness and their Consequences
Nilforoshan, H.; Gaebler, J. D.; Shroff, R.; and Goel, S. 2022 · 2022
Closest in time.
On the Fairness of Causal Algorithmic Recourse
von Kügelgen, J.; Bhatt, U.; Karimi, A.-H.; Valera, I.; Weller, A.; and Scholkopf, B. 2022 · 2022
Closest in time.