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Building machine learning models that are fair with respect to an unprivileged group is a topical problem.
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Fairness through Awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (Cambridge, Massachusetts) (ITCS 2012) . Association for Computing Machinery, New York, NY, USA, 214–226
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Data preprocessing techniques for classification without discrimination
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Fairness-Aware Classifier with Prejudice Remover Regularizer. In Machine Learning and Knowledge Discovery in Databases , Peter A. Flach, Tijl De Bie, and Nello Cristianini (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 35–50
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Tal Zarsky. 2012 · 2012
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Learning Fair Representations. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (Atlanta, GA, USA) (ICML 13) . JMLR.org, 325–333
Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork. 2013 · 2013
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Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Sydney, NSW, Australia) (KDD �15) . Association for Computing Machinery, New York, NY, USA, 259�268
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On Fairness and Calibration. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS-17) . Curran Associates Inc., Red Hook, NY, USA, 5684–5693
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger. 2017 · 2017
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A Causal Framework for Discovering and Removing Direct and Indirect Discrimination. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 . 3929–3935
Lu Zhang, Yongkai Wu, and Xintao Wu. 2017 · 2017
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A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Jennifer Dy and Andreas Krause (Eds.), Vol. 80. PMLR, 60–69
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach. 2018 · 2018
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Path-Specific Counterfactual Fairness
Silvia Chiappa and Thomas Gillam. 2018 · 2018
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XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD 2016) . Association for Computing Machinery, New York, NY, USA, 785–794
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Alexandra Chouldechova. 2017 · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Sorelle Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016 · 2016
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Equality of Opportunity in Supervised Learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems (Barcelona, Spain) (NIPS-16) . Curran Associates Inc., Red Hook, NY, USA, 3323–3331
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
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To predict and serve?
Kristian Lum and William Isaac. 2016 · 2016
Cited alongside, same era.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Cathy O’Neil. 2016 · 2016
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Optimized Pre-Processing for Discrimination Prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. 2017 · 2017
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UCI Machine Learning Repository
Dheeru Dua and Casey Graff. 2017 · 2017
Cited alongside, same era.
Defining and Designing Fair Algorithms. In Proceedings of the 2018 ACM Conference on Economics and Computation (Ithaca, NY, USA). Association for Computing Machinery, New York, NY, USA, 705
Sam Corbett-Davies, Sharad Goel, Jamie Morgenstern, and Rachel Cummings. 2018 · 2018
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Non-Discriminatory Machine Learning Through Convex Fairness Criteria
Naman Goel, Mohammad Yaghini, and Boi Faltings. 2018 · 2018
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Inherent Trade-Offs in Algorithmic Fairness. In Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems (Irvine, CA, USA) (SIGMETRICS-18) . Association for Computing Machinery, New York, NY, USA, 40
Jon Kleinberg. 2018 · 2018
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Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner, and Ricardo Silva. 2018 · 2018
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A Neural Network Framework for Fair classifier
P Manisha and S Gujar. 2018 · 2018
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Fair Inference on Outcomes
Razieh Nabi and Ilya Shpitser. 2018 · 2018
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Mitigating Unwanted Biases with Adversarial Learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (New Orleans, LA, USA) (AIES 18) . Association for Computing Machinery, New York, NY, USA, 335–340
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018 · 2018
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Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees. In Proceedings of the Conference on Fairness, Accountability, and Transparency (Atlanta, GA, USA). Association for Computing Machinery, New York, NY, USA, 319–328
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K. Vishnoi. 2019 · 2019
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A Framework for Understanding Unintended Consequences of Machine Learning
Harini Suresh and John V. Guttag. 2019 · 2019
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Achieving Causal Fairness through Generative Adversarial Networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 1452–1458
Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, and Xintao Wu. 2019 · 2019
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