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Binary decision making classifiers are not fair by default.
A survey on bias and fairness in machine learning. (2019)
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2019 · 1908
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Fairness in Machine Learning: A Survey. (2020)
Simon Caton and Christian Haas. 2020 · 2010
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Scikit-learn: Machine Learning in Python
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Quantifying explainable discrimination and removing illegal discrimination in automated decision making
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Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15) . Association for Computing Machinery, New York, NY, USA, 259–268
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Can an Algorithm Hire Better Than a Human?
Claire Cain Miller. 2015 · 2015
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Machine bias
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Big Data’s Disparate Impact
Solon Barocas and Andrew D Selbst. 2016 · 2016
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Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Alexandra Chouldechova. 2017 · 2016
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COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity
William Dieterich, Christina Mendoza, and Tim Brennan. 2016 · 2016
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A Confidence-Based Approach for Balancing Fairness and Accuracy
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes. 2016 · 2016
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On the (im)possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016 · 2016
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Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
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Inherent Trade-Offs in the Fair Determination of Risk Scores. (2016)
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016 · 2016
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Algorithmic Decision Making and the Cost of Fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’17) . Association for Computing Machinery, New York, NY, USA, 797–806
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
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Predictably Unequal? The Effects of Machine Learning on Credit Markets
Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther. 2017 · 2017
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Human Decisions and Machine Predictions*
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2017 · 2017
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On Fairness and Calibration. In Proceedings of the 31st International Conference on Neural Information Processing Systems . Curran Associates Inc., 5684–5693
From Soft Classifiers to Hard Decisions. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, New York, NY, USA, 309–318
Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, and Adam Smith. 2019 · 2019
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A Comparative Study of Fairness-Enhancing Interventions in Machine Learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19) . Association for Computing Machinery, New York, NY, USA, 329–338
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth. 2019 · 2019
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The Ethical Algorithm: The Science of Socially Aware Algorithm Design
Michael Kearns and Aaron Roth. 2019 · 2019
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The Implicit Fairness Criterion of Unconstrained Learning. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 4051–4060
Lydia T Liu, Max Simchowitz, and Moritz Hardt. 2019 · 2019
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Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017 · 2017
Cited alongside, same era.
The problem of infra-marginality in outcome tests for discrimination
Camelia Simoiu, Sam Corbett-Davies, and Sharad Goel. 2017 · 2017
Cited alongside, same era.
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1171–1180
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. 2017 · 2017
Cited alongside, same era.
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
Sam Corbett-Davies and Sharad Goel. 2018 · 2018
Cited alongside, same era.
Decoupled Classifiers for Group-Fair and Efficient Machine Learning. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research, Vol. 81) , Sorelle A Friedler and Christo Wilson (Eds.). PMLR, New York, NY, USA, 119–133
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson. 2018 · 2018
Cited alongside, same era.
Does mitigating ML’s impact disparity require treatment disparity?. In Proceedings of the 32nd International Conference on Neural Information Processing Systems . Curran Associates, Inc., 8136–8146
Zachary C Lipton, Alexandra Chouldechova, and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Bias In, Bias Out
Sandra Gabriel Mayson. 2018 · 2018
Cited alongside, same era.
The cost of fairness in binary classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research, Vol. 81) , Sorelle A Friedler and Christo Wilson (Eds.). PMLR, New York, NY, USA, 107–118
Aditya Krishna Menon and Robert C Williamson. 2018 · 2018
Cited alongside, same era.
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Fairness metrics: A comparative analysis. In 2020 IEEE International Conference on Big Data (Big Data) . IEEE, IEEE Computer Society, Los Alamitos, CA, USA, 3662–3666
Pratyush Garg, John Villasenor, and Virginia Foggo. 2020 · 2020
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Normative Principles for Evaluating Fairness in Machine Learning
Derek Leben. 2020 · 2020
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Dana Pessach and Erez Shmueli. 2020 · 2020
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Fairness in Criminal Justice Risk Assessments: The State of the Art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth. 2021 · 2021
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Fairness, Equality, and Power in Algorithmic Decision-Making. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency , Vol. 11. ACM, New York, NY, USA, 576–586
Maximilian Kasy and Rediet Abebe. 2021 · 2021
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Fairness in credit scoring: Assessment, implementation and profit implications
Nikita Kozodoi, Johannes Jacob, and Stefan Lessmann. 2022 · 2021
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Matthias Kuppler, Christoph Kern, Ruben L Bach, and Frauke Kreuter. 2021 · 2021
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Fair Selective Classification Via Sufficiency. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 6076–6086
Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, and Gregory W Wornell. 2021 · 2021
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On the Applicability of Machine Learning Fairness Notions
Karima Makhlouf, Sami Zhioua, and Catuscia Palamidessi. 2021 · 2021
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Algorithmic Fairness: Choices, Assumptions, and Definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. 2021 · 2021
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Group Fairness in Prediction-Based Decision Making: From Moral Assessment to Implementation. In 2022 9th Swiss Conference on Data Science (SDS)
Joachim Baumann and Christoph Heitz. 2022 · 2022
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