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In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set.
Proportional equality: Readings of romer
Nan D Hunter · 2000
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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Counting positives accurately despite inaccurate classification
George Forman · 2005
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Indre Zliobaite and Bart Custers · 2005
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Quantifying trends accurately despite classifier error and class imbalance
George Forman · 2006
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Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
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Quantification via probability estimators
Antonio Bella, Cesar Ferri, José Hernández-Orallo, and Maria Jose Ramirez-Quintana · 2010
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Decision theory for discrimination-aware classification
Faisal Kamiran, Asim Karim, and Xiangliang Zhang · 2012
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Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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A unifying view on dataset shift in classification
Jose G Moreno-Torres, Troy Raeder, RocíO Alaiz-RodríGuez, Nitesh V Chawla, and Francisco Herrera · 2012
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Class distribution estimation based on the hellinger distance
VíCtor GonzáLez-Castro, RocíO Alaiz-RodríGuez, and Enrique Alegre · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Patterns of dataset shift
Meelis Kull and Peter Flach · 2014
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A multidisciplinary survey on discrimination analysis
Andrea Romei and Salvatore Ruggieri · 2014
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Medical Expenditure Panel Survey
Agency for Healthcare Research & Quality · 2016
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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Gabriel Goh, Andrew Cotter, Maya Gupta, and Michael P Friedlander · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias, October 2018
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang · 2018
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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 · 2018
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COMPAS Recidivism Risk Score Data & Analysis
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Algorithmic decision making and the cost of fairness
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Decoupled classifiers for fair and efficient machine learning
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Why is quantification an interesting learning problem?
Pablo González, Jorge Díez, Nitesh Chawla, and Juan José del Coz · 2017
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Alexandra Chouldechova and Aaron Roth · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
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The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 2018
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Discrimination in the age of algorithms
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Cass R Sunstein · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Lu Zhang, Yongkai Wu, and Xintao Wu · 2018
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Fairness through the lens of proportional equality
Arpita Biswas and Suvam Mukherjee · 2019
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Classification with fairness constraints: A meta-algorithm with provable guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K. Vishnoi · 2019
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Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, and Seungil You · 2019
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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
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