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Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification algorithms in various social contexts.
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Susan Magarey · 2004
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Biased normalized cuts
Subhransu Maji, Nisheeth K. Vishnoi, and Jitendra Malik · 2011
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Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Fairness-aware classifier with prejudice remover regularizer
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A spectral algorithm for improving graph partitions
Michael W. Mahoney, Lorenzo Orecchia, and Nisheeth K. Vishnoi · 2012
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Northpointe · 2012
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Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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On the statistical consistency of plug-in classifiers for non-decomposable performance measures
Harikrishna Narasimhan, Rohit Vaish, and Shivani Agarwal · 2014
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Big data: Seizing opportunities, preserving values
United States. Executive Office of the President and John Podesta · 2014
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Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
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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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Can an algorithm hire better than a human
Claire Cain Miller · 2015
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https://github.com/propublica/compas-analysis , 2016
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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How to be fair and diverse?
L. Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth K Vishnoi · 2016
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Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
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A confidence-based approach for balancing fairness and accuracy
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes · 2016
Meritocratic fairness for cross-population selection
Michael Kearns, Aaron Roth, and Zhiwei Steven Wu · 2017
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Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
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On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, and Kilian Q. Weinberger · 2017
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Recycling privileged learning and distribution matching for fairness
Novi Quadrianto and Viktoriia Sharmanska · 2017
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Learning non-discriminatory predictors
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, and Nathan Srebro · 2017
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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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Anthony W Flores, Kristin Bechtel, and Christopher T Lowenkamp · 2016
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On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
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Precinct or prejudice? understanding racial disparities in new york city’s stop-and-frisk policy
Sharad Goel, Justin M Rao, Ravi Shroff, et al · 2016
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Satisfying real-world goals with dataset constraints
Gabriel Goh, Andrew Cotter, Maya R. Gupta, and Michael P. Friedlander · 2016
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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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From parity to preference-based notions of fairness in classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, and Adrian Weller · 2017
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Measuring discrimination in algorithmic decision making
Indre Zliobaite · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach · 2018
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Ranking with fairness constraints
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