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Ensuring that classifiers are non-discriminatory or fair with respect to a sensitive feature (e.g., race or gender) is a topical problem.
A Theory of Justice
John Rawls · 1971
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Rational Behaviour and Bargaining Equilibrium in Games and Social Situations
John C. Harsanyi · 1977
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Coherent measures of risk
Philippe Artzner, Freddy Delbaen, Jean-Marc Eber, and David Heath · 1999
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Optimization of conditional value-at-risk
R. Tyrrell Rockafellar and Stanislav Uryasev · 2000
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New support vector algorithms
Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, and Peter L. Bartlett · 2000
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Vicinal risk minimization
Olivier Chapelle, Jason Weston, Léon Bottou, and Vladimir Vapnik · 2001
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Spectral measures of risk: A coherent representation of subjective risk aversion
Carlo Acerbi · 2002
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Conditional value-at-risk for general loss distributions
R.T. Rockafellar and S. Uryasev · 2002
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Minimizing the sum of the k largest functions in linear time
Wlodzimierz Ogryczak and Arie Tamir · 2003
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A linear classification model based on conditional geometric score
Jun-ya Gotoh and Akiko Takeda · 2005
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Generalized deviations in risk analysis
R. Tyrrell Rockafellar, Stan Uryasev, and Michael Zabarankin · 2006
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An old-new concept of convex risk measures: the optimized certainty equivalent
Aharon Ben-Tal and Marc Teboulle · 2007
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Modeling, measuring and managing risk
Georg Ch Pflug and Werner Romisch · 2007
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Coherent approaches to risk in optimization under uncertainty
R. Tyrrell Rockafellar · 2007
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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nu -support vector machine as conditional value-at-risk minimization
Akiko Takeda and Masashi Sugiyama · 2008
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Empirical bernstein bounds and sample-variance penalization
Andreas Maurer and Massimiliano Pontil · 2009
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The balanced accuracy and its posterior distribution
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, and Joachim M. Buhmann · 2010
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Three Naive Bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Stochastic finance: an introduction in discrete time
Hans Föllmer and Alexander Schied · 2011
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Modeling and optimization of risk
Pavlo Krokhmal, Michael Zabarankin, and Stan Uryasev · 2011
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Entropic value-at-risk: A new coherent risk measure
Amir Ahmadi-Javid · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 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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Prediction with model-based neutrality
Kazuto Fukuchi, Jun Sakuma, and Toshihiro Kamishima · 2013
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On the statistical consistency of algorithms for binary classification under class imbalance
Aditya Krishna Menon, Harikrishna Narasimhan, Shivani Agarwal, and Sanjay Chawla · 2013
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On conditional parity as a notion of non-discrimination in machine learning
Y. Ritov, Y. Sun, and R. Zhao · 2017
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Measuring discrimination in algorithmic decision making
Indrė Žliobaitė · 2017
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Auditing black-box models for indirect influence
Philip Adler, Casey Falk, Sorelle A. Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian · 2018
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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Unleashing linear optimizers for group-fair learning and optimization
Daniel Alabi, Nicole Immorlica, and Adam Tauman Kalai · 2018
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The fundamental risk quadrangle in risk management, optimization and statistical estimation
R. Tyrrell Rockafellar and Stan Uryasev · 2013
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Learning fair representations
Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
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Value-at-risk support vector machine: stability to outliers
Peter Tsyurmasto, Michael Zabarankin, and Stan Uryasev · 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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Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Alexandra Chouldechova, Diana Benavides Prado, Oleksandr Fialko, and Rhema Vaithianathan · 2018
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Obtaining fairness using optimal transport theory
Eustasio del Barrio, Fabrice Gamboa, Paula Gordaliza, and Jean-Michel Loubes · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
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Decoupled classifiers for group-fair and efficient machine learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson · 2018
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Fairness in supervised learning: An information theoretic approach
AmirEmad Ghassami, Sajad Khodadadian, and Negar Kiyavash · 2018
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Robust empirical optimization is almost the same as mean-variance optimization
Jun-ya Gotoh, Michael Jong Kim, and Andrew E.B. Lim · 2018
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Fairness without demographics in repeated loss minimization
T. B. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
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Fairness behind a veil of ignorance: A welfare analysis for automated decision making
Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, and Andreas Krause · 2018
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C. Williamson · 2018
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Spectral algorithms for computing fair support vector machines
Mahbod Olfat and Anil Aswani · 2018
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A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices
Till Speicher, Hoda Heidari, Nina Grgic-Hlaca, Krishna P. Gummadi, Adish Singla, Adrian Weller, and Muhammad Bilal Zafar · 2018
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Equality of opportunity in classification: A causal approach
Junzhe Zhang and Elias Bareinboim · 2018
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A moral framework for understanding of fair ML through economic models of equality of opportunity
Hoda Heidari, Michele Loi, Krishna P. Gummadi, and Andreas Krause · 2019
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Costs and benefits of fair representation learning
Daniel McNamara, Cheng Soon Ong, and Robert C. Williamson · 2019
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