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Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task.
Stability and change in judicial decision-making: Incre mentalism or stare decisis
Martin Shapiro · 1965
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The color of money
Bill Dedman et al · 1988
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Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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Consumer acceptance of online agent advice: Extremity and positivity effects
Andrew D Gershoff, Ashesh Mukherjee, and Anirban Mukhopadhyay · 2003
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Shilling recommender systems for fun and profit
Shyong K Lam and John Riedl · 2004
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An evaluation of neighbourhood formation on the performance of collaborative filtering
Michael P O’mahony, Neil J Hurley, and Guenole CM Silvestre · 2004
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Factors affecting the acceptance of expert advice
Lyn M Van Swol and Janet A Sniezek · 2005
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Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
Sayan Mukherjee, Partha Niyogi, Tomaso A. Poggio, and Ryan M. Rifkin · 2006
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Estimating the“wrong”graphical model: Benefits in the computation-limited setting
Martin J Wainwright · 2006
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Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams · 2007
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Stochastic subgradient methods
Stephen Boyd and Almir Mutapcic · 2008
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The role of race in forecasts of violent crime
Richard Berk · 2009
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A similarity measure to assess the stability of classification trees
Bénédicte Briand, Gilles R Ducharme, Vanessa Parache, and Catherine Mercat-Rommens · 2009
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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The security of machine learning
Marco Barreno, Blaine Nelson, Anthony D Joseph, and J Doug Tygar · 2010
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Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
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Maximizing stability of recommendation algorithms: A collective inference approach
Gediminas Adomavicius and Jingjing Zhang · 2011
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k-nn as an implementation of situation testing for discrimination discovery and prevention
Binh Thanh Luong, Salvatore Ruggieri, and Franco Turini · 2011
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Stability of recommendation algorithms
Gediminas Adomavicius and Jingjing Zhang · 2012
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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How effective is targeted advertising?
Ayman Farahat and Michael C Bailey · 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
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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A literature review and classification of recommender systems research
Deuk Hee Park, Hyea Kyeong Kim, Il Young Choi, and Jae Kyeong Kim · 2012
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Collective stability in structured prediction: Generalization from one example
Ben London, Bert Huang, Ben Taskar, and Lise Getoor · 2013
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Pac-bayesian collective stability
Ben London, Bert Huang, Ben Taskar, and Lise Getoor · 2014
Cited alongside, same era.
Anti-discrimination analysis using privacy attack strategies
Salvatore Ruggieri, Sara Hajian, Faisal Kamiran, and Xiangliang Zhang · 2014
Cited alongside, same era.
Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
Cited alongside, same era.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
Discrimination-and privacy-aware patterns
Sara Hajian, Josep Domingo-Ferrer, Anna Monreale, Dino Pedreschi, and Fosca Giannotti · 2015
Cited alongside, same era.
Discrimination prevention using privacy preserving techniques
Asmita Kashid, Vrushali Kulkarni, and Ruhi Patankar · 2015
Cited alongside, same era.
On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy S. Liang · 2017
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Rene Vidal, Joan Bruna, Raja Giryes, and Stefano Soatto · 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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Measuring fairness in ranked outputs
Ke Yang and Julia Stoyanovich · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
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Using machine teaching to identify optimal training-set attacks on machine learners
Shike Mei and Xiaojin Zhu · 2015
Cited alongside, same era.
Classification, ranking, and top-k stability of recommendation algorithms
Gediminas Adomavicius and Jingjing Zhang · 2016
Cited alongside, same era.
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
Cited alongside, same era.
A confidence-based approach for balancing fairness and accuracy
Benjamin Fish, Jeremy Kun, and Ádám D Lelkes · 2016
Cited alongside, same era.
False positives, false negatives, and false analyses: A rejoinder to machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Anthony W Flores, Kristin Bechtel, and Christopher T Lowenkamp · 2016
Cited alongside, same era.
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
Cited alongside, same era.
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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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach · 2018
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Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
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Multiwinner voting with fairness constraints
L Elisa Celis, Lingxiao Huang, and Nisheeth K Vishnoi · 2018
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Fair and diverse dpp-based data summarization
L. Elisa Celis, Vijay Keswani, Damian Straszak, Amit Deshpande, Tarun Kathuria, and Nisheeth K. Vishnoi · 2018
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Ranking with fairness constraints
L. Elisa Celis, Damian Straszak, and Nisheeth K. Vishnoi · 2018
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Decoupled classifiers for group-fair and efficient machine learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Mark D. M. Leiserson · 2018
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Privacy for all: Ensuring fair and equitable privacy protections
Michael D Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan · 2018
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Analysis of classifiers’ robustness to adversarial perturbations
Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2018
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Generalization bounds for uniformly stable algorithms
Vitaly Feldman and Jan Vondrak · 2018
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Non-discriminatory machine learning through convex fairness criteria
Naman Goel, Mohammad Yaghini, and Boi Faltings · 2018
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Adaptive sensitive reweighting to mitigate bias in fairness-aware classification
Emmanouil Krasanakis, Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, and Yiannis Kompatsiaris · 2018
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Data-dependent stability of stochastic gradient descent
Ilja Kuzborskij and Christoph H. Lampert · 2018
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C. Williamson · 2018
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On the stability of feature selection algorithms
Sarah Nogueira, Konstantinos Sechidis, and Gavin Brown · 2018
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The use of machine learning algorithms in recommender systems: A systematic review
Ivens Portugal, Paulo S. C. Alencar, and Donald D. Cowan · 2018
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Towards controlling discrimination in online ad auctions
Elisa Celis, Anay Mehrotra, and Nisheeth Vishnoi · 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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Controlling polarization in personalization: An algorithmic framework
L Elisa Celis, Sayash Kapoor, Farnood Salehi, and Nisheeth K Vishnoi · 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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