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Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
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On the method of bounded differences
Colin McDiarmid · 1989
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Game theory, on-line prediction and boosting
Yoav Freund and Robert E Schapire · 1996
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Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid
Ron Kohavi · 1996
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Use of geocoding and surname analysis to estimate race and ethnicity
Kevin Fiscella and Allen M Fremont · 2006
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Efficient projections onto the l 1-ball for learning in high dimensions
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Your equal credit opportunity rights, January 2013
Federal Trade Commission · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Using the bayesian improved surname geocoding method (bisg) to create a working classification of race and ethnicity in a diverse managed care population: a validation study
Dzifa Adjaye-Gbewonyo, Robert A Bednarczyk, Robert L Davis, and Saad B Omer · 2014
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Using publicly available information to proxy for unidentified race and ethnicity, June 2014
Consumer Financial Protection Bureau · 2014
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Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2014
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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Peter Kairouz, Keith Bonawitz, and Daniel Ramage · 2016
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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Effectiveness of equalized odds for fair classification under imperfect group information
Pranjal Awasthi, Matthäus Kleindessner, and Jamie Morgenstern · 2019
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The cost of a reductions approach to private fair optimization
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Fairness and Machine Learning
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Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan and Vitaly Shmatikov · 2019
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Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
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Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
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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 · 2018
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Maya Gupta, Andrew Cotter, Mahdi Milani Fard, and Serena Wang · 2018
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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman · 2018
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Blind justice: Fairness with encrypted sensitive attributes
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Fairness under unawareness: Assessing disparity when protected class is unobserved
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, and Madeleine Udell · 2019
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Assessing algorithmic fairness with unobserved protected class using data combination
Nathan Kallus, Xiaojie Mao, and Angela Zhou · 2019
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Noise-tolerant fair classification
Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon, and Nakul Verma · 2019
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Apple card investigated after gender discrimination complaints, November 2019
Neil Vigdor · 2019
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Achieving differential privacy and fairness in logistic regression
Depeng Xu, Shuhan Yuan, and Xintao Wu · 2019
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Robust optimization for fairness with noisy protected groups
Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Michael I Jordan · 2020
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