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We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning.
Two further applications of a model for binary regression
David R. Cox · 1958
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T. Anne Cleary · 1966
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Test bias: Prediction of grades of negro and white students in integrated colleges
T. Anne Cleary · 1968
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Another look at “cultural fairness”
Richard B Darlington · 1971
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Reliability of subjective probability forecasts of precipitation and temperature
Allan H. Murphy and Robert L. Winkler · 1977
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The well-calibrated bayesian
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The comparison and evaluation of forecasters
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
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Predicting good probabilities with supervised learning
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Convexity, classification, and risk bounds
Peter L Bartlett, Michael I Jordan, and Jon D McAuliffe · 2006
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Szemerédi’s regularity lemma revisited
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Alexandre B. Tsybakov · 2008
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An analysis of random design linear regression
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The hidden biases in big data
Kate Crawford · 2013
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Certifying and removing disparate impact
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An introduction to matrix concentration inequalities
Joel A. Tropp · 2015
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Thoughts on machine learning accuracy
Sam Corbett-Davies, Sharad Goel, and Sandra González-Bailón · 2017
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The trouble with bias
Kate Crawford · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Efi Karra Taniskidou · 2017
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael J. Kearns, Seth Neel, 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 Kleinberg, and Kilian Q Weinberger · 2017
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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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Compas risk scales: Demonstrating accuracy equity and predictive parity, 2016
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebo · 2016
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Best-of-K-bandits
Max Simchowitz, Kevin Jamieson, and Benjamin Recht · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2018
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The accuracy, fairness, and limits of predicting recidivism
Julia Dressel and Hany Farid · 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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Multicalibration: Calibration for the (Computationally-identifiable) masses
Ursula Hebert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
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Algorithmic fairness
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan · 2018
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Delayed impact of fair machine learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt · 2018
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