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Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.
Statistical methods for research workers
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Mahalanobis, Prasanta Chandra · 1936
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Bhattacharyya, Anil · 1943
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Comparing individual means in the analysis of variance
Tukey, John W · 1949
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Verification of forecasts expressed in terms of probability
Brier, Glenn W · 1950
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Nearest neighbor pattern classification
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Devijver, Pierre A. and Kittler, Josef · 1982
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Bayes error estimation using parzen and k-nn procedures
Fukunaga, Keinosuke and Hummels, Donald M · 1987
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Bootstrap methods: another look at the jackknife
Efron, Bradley · 1992
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Amari, Shun-Ichi · 1993
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Estimating the bayes error rate through classifier combining
Tumer, Kagan and Ghosh, Joydeep · 1996
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Lower bounds for bayes error estimation
Antos, András, Devroye, Luc, and Gyorfi, Laszlo · 1999
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A unified bias-variance decomposition
Domingos, Pedro · 2000
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Interval estimation for a binomial proportion
Brown, Lawrence D, Cai, T Tony, and DasGupta, Anirban · 2001
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Latent dirichlet allocation
Blei, David M, Ng, Andrew Y, and Jordan, Michael I · 2003
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Estimating dataset size requirements for classifying dna microarray data
Mukherjee, Sayan, Tamayo, Pablo, Rogers, Simon, Rifkin, Ryan, Engle, Anna, Campbell, Colin, Golub, Todd R, and Mesirov, Jill P · 2003
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Dataset shift in machine learning
Quionero-Candela, Joaquin, Sugiyama, Masashi, Schwaighofer, Anton, and Lawrence, Neil D · 2009
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Three naive bayes approaches for discrimination-free classification
Calders, Toon and Verwer, Sicco · 2010
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Discrimination aware decision tree learning
Kamiran, Faisal, Calders, Toon, and Pechenizkiy, Mykola · 2010
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Data mining for discrimination discovery
Ruggieri, Salvatore, Pedreschi, Dino, and Turini, Franco · 2010
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Fairness-aware learning through regularization approach
Kamishima, Toshihiro, Akaho, Shotaro, and Sakuma, Jun · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Fairness through awareness
Dwork, Cynthia, Hardt, Moritz, Pitassi, Toniann, Reingold, Omer, and Zemel, Richard · 2012
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Mimic-iii, a freely accessible critical care database
Johnson, Alistair EW, Pollard, Tom J, Shen, Lu, Lehman, Li-wei H, Feng, Mengling, Ghassemi, Mohammad, Moody, Benjamin, Szolovits, Peter, Celi, Leo Anthony, and Mark, Roger G · 2016
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Inherent trade-offs in the fair determination of risk scores
Kleinberg, Jon, Mullainathan, Sendhil, and Raghavan, Manish · 2016
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Learning fair classifiers: A regularization-inspired approach
Bechavod, Yahav and Ligett, Katrina · 2017
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Optimized pre-processing for discrimination prevention
Calmon, Flavio, Wei, Dennis, Vinzamuri, Bhanukiran, Ramamurthy, Karthikeyan Natesan, and Varshney, Kush R · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
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Koepke, Hoyt and Bilenko, Mikhail · 2012
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Unsupervised pattern discovery in electronic health care data using probabilistic clustering models
Marlin, Benjamin M, Kale, David C, Khemani, Robinder G, and Wetzel, Randall C · 2012
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A methodology for direct and indirect discrimination prevention in data mining
Hajian, Sara and Domingo-Ferrer, Josep · 2013
Cited alongside, same era.
UCI machine learning repository, 2013
Lichman, M · 2013
Cited alongside, same era.
Learning fair representations
Zemel, Richard S, Wu, Yu, Swersky, Kevin, Pitassi, Toniann, and Dwork, Cynthia · 2013
Cited alongside, same era.
Unfolding physiological state: Mortality modelling in intensive care units
Ghassemi, Marzyeh, Naumann, Tristan, Doshi-Velez, Finale, Brimmer, Nicole, Joshi, Rohit, Rumshisky, Anna, and Szolovits, Peter · 2014
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Domhan, Tobias, Springenberg, Jost Tobias, and Hutter, Frank · 2015
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Chouldechova, Alexandra · 2017
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Algorithmic decision making and the cost of fairness
Corbett-Davies, Sam, Pierson, Emma, Feller, Avi, Goel, Sharad, and Huq, Aziz · 2017
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Runaway feedback loops in predictive policing
Ensign, Danielle, Friedler, Sorelle A., Neville, Scott, Scheidegger, Carlos Eduardo, and Venkatasubramanian, Suresh · 2017
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Goodreads book reviews, 2017
Gnanesh · 2017
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Calibration for the (computationally-identifiable) masses
Hébert-Johnson, Ursula, Kim, Michael P, Reingold, Omer, and Rothblum, Guy N · 2017
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Deep learning scaling is predictable, empirically
Hestness, Joel, Narang, Sharan, Ardalani, Newsha, Diamos, Gregory, Jun, Heewoo, Kianinejad, Hassan, Patwary, Md, Ali, Mostofa, Yang, Yang, and Zhou, Yanqi · 2017
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Counterfactual fairness
Kusner, Matt J, Loftus, Joshua, Russell, Chris, and Silva, Ricardo · 2017
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On fairness and calibration
Pleiss, Geoff, Raghavan, Manish, Wu, Felix, Kleinberg, Jon, and Weinberger, Kilian Q · 2017
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Learning non-discriminatory predictors
Woodworth, Blake, Gunasekar, Suriya, Ohannessian, Mesrob I, and Srebro, Nathan · 2017
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Fairness constraints: Mechanisms for fair classification
Zafar, Muhammad Bilal, Valera, Isabel, Gomez Rodriguez, Manuel, and Gummadi, Krishna P · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, Joy and Gebru, Timnit · 2018
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A comparative study of fairness-enhancing interventions in machine learning
Friedler, Sorelle A, Scheidegger, Carlos, Venkatasubramanian, Suresh, Choudhary, Sonam, Hamilton, Evan P, and Roth, Derek · 2018
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Delayed impact of fair machine learning
Liu, Lydia T, Dean, Sarah, Rolf, Esther, Simchowitz, Max, and Hardt, Moritz · 2018
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