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In many machine learning applications, there are multiple decision-makers involved, both automated and human.
An optimum character recognition system using decision function
C. Chow · 1957
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On optimum recognition error and reject trade-off
C. Chow · 1970
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Clinical versus actuarial judgment
Robyn M Dawes, David Faust, and Paul E Meehl · 1989
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Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
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Differences between entrepreneurs and managers in large organizations: Biases and heuristics in strategic decision-making
Lowell W Busenitz and Jay B Barney · 1997
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Classifying without discriminating
F. Kamiran and T. Calders · 2009
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Beat the machine: Challenging workers to find the unknown unknowns
Josh Attenberg, Panagiotis G Ipeirotis, and Foster J Provost · 2011
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Extraneous factors in judicial decisions
Shai Danziger, Jonathan Levav, and Liora Avnaim-Pesso · 2011
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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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Actuarial sentencing: An “unsettled” proposition
Kelly Hannah-Moffat · 2013
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Learning Fair Representations
Richard Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Jenna Burrell · 2016
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Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
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A probabilistic classifier model with adaptive rejection option
Lydia Fischer and Thomas Villmann · 2016
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Cooperative inverse reinforcement learning
Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan · 2016
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Fair Pipelines
Amanda Bower, Sarah N. Kitchen, Laura Niss, Martin J. Strauss, Alexander Vargas, and Suresh Venkatasubramanian · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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On Fairness, Diversity and Randomness in Algorithmic Decision Making
Nina Grgić-Hlaca, Muhammad Bilal Zafar, Krishna P. Gummadi, and Adrian Weller · 2017
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Categorical reparameterization with gumbel-softmax
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
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Lauren Kirchner and Jeff Larson · 2016
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Inherent Trade-Offs in the Fair Determination of Risk Scores
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Learning Fair Classifiers: A Regularization-Inspired Approach
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On the safety of machine learning: Cyber-physical systems, decision sciences, and data products
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IDK Cascades: Fast Deep Learning by Learning not to Overthink
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
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