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Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate.
Neural Networks and the Bias/Variance Dilemma
Stuart Geman, Elie Bienenstock, and René Doursat · 1992
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The nature of statistical learning theory
Vladimir Vapnik · 1995
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Bias plus variance decomposition for zero-one loss functions
Ron Kohavi and David Wolpert · 1996
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Egalitarianism and the levelling down objection
Nils Holtug · 1998
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On the effect of data set size on bias and variance in classification learning
Damien Brain and Geoffrey Webb · 1999
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A unified bias-variance decomposition
Pedro Domingos · 2000
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Reconsidering the levelling-down objection against egalitarianism
Brett Doran · 2001
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Egalitarianism and the levelling down objection
Andrew Mason · 2001
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Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
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Shape distributions
Robert Osada, Thomas Funkhouser, Bernard Chazelle, and David Dobkin · 2002
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Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
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Rethinking bias-variance trade-off for generalization of neural networks
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, and Yi Ma · 2002
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Giving up levelling down
Campbell Brown · 2003
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Smoteboost: Improving prediction of the minority class in boosting
Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer · 2003
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Borderline-smote: A new over-sampling method in imbalanced data sets learning
Hui Han, Wen-Yuan Wang, and Bing-Huan Mao · 2005
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Dataset issues in object recognition
Jean Ponce, Tamara L Berg, Mark Everingham, David A Forsyth, Martial Hebert, Svetlana Lazebnik, Marcin Marszalek, Cordelia Schmid, Bryan C Russell, Antonio Torralba, et al · 2006
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Inequality, injustice and levelling down
Thomas Christiano and Will Braynen · 2008
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo A. Garcia, and Shutao Li · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The Elements of Statistical Learning — Data Mining, Inference, and Prediction
T. Hastie, R. Tibshirani, and J. Friedman · 2009
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Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Face recognition performance: Role of demographic information
Brendan F Klare, Mark J Burge, Joshua C Klontz, Richard W Vorder Bruegge, and Anil K Jain · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Classifier adaptation at prediction time
Amélie Royer and Christoph H. Lampert · 2015
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2016
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Satisfying real-world goals with dataset constraints
Gabriel Goh, Andrew Cotter, Maya Gupta, and Michael Friedlander · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Playing for data: Ground truth from computer games
Stephan R. Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
Seeing is not necessarily believing: Limitations of biggans for data augmentation
Suman Ravuri and Oriol Vinyals · 2019
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Fairness gan: Generating datasets with fairness properties using a generative adversarial network
Prasanna Sattigeri, Samuel C Hoffman, Vijil Chenthamarakshan, and Kush R Varshney · 2019
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Racial faces in the wild: Reducing racial bias by information maximization adaptation network
Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, and Yaohai Huang · 2019
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Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez · 2019
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Unlocking fairness: a trade-off revisited
Michael Wick, Swetasudha Panda, and Jean-Baptiste Tristan · 2019
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Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed Huai-hsin Chi · 2017
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
Cited alongside, same era.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Cited alongside, same era.
Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2018
Cited alongside, same era.
Analysis of gender inequality in face recognition accuracy
Vitor Albiero, Krishnapriya KS, Kushal Vangara, Kai Zhang, Michael C King, and Kevin W Bowyer · 2020
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Fair generative modeling via weak supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, and Stefano Ermon · 2020
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Too relaxed to be fair
Michael Lohaus, Michael Perrot, and Ulrike von Luxburg · 2020
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Minimax Pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
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Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertrana, and Guillermo Sapiro · 2020
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Fnnc: Achieving fairness through neural networks
Manisha Padala and Sujit Gujar · 2020
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Adversarial latent autoencoders
Stanislav Pidhorskyi, Donald A Adjeroh, and Gianfranco Doretto · 2020
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Tackling algorithmic bias in neural-network classifiers using wasserstein-2 regularization, 2020
Laurent Risser, Quentin Vincenot, and Jean-Michel Loubes · 2020
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Interpreting the latent space of GANs for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
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Bias preservation in machine learning: the legality of fairness metrics under eu non-discrimination law
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation
Zeyu Wang, Klint Qinami, Ioannis Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 2020
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Active sampling for min-max fairness
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, and Jie Zhang · 2021
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Evaluating fairness of machine learning models under uncertain and incomplete information
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, and Xuezhi Wang · 2021
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Towards causal benchmarking of biasin face analysis algorithms
Guha Balakrishnan, Yuanjun Xiong, Wei Xia, and Pietro Perona · 2021
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Fair mixup: Fairness via interpolation
Ching-Yao Chuang and Youssef Mroueh · 2021
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Minimax group fairness: Algorithms and experiments
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth · 2021
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Invgan: Invertible gans, 2021
Partha Ghosh, Dominik Zietlow, Michael J. Black, Larry S. Davis, and Xiaochen Hu · 2021
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Can we obtain fairness for free?
Rashidul Islam, Shimei Pan, and James R Foulds · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G. Northcutt, Anish Athalye, and Jonas Mueller · 2021
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Fair bayesian optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar, Robin Schmucker, Krishnaram Kenthapadi, and Cédric Archambeau · 2021
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Fair attribute classification through latent space de-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, and Olga Russakovsky · 2021
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Matched sample selection with gans for mitigating attribute confounding
Chandan Singh, Guha Balakrishnan, and Pietro Perona · 2021
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How fair can we go in machine learning? assessing the boundaries of accuracy and fairness
Ana Valdivia, Javier Sánchez-Monedero, and Jorge Casillas · 2021
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Pairwise fairness for ordinal regression
Matthäus Kleindessner, Samira Samadi, Muhammad Bilal Zafar, Krishnaram Kenthapadi, and Chris Russell · 2022
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