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An optimum character recognition system using decision functions
C. K. Chow · 1957
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Paul R. Rosenbaum and Donald B. Rubin · 1983
Earlier work this paper cites.
Targeted Learning: Causal Inference for Observational and Experimental Data
Mark J. van der Laan and Sherri Rose · 2011
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander J. Smola · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
F. Kamiran and T. Calders · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
Earlier work this paper cites.
Variations in skin colour and the biological consequences of ultraviolet radiation exposure
Sandra Del Bino and F Bernerd · 2013
Earlier work this paper cites.
Learning fair representations
Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
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Earlier work this paper cites.
Simultaneous deep transfer across domains and tasks
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Earlier work this paper cites.
Big data’s disparate impact
S. Barocas and A. Selbst · 2016
Earlier work this paper cites.
Learning with rejection
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Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
The perpetual line-up: Unregulated police face recognition in america
Clare Garvie · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
The problem with bias: from allocative to representational harms in machine learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi · 2017
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Amanda Bower, Sarah N. Kitchen, Laura Niss, Martin J. Strauss, Alexander Vargas, and Suresh Venkatasubramanian · 2017
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Alexandra Chouldechova · 2017
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UCI machine learning repository, 2017
Dua Dheeru and Efi Karra Taniskidou · 2017
Cited alongside, same era.
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Mohsan Alvi, Andrew Zisserman, and Christoffer Nellåker · 2018
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VGGFace2: A dataset for recognising faces across pose and age
Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman · 2018
Later among the works it cites.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
Later among the works it cites.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, and Massimiliano Pontil · 2018
Later among the works it cites.
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Improved training of Wasserstein GANs
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Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2017
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Interpretable learning for self-driving cars by visualizing causal attention
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Inherent trade-offs in the fair determination of risk scores
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Recycling privileged learning and distribution matching for fairness
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InclusiveFaceNet: Improving face attribute detection with race and gender diversity
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Cynthia Dwork and Christina Ilvento · 2018
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A comparative study of fairness-enhancing interventions in machine learning
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth · 2018
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Fairness without demographics in repeated loss minimization
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, and Kush R. Varshney · 2018
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Michele Merler, Nalini K. Ratha, Rogério Schmidt Feris, and John R. Smith · 2019
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Discovering fair representations in the data domain
Novi Quadrianto, Viktoriia Sharmanska, and Oliver Thomas · 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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