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Research has shown that, machine learning models might inherit and propagate undesired social biases encoded in the data.
A theory of justice: Revised edition
John Rawls · 1999
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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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Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Generic methods for optimization-based modeling
Justin Domke · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Decision theory for discrimination-aware classification
Faisal Kamiran, Asim Karim, and Xiangliang Zhang · 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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Fairbatch: Batch selection for model fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Certifying and removing disparate impact
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Eric Price, and Nati Srebro · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning fair classifiers: A regularization-inspired approach
Yahav Bechavod and Katrina Ligett · 2017
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Optimized pre-processing for discrimination prevention
Flavio P Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Classification with fairness constraints: A meta-algorithm with provable guarantees
L Elisa Celis, Lingxiao Huang, Vijay Keswani, and Nisheeth K Vishnoi · 2019
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Generalized inner loop meta-learning
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, and Soumith Chintala · 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudik, and Hanna Wallach · 2019
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Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, and Massimiliano Pontil · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Deep bilevel learning
Simon Jenni and Paolo Favaro · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, and Sergey Levine · 2019
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Truncated back-propagation for bilevel optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots · 2019
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Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi · 2020
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Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
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Amazon scraps secret ai recruiting tool that showed bias against women
Jeffrey Dastin · 2021
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How we analyzed the compas recidivism algorithm, 2016
Lauren Kirchner Jeff Larson, Surya Mattu and Julia Angwin · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks, 2021
Curtis G. Northcutt, Anish Athalye, and Jonas Mueller · 2021
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