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Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users.
Evaluating the predictive validity of the compas risk and needs assessment system
Tim Brennan, William Dieterich, and Beate Ehret · 2009
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Robust optimization
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Consumer credit-risk models via machine-learning algorithms
Amir E Khandani, Adlar J Kim, and Andrew W Lo · 2010
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Learning fair representations
Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 2013
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Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam Tauman Kalai · 2016
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Big data’s disparate impact
Solon Barocas and Andrew D. Selbst · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2016
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2016
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Machine bias, 2016
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Daniel McNamara, Cheng Soon Ong, and Robert C Williamson · 2017
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Counterfactual fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2017
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A causal framework for discovering and removing direct and indirect discrimination
Lu Zhang, Yongkai Wu, and Xintao Wu · 2017
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Reluplex: An efficient SMT solver for verifying deep neural networks
Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
LSAC national longitudinal bar passage study, 2017
F. Linda Wightman · 2017
Cited alongside, same era.
Formal verification of piece-wise linear feed-forward neural networks
Rüdiger Ehlers · 2017
Cited alongside, same era.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2018
Cited alongside, same era.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Costs and benefits of fair representation learning
Daniel McNamara, Cheng Soon Ong, and Robert C. Williamson · 2019
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Learning fair representations via an adversarial framework
Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, and Chunping Wang · 2019
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A center in your neighborhood: Fairness in facility location
Christopher Jung, Sampath Kannan, and Neil Lutz · 2019
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Fairness through causal awareness: Learning causal latent-variable models for biased data
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2019
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Differentially private fair learning
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman · 2019
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Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
A spectral view of adversarially robust features
Shivam Garg, Vatsal Sharan, Brian Hu Zhang, and Gregory Valiant · 2018
Cited alongside, same era.
Fairness Under Composition
Cynthia Dwork and Christina Ilvento · 2018
Cited alongside, same era.
AI2: safety and robustness certification of neural networks with abstract interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin T. Vechev · 2018
Cited alongside, same era.
Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
Cited alongside, same era.
Considerations for Evaluation and Generalization in Interpretable Machine Learning
Finale Doshi-Velez and Been Kim · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Achieving differential privacy and fairness in logistic regression
Depeng Xu, Shuhan Yuan, and Xintao Wu · 2019
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Perfectly parallel fairness certification of neural networks
Caterina Urban, Maria Christakis, Valentin Wüstholz, and Fuyuan Zhang · 2019
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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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Training individually fair ML models with sensitive subspace robustness
Mikhail Yurochkin, Amanda Bower, and Yuekai Sun · 2020
Closest in time.
Metric Learning for Individual Fairness
Christina Ilvento · 2020
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Two simple ways to learn individual fairness metrics from data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun · 2020
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Extracting robust and accurate features via a robust information bottleneck
A. Pensia, V. Jog, and P. Loh · 2020
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Learning adversarially robust representations via worst-case mutual information maximization
Sicheng Zhu, Xiao Zhang, and David Evans · 2020
Closest in time.
Individual fairness for k k -clustering
Sepideh Mahabadi and Ali Vakilian · 2020
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Learning individually fair classifier with path-specific causal-effect constraint
Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, and Hisashi Kashima · 2020
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Individual Fairness in Pipelines
Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan · 2020
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Individual fairness revisited: Transferring techniques from adversarial robustness
Samuel Yeom and Matt Fredrikson · 2020
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Verifying individual fairness in machine learning models
Philips George John, Deepak Vijaykeerthy, and Diptikalyan Saha · 2020
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