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Extensive efforts have been made to understand and improve the fairness of machine learning models based on observational metrics, especially in high-stakes domains such as medical insurance, education, and hiring decisions.
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Ton Steerneman · 1983
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Reviewing qualitative research: Proposed criteria for fairness and rigor
Jeffrey A Gliner · 1994
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Linda F Wightman · 1998
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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Andreas Maurer and Massimiliano Pontil · 2009
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni 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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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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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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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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Robust sensitivity analysis for stochastic systems
Henry Lam · 2016
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Fairsquare: probabilistic verification of program fairness
Aws Albarghouthi, Loris D’Antoni, Samuel Drews, and Aditya V Nori · 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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The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables
Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2017
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, Riccardo Volpi, and John Duchi · 2017
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
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Probabilistic verification of fairness properties via concentration
Osbert Bastani, Xin Zhang, and Armando Solar-Lezama · 2019
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Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
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Variance-based regularization with convex objectives
John Duchi and Hongseok Namkoong · 2019
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Learning certified individually fair representations
Anian Ruoss, Mislav Balunovic, Marc Fischer, and Martin Vechev · 2020
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Fairness by learning orthogonal disentangled representations
Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, and Shadi Albarqouni · 2020
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Perfectly parallel fairness certification of neural networks
Caterina Urban, Maria Christakis, Valentin Wüstholz, and Fuyuan Zhang · 2020
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Individual fairness revisited: Transferring techniques from adversarial robustness
Samuel Yeom and Matt Fredrikson · 2020
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How do fair decisions fare in long-term qualification?
Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellstrom, Kun Zhang, and Cheng Zhang · 2020
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Jiachun Liao, Chong Huang, Peter Kairouz, and Lalitha Sankar · 2019
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The implicit fairness criterion of unconstrained learning
Lydia T Liu, Max Simchowitz, and Moritz Hardt · 2019
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On the fairness of disentangled representations
Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem · 2019
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Costs and benefits of fair representation learning
Daniel McNamara, Cheng Soon Ong, and Robert C. Williamson · 2019
Cited alongside, same era.
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, et al · 2019
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Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2019
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
Robin Winter, Floriane Montanari, Frank Noé, and Djork-Arné Clevert · 2019
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Mislav Balunović, Anian Ruoss, and Martin Vechev · 2021
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Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2021
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Statistics of robust optimization: A generalized empirical likelihood approach
John C Duchi, Peter W Glynn, and Hongseok Namkoong · 2021
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On transferability of bias mitigation effects in language model fine-tuning
Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, and Xiang Ren · 2021
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Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, and Yuekai Sun · 2021
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Latent space smoothing for individually fair representations
Momchil Peychev, Anian Ruoss, Mislav Balunović, Maximilian Baader, and Martin Vechev · 2021
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Unintended selection: Persistent qualification rate disparities and interventions
Reilly Raab and Yang Liu · 2021
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Sample selection for fair and robust training
Yuji Roh, Kangwook Lee, Steven Whang, and Changho Suh · 2021
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Fairness in the eyes of the data: Certifying machine-learning models
Shahar Segal, Yossi Adi, Benny Pinkas, Carsten Baum, Chaya Ganesh, and Joseph Keshet · 2021
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Adaptive sampling for minimax fair classification
Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, and Tara Javidi · 2021
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Fairness transferability subject to bounded distribution shift
Yatong Chen, Reilly Raab, Jialu Wang, and Yang Liu · 2022
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Heritage health prize kaggle
Kaggle Inc · 2022
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Certifying out-of-domain generalization for blackbox functions
Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, and Ce Zhang · 2022
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