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We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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The ladder: A reliable leaderboard for machine learning competitions
Avrim Blum and Moritz Hardt · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
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Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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We need bug bounties for bad algorithms
Amit Elazari Bar On · 2018
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Probably approximately metric-fair learning
Gal Yona and Guy Rothblum · 2018
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Recovering from biased data: Can fairness constraints improve accuracy?
Avrim Blum and Kevin Stangl · 2019
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An algorithmic framework for fairness elicitation
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu · 2019
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An empirical study of rich subgroup fairness for machine learning
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
Cited alongside, same era.
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
Cited alongside, same era.
Advancing subgroup fairness via sleeping experts
Avrim Blum and Thodoris Lykouris · 2020
Cited alongside, same era.
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush Varshney · 2020
Cited alongside, same era.
Metric learning for individual fairness
Christina Ilvento · 2020
Lexicographically fair learning: Algorithms and generalization
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, and Saeed Sharifi-Malvajerdi · 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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Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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Outcome indistinguishability
Cynthia Dwork, Michael P Kim, Omer Reingold, Guy N Rothblum, and Gal Yona · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
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Online multivalid learning: Means, moments, and prediction intervals
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Cited alongside, same era.
A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld · 2020
Cited alongside, same era.
Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
Cited alongside, same era.
Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2021
Cited alongside, same era.
Introducing twitter’s first algorithmic bias bounty challenge
Rumman Chowdhury and Jutta Williams · 2021
Cited alongside, same era.
The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth
Cited in the paper.
Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth
Cited in the paper.
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M Pai, and Aaron Roth · 2021
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Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh M Pai, Aaron Roth, and Rakesh Vohra · 2021
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Online multiobjective minimax optimization and applications
Georgy Noarov, Mallesh Pai, and Aaron Roth · 2021
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Multi-group agnostic pac learnability
Guy N Rothblum and Gal Yona · 2021
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Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher Tosh and Daniel Hsu · 2021
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