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This paper presents fairlib, an open-source framework for assessing and improving classification fairness.
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John Rawls. 2001 · 2001
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Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013 · 2013
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Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017 · 2017
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Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al. 2018 · 2018
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Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
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Aequitas: A bias and fairness audit toolkit
Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T Rodolfa, and Rayid Ghani. 2018 · 2018
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez. 2019 · 2019
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Inherent tradeoffs in learning fair representations
Han Zhao and Geoff Gordon. 2019 · 2019
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Fairlearn: A toolkit for assessing and improving fairness in ai
Sarah Bird, Miro Dudík, Richard Edgar, Brandon Horn, Roman Lutz, Vanessa Milan, Mehrnoosh Sameki, Hanna Wallach, and Kathleen Walker. 2020 · 2020
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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 · 2020
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Connecting loss difference with equal opportunity for fair models
Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin, and Lea Frermann. 2022 · 2018
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Achieving fairness through adversarial learning: an application to recidivism prediction
Christina Wadsworth, Francesca Vera, and Chris Piech. 2018 · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
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Stereotypical bias removal for hate speech detection task using knowledge-based generalizations
Pinkesh Badjatiya, Manish Gupta, and Vasudeva Varma. 2019 · 2019
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
Cited alongside, same era.
Balancing out bias: Achieving fairness through training reweighting
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021a
Cited in the paper.
pandas-dev/pandas: Pandas
The pandas development team. 2020 · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
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Decoupling adversarial training for fair NLP
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2021b · 2021
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Fairbatch: Batch selection for model fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh. 2021 · 2021
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Contrastive learning for fair representations
Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin, and Lea Frermann. 2021 · 2021
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Towards equal opportunity fairness through adversarial learning
Xudong Han, Timothy Baldwin, and Trevor Cohn. 2022 · 2022
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