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Dataset bias is one of the prevailing causes of unfairness in machine learning.
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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Active learning for biomedical citation screening
Byron C Wallace, Kevin Small, Carla E Brodley, and Thomas A Trikalinos · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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Large-scale live active learning: Training object detectors with crawled data and crowds
Sudheendra Vijayanarasimhan and Kristen Grauman · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Proxy non-discrimination in data-driven systems
Anupam Datta, Matt Fredrikson, Gihyuk Ko, Piotr Mardziel, and Shayak Sen · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
Cited alongside, same era.
Gradient reversal against discrimination
Edward Raff and Jared Sylvester · 2018
Cited alongside, same era.
Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
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Uncovering and mitigating algorithmic bias through learned latent structure
Alexander Amini, Ava P. Soleimany, Wilko Schwarting, Sangeeta N. Bhatia, and Daniela Rus · 2019
Cited alongside, same era.
Active fairness in algorithmic decision making
Alejandro Noriega-Campero, Michiel A Bakker, Bernardo Garcia-Bulle, and Alex’Sandy’ Pentland · 2019
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Active learning for imbalanced datasets
Umang Aggarwal, Adrian Popescu, and Céline Hudelot · 2020
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Hadis Anahideh, Abolfazl Asudeh, and Saravanan Thirumuruganathan · 2020
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Bayesian active learning for production, a systematic study and a reusable library, 2020
Parmida Atighehchian, Frédéric Branchaud-Charron, and Alexandre Lacoste · 2020
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Fairness in machine learning: A survey
Simon Caton and Christian Haas · 2020
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Abigail Z Jacobs and Hanna Wallach · 2019
Cited alongside, same era.
Repair: Removing representation bias by dataset resampling
Yi Li and Nuno Vasconcelos · 2019
Cited alongside, same era.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
Cited alongside, same era.
Promoting fairness in learned models by learning to active learn under parity constraints
Amr Sharaf and Hal Daumé III
Cited in the paper.
Synbols: Probing learning algorithms with synthetic datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, and David Vázquez · 2020
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Measuring non-expert comprehension of machine learning fairness metrics
Debjani Saha, Candice Schumann, Duncan Mcelfresh, John Dickerson, Michelle Mazurek, and Michael Tschantz · 2020
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
Later among the works it cites.