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With the recent growth in computer vision applications, the question of how fair and unbiased they are has yet to be explored.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Matrix analysis
Roger A Horn and Charles R Johnson · 2012
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Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Earlier work this paper cites.
A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 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
Earlier work this paper cites.
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Mohsan Alvi, Andrew Zisserman, and Christoffer Nellåker · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
Cited alongside, same era.
Image counterfactual sensitivity analysis for detecting unintended bias
Emily Denton, Ben Hutchinson, Margaret Mitchell, Timnit Gebru, and Andrew Zaldivar · 2019
Cited alongside, same era.
Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads
Anja Lambrecht and Catherine Tucker · 2019
Cited alongside, same era.
Fairalm: Augmented lagrangian method for training fair models with little regret
Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N Ravi, and Vikas Singh · 2020
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Dana Pessach and Erez Shmueli · 2020
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Contrastive examples for addressing the tyranny of the majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell, and Novi Quadrianto · 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
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Discovering fair representations in the data domain
Novi Quadrianto, Viktoriia Sharmanska, and Oliver Thomas · 2019
Cited alongside, same era.
Fairness gan: Generating datasets with fairness properties using a generative adversarial network
Prasanna Sattigeri, Samuel C Hoffman, Vijil Chenthamarakshan, and Kush R Varshney · 2019
Cited alongside, same era.
Mitigate bias in face recognition using skewness-aware reinforcement learning
Mei Wang and Weihong Deng · 2019
Cited alongside, same era.
Fair generative modeling via weak supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, and Stefano Ermon · 2020
Cited alongside, same era.
Fairfacegan: Fairness-aware facial image-to-image translation
Sunhee Hwang, Sungho Park, Dohyung Kim, Mirae Do, and Hyeran Byun · 2020
Cited alongside, same era.
Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf
Cited in the paper.
Kernel methods for measuring independence
Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf, et al
Cited in the paper.
Tabfairgan: Fair tabular data generation with generative adversarial networks
Amirarsalan Rajabi and Ozlem Ozmen Garibay · 2021
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Fair attribute classification through latent space de-biasing
Vikram V Ramaswamy, Sunnie SY Kim, and Olga Russakovsky · 2021
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Towards fair cross-domain adaptation via generative learning
Tongxin Wang, Zhengming Ding, Wei Shao, Haixu Tang, and Kun Huang · 2021
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Consistent instance false positive improves fairness in face recognition
Xingkun Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang, Yong Li, and Zhen Cui · 2021
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