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Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good.
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Bias in computer systems
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Mitigating gender bias in captioning systems
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Data preprocessing techniques for classification without discrimination
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Microsoft coco: Common objects in context
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
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Microsoft coco captions: Data collection and evaluation server
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”i always assumed that i wasn’t really that close to [her]”: Reasoning about invisible algorithms in news feeds
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Certifying and removing disparate impact
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Unequal Representation and Gender Stereotypes in Image Search Results for Occupations , page 3819–3828. Association for Computing Machinery, New York, NY, USA
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Learning fair classifiers
M. Zafar, I. Valera, M. G. Rodriguez, and K. Gummadi. 2015 · 2015
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016 · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro. 2016 · 2016
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, J. Bryson, and A. Narayanan. 2017 · 2017
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Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. 2017 · 2017
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Deep visual-semantic alignments for generating image descriptions
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Stacked cross attention for image-text matching
Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. 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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Learning representations by maximizing mutual information across views
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019 · 2019
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Fairness in recommendation ranking through pairwise comparisons
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Gender as a variable in natural-language processing: Ethical considerations
Brian Larson. 2017 · 2017
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Competent Men and Warm Women: Gender Stereotypes and Backlash in Image Search Results , page 6620–6631. Association for Computing Machinery, New York, NY, USA
Jahna Otterbacher, Jo Bates, and Paul Clough. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Fairness constraints: Mechanisms for fair classification
M. Zafar, I. Valera, M. Gomez-Rodriguez, and K. Gummadi. 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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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna M. Wallach. 2018 · 2018
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Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, M. Gupta, S. You, and K. Sridharan. 2019 · 2019
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Explicit bias discovery in visual question answering models
Varun Manjunatha, Nirat Saini, and Larry S. Davis. 2019 · 2019
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Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings
Thomas Manzini, Lim Yao Chong, Alan W Black, and Yulia Tsvetkov. 2019 · 2019
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Inherent tradeoffs in learning fair representations
Han Zhao and Geoff Gordon. 2019 · 2019
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Large-scale adversarial training for vision-and-language representation learning
Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, and Jingjing Liu. 2020 · 2020
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Contrastive representation learning: A framework and review
P. H. Le-Khac, G. Healy, and A. F. Smeaton. 2020 · 2020
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Controlling Fairness and Bias in Dynamic Learning-to-Rank , page 429–438. Association for Computing Machinery, New York, NY, USA
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020 · 2020
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Female librarians and male computer programmers? gender bias in occupational images on digital media platforms
Vivek K. Singh, Mary Chayko, Raj Inamdar, and Diana Floegel. 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, Chris Hallacy, Aditya Ramesh, G. Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, J. Clark, G. Krüger, and Ilya Sutskever. 2021 · 2021
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Image representations learned with unsupervised pre-training contain human-like biases
Ryan Steed and Aylin Caliskan. 2021 · 2021
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