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Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models.
Gender inequality
Judith Lorber · 2001
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Gender trouble
Judith Butler · 2002
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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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How many pixels make an image?
Antonio Torralba · 2009
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Framed by gender: How gender inequality persists in the modern world
Cecilia L Ridgeway · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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A survey of current datasets for vision and language research
Francis Ferraro, Nasrin Mostafazadeh, Lucy Vanderwende, Jacob Devlin, Michel Galley, Margaret Mitchell, et al · 2015
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Unequal representation and gender stereotypes in image search results for occupations
Matthew Kay, Cynthia Matuszek, and Sean A Munson · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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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, Alexander C. Berg, and Li Fei-Fei · 2015
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Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Eric Price, and Nati Srebro · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Stereotyping and bias in the Flickr30K dataset
Emiel van Miltenburg · 2016
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Joint face detection and alignment using multitask cascaded convolutional networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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The problem with bias: Allocative versus representational harms in machine learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
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Gender as a variable in natural-language processing: Ethical considerations
Brian Larson · 2017
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Competent men and warm women: Gender stereotypes and backlash in image search results
Jahna Otterbacher, Jo Bates, and Paul Clough · 2017
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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No classification without representation: Assessing geodiversity issues in open data sets for the developing world
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D Sculley · 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
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Mohsan S. Alvi, Andrew Zisserman, and Christoffer Nellåker · 2018
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Fairness and machine learning limitations and opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
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Decoupled classifiers for group-fair and efficient machine learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson · 2018
Cited alongside, same era.
Gender recognition or gender reductionism? the social implications of embedded gender recognition systems
Foad Hamidi, Morgan Klaus Scheuerman, and Stacy M Branham · 2018
Cited alongside, same era.
Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach · 2018
Cited alongside, same era.
Co-designing checklists to understand organizational challenges and opportunities around fairness in ai
Michael A Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach · 2020
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How we’ve taught algorithms to see identity: Constructing race and gender in image databases for facial analysis
Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R. Brubaker · 2020
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Diagnosing gender bias in image recognition systems
Carsten Schwemmer, Carly Knight, Emily D Bello-Pardo, Stan Oklobdzija, Martijn Schoonvelde, and Jeffrey W Lockhart · 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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Don’t judge an object by its context: Learning to overcome contextual bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, and Deepti Ghadiyaram · 2020
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Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski · 2018
Cited alongside, same era.
The misgendering machines: Trans/HCI implications of automatic gender recognition
Os Keyes · 2018
Cited alongside, same era.
Algorithms of Oppression: How Search Engines Reinforce Racism
Safiya Umoja Noble · 2018
Cited alongside, same era.
Predicting cardiovascular risk factors in retinal fundus photographs using deep learning
Ryan Poplin, Avinash Vaidyanathan Varadarajan, Katy Blumer, Yun Liu, Mike McConnell, Greg Corrado, Lily Peng, and Dale Webster · 2018
Cited alongside, same era.
Gender as a social structure
Barbara J Risman · 2018
Cited alongside, same era.
Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
Pierre Stock and Moustapha Cisse · 2018
Cited alongside, same era.
Exposing and correcting the gender bias in image captioning datasets and models
Shruti Bhargava and David Forsyth · 2019
Cited alongside, same era.
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REVISE: A tool for measuring and mitigating bias in visual datasets
Angelina Wang, Arvind Narayanan, and Olga Russakovsky · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation
Zeyu Wang, Klint Qinami, Ioannis Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 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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Pass: An imagenet replacement for self-supervised pretraining without humans
Yuki M. Asano, Christian Rupprecht, Andrew Zisserman, and Andrea Vedaldi · 2021
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Designing disaggregated evaluations of AI systems: Choices, considerations, and tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, and Hanna Wallach · 2021
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“It’s complicated”: Negotiating accessibility and (mis)representation in image descriptions of race, gender, and disability
Cynthia L. Bennett, Cole Gleanson, Morgan Klaus Scheuerman, Jeffrey P. Bigham, Anhong Guo, and Alexandra To · 2021
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Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
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The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision making systems
Kathleen Creel and Deborah Hellman · 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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The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Frederick Howard, James Dolezal, Sara Kochanny, Jefree Schulte, Heather Chen, Lara Heij, Dezheng Huo, Rita Nanda, Olufunmilayo Olopade, Jakob Kather, Nicole Cipriani, Robert Grossman, and Alexander Pearson · 2021
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Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
Ben Hutchinson, Andrew Smart, Alex Hanna, Emily Denton, Christina Greer, Oddur Kjartansson, Parker Barnes, and Margaret Mitchell · 2021
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Image segmentation using deep learning: A survey
Shervin Minaee, Yuri Y Boykov, Fatih Porikli, Antonio J Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos · 2021
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Understanding the representation and representativeness of age in ai data sets
Joon Sung Park, Michael S Bernstein, Robin N Brewer, Ece Kamar, and Meredith Ringel Morris · 2021
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M Bender, Emily Denton, and Alex Hanna · 2021
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Mitigating dataset harms requires stewardship: Lessons from 1000 papers
Kenneth L Peng, Arunesh Mathur, and Arvind Narayanan · 2021
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Fair attribute classification through latent space de-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, and Olga Russakovsky · 2021
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A step toward more inclusive people annotations for fairness
Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru · 2021
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Mitigating gender bias in captioning systems
Ruixiang Tang, Mengnan Du, Yuening Li, Zirui Liu, Na Zou, and Xia Hu · 2021
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Are gender-neutral queries really gender-neutral? mitigating gender bias in image search
Jialu Wang, Yang Liu, and Xin Eric Wang · 2021
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Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2021
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Understanding and evaluating racial biases in image captioning
Dora Zhao, Angelina Wang, and Olga Russakovsky · 2021
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AI recognition of patient race in medical imaging: a modelling study
Judy Wawira Gichoya, Imon Banerjee, Ananth Reddy Bhimireddy, John L Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle J Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis T Pyrros, Lauren Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, and Haoran Zhang · 2022
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Fairgrape: Fairness-aware gradient pruning method for face attribute classification
Xiaofeng Lin, Seungbae Kim, and Jungseock Joo · 2022
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