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Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceived gender, skin type, lighting condition, etc.
The validity and practicality of sun-reactive skin types i through vi
Thomas B Fitzpatrick · 1988
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Face sketch recognition
Xiaoou Tang and Xiaogang Wang · 2004
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Face recognition algorithms surpass humans matching faces over changes in illumination
Alice J O’Toole, P Jonathon Phillips, Fang Jiang, Janet Ayyad, Nils Penard, and Herve Abdi · 2007
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Labeled faces in the wild: A database forstudying face recognition in unconstrained environments
Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 2008
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Face recognition performance: Role of demographic information
Brendan F Klare, Mark J Burge, Joshua C Klontz, Richard W Vorder Bruegge, and Anil K Jain · 2012
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Demographic effects on estimates of automatic face recognition performance
Alice J O’Toole, P Jonathon Phillips, Xiaobo An, and Joseph Dunlop · 2012
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Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Comparison of human and computer performance across face recognition experiments
P Jonathon Phillips and Alice J O’toole · 2014
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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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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Surpassing human-level face verification performance on lfw with gaussianface
Chaochao Lu and Xiaoou Tang · 2015
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Error rates in users of automatic face recognition software
David White, James D Dunn, Alexandra C Schmid, and Richard I Kemp · 2015
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Censoring representations with an adversary
Harrison Edwards and Amos J. Storkey · 2016
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The perpetual line-up: Unregulated police face recognition in America
Clare Garvie · 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
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Face recognition by metropolitan police super-recognisers
David J Robertson, Eilidh Noyes, Andrew J Dowsett, Rob Jenkins, and A Mike Burton · 2016
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
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Person recognition: Qualitative differences in how forensic face examiners and untrained people rely on the face versus the body for identification
Ying Hu, Kelsey Jackson, Amy Yates, David White, P Jonathon Phillips, and Alice J O’Toole · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices
Sheng Chen, Yang Liu, Xiang Gao, and Zhen Han · 2018
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Discovering fair representations in the data domain
Novi Quadrianto, Viktoriia Sharmanska, and Oliver Thomas · 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
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Fairness constraints: A flexible approach for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi · 2019
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Convergent algorithms for (relaxed) minimax fairness
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth · 2020
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The secretive company that might end privacy as we know it
Woodrow Hartzog · 2020
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Alexandra Chouldechova and Aaron Roth · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, and Massimiliano Pontil · 2018
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Non-discriminatory machine learning through convex fairness criteria
Naman Goel, Mohammad Yaghini, and Boi Faltings · 2018
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Facial recognition is accurate, if you’re a white guy
Steve Lohr · 2018
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2018
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Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
P Jonathon Phillips, Amy N Yates, Ying Hu, Carina A Hahn, Eilidh Noyes, Kelsey Jackson, Jacqueline G Cavazos, Géraldine Jeckeln, Rajeev Ranjan, Swami Sankaranarayanan, et al · 2018
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Inclusivefacenet: Improving face attribute detection with race and gender diversity
Hee Jung Ryu, Hartwig Adam, and Margaret Mitchell · 2018
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Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H. Chi · 2020
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Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
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Fnnc: Achieving fairness through neural networks
Manisha Padala and Sujit Gujar · 2020
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Intra-processing methods for debiasing neural networks
Yash Savani, Colin White, and Naveen Sundar Govindarajulu · 2020
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Mitigating bias in face recognition using skewness-aware reinforcement learning
Mei Wang and Weihong Deng · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation, 2020
Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 2020
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Amazon pauses police use of its facial recognition software
Karen Weise and Natasha Singer · 2020
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Representation learning with statistical independence to mitigate bias
Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V Sullivan, Li Fei-Fei, Juan Carlos Niebles, and Kilian M Pohl · 2021
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Robustness disparities in commercial face detection
Samuel Dooley, Tom Goldstein, and John P Dickerson · 2021
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How google uses pattern recognition to make sense of images
Google · 2021
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Between security and convenience: Facial recognition technology in the eyes of citizens in china, germany, the united kingdom, and the united states
Genia Kostka, Léa Steinacker, and Miriam Meckel · 2021
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Armed low-cost drones, made by turkey, reshape battlefields and geopolitics
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In response to climate change, citizens in advanced economies are willing to alter how they live and work
Pew Research Center · 2021
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