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Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade.
The validity and practicality of sun-reactive skin types i through vi
T. B. Fitzpatrick · 1988
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Frvt 2006 and ice 2006 large-scale results
P. J. Phillips, W. T. Scruggs, A. J. O’Toole, P. J. Flynn, K. W. Bowyer, C. L. Schott, and M. Sharpe · 2007
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An introduction to the good, the bad, & the ugly face recognition challenge problem
P. J. Phillips, J. R. Beveridge, B. A. Draper, G. Givens, A. J. O’Toole, D. S. Bolme, J. Dunlop, Y. M. Lui, H. Sahibzada, and S. Weimer · 2011
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Face recognition performance: Role of demographic information
B. F. Klare, M. J. Burge, J. C. Klontz, R. W. V. Bruegge, and A. K. Jain · 2012
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Demographic effects on estimates of automatic face recognition performance
A. J. O’Toole, P. J. Phillips, X. An, and J. Dunlop · 2012
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Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Age and gender estimation of unfiltered faces
E. Eidinger, R. Enbar, and T. Hassner · 2014
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Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Censoring representations with an adversary
H. Edwards and A. J. Storkey · 2016
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Face verification subject to varying (age, ethnicity, and gender) demographics using deep learning
H. El Khiyari and H. Wechsler · 2016
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The perpetual line-up: Unregulated police face recognition in America
C. Garvie · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, E. Price, and N. Srebro · 2016
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Can we still avoid automatic face detection?
M. J. Wilber, V. Shmatikov, and S. Belongie · 2016
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Longitudinal study of automatic face recognition
L. Best-Rowden and A. K. Jain · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
A. Beutel, J. Chen, Z. Zhao, and E. H. Chi · 2017
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Google’s cloud vision API is not robust to noise
H. Hosseini, B. Xiao, and R. Poovendran · 2017
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi · 2017
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Age progression/regression by conditional adversarial autoencoder
Z. Zhang, Y. Song, and H. Qi · 2017
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A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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The frontiers of fairness in machine learning
A. Chouldechova and A. Roth · 2018
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Empirical risk minimization under fairness constraints
M. Donini, L. Oneto, S. Ben-David, J. Shawe-Taylor, and M. Pontil · 2018
Cited alongside, same era.
Non-discriminatory machine learning through convex fairness criteria
N. Goel, M. Yaghini, and B. Faltings · 2018
Cited alongside, same era.
Gender recognition or gender reductionism? the social implications of embedded gender recognition systems
F. Hamidi, M. K. Scheuerman, and S. M. Branham · 2018
Cited alongside, same era.
The misgendering machines: Trans/hci implications of automatic gender recognition
O. Keyes · 2018
Cited alongside, same era.
Facial recognition is accurate, if you’re a white guy
S. Lohr · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. S. Zemel · 2018
Coded bias, 2020
S. Kantayya · 2020
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Fairness without demographics through adversarially reweighted learning
P. Lahoti, A. Beutel, J. Chen, K. Lee, F. Prost, N. Thain, X. Wang, and E. H. Chi · 2020
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Minimax pareto fairness: A multi objective perspective
N. Martinez, M. Bertran, and G. Sapiro · 2020
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Fnnc: Achieving fairness through neural networks
M. Padala and S. Gujar · 2020
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Intra-processing methods for debiasing neural networks
Y. Savani, C. White, and N. S. Govindarajulu · 2020
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Fawkes: Protecting privacy against unauthorized deep learning models
S. Shan, E. Wenger, J. Zhang, H. Li, H. Zheng, and B. Y. Zhao · 2020
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Cited alongside, same era.
Inclusivefacenet: Improving face attribute detection with race and gender diversity
H. J. Ryu, H. Adam, and M. Mitchell · 2018
Cited alongside, same era.
Microsoft urges congress to regulate use of facial recognition
N. Singer · 2018
Cited alongside, same era.
Bridging social critique and design: Building a health informatics tool for transgender voice
A. A. Ahmed · 2019
Cited alongside, same era.
Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2019
Cited alongside, same era.
Racial categories in machine learning
S. Benthall and B. D. Haynes · 2019
Cited alongside, same era.
Demographic effects in facial recognition and their dependence on image acquisition: An evaluation of eleven commercial systems
C. M. Cook, J. J. Howard, Y. B. Sirotin, J. L. Tipton, and A. R. Vemury · 2019
Cited alongside, same era.
Later among the works it cites.
On the robustness of face recognition algorithms against attacks and bias
R. Singh, A. Agarwal, M. Singh, S. Nagpal, and M. Vatsa · 2020
Later among the works it cites.
Mitigating bias in face recognition using skewness-aware reinforcement learning
M. Wang and W. Deng · 2020
Later among the works it cites.
Towards fairness in visual recognition: Effective strategies for bias mitigation, 2020
Z. Wang, K. Qinami, I. C. Karakozis, K. Genova, P. Nair, K. Hata, and O. Russakovsky · 2020
Later among the works it cites.
Amazon pauses police use of its facial recognition software
K. Weise and N. Singer · 2020
Later among the works it cites.
https://docs.aws.amazon.com/rekognition/latest/dg/guidance-face-attributes.html
Guidelines on face attributes · 2021
Closest in time.
Representation learning with statistical independence to mitigate bias
E. Adeli, Q. Zhao, A. Pfefferbaum, E. V. Sullivan, L. Fei-Fei, J. C. Niebles, and K. M. Pohl · 2021
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Lowkey: leveraging adversarial attacks to protect social media users from facial recognition
V. Cherepanova, M. Goldblum, H. Foley, S. Duan, J. P. Dickerson, G. Taylor, and T. Goldstein · 2021
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Exploring a makeup support system for transgender passing based on automatic gender recognition
T. Chong, N. Maudet, K. Harima, and T. Igarashi · 2021
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How google uses pattern recognition to make sense of images
Google · 2021
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King County Council bans use of facial recognition technology by Sheriff’s Office, other agencies
D. Gutman · 2021
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Towards measuring fairness in ai: the casual conversations dataset
C. Hazirbas, J. Bitton, B. Dolhansky, J. Pan, A. Gordo, and C. C. Ferrer · 2021
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1.4 billion missing pieces? auditing the accuracy of facial processing tools on indian faces
G. Jain and S. Parsheera · 2021
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Armed low-cost drones, made by turkey, reshape battlefields and geopolitics
J. Marson and B. Forrest · 2021
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Fairness through robustness: Investigating robustness disparity in deep learning
V. Nanda, S. Dooley, S. Singla, S. Feizi, and J. P. Dickerson · 2021
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A step toward more inclusive people annotations for fairness
C. Schumann, C. R. Pantofaru, S. Ricco, U. Prabhu, and V. Ferrari · 2021
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