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This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators.
“Soleil et peau” [Sun and skin]
Thomas B. Fitzpatrick · 1975
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Skin tone stratification among black americans, 2001–2003
Ellis P Monk Jr · 2014
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Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, and Jianfeng Gao · 2016
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Wendy D. Roth · 2016
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Ongoing face recognition vendor test (frvt) part 2: Identification, 2018
Patrick Grother, Mei Ngan, and Kayee Hanaoka · 2018
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Demographic effects in facial recognition and their dependence on image acquisition: An evaluation of eleven commercial systems
Cynthia M. Cook, John J. Howard, Yevgeniy B. Sirotin, Jerry L. Tipton, and Arun R. Vemury · 2019
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Does object recognition work for everyone?
Terrance de Vries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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The effect of broad and specific demographic homogeneity on the imposter distributions and false match rates in face recognition algorithm performance
John J. Howard, Yevgeniy B. Sirotin, and Arun R. Vemury · 2019
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Racial faces in the wild: Reducing racial bias by information maximization adaptation network
Mei Wang, Weihong Deng, Jiani Hu, Xunqiang Tao, and Yaohai Huang · 2019
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Does face recognition accuracy get better with age? deep face matchers say no
Vítor Albiero, Kevin Bowyer, Kushal Vangara, and Michael King · 2020
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Analysis of gender inequality in face recognition accuracy
Vítor Albiero, S. Krishnapriya K., K. Vangara, K. Zhang, Michael C. King, and K. Bowyer · 2020
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The deepfake detection challenge dataset, 2020
Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer · 2020
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Demographic bias in biometrics: A survey on an emerging challenge
Pawel Drozdowski, Christian Rathgeb, Antitza Dantcheva, Naser Damer, and Christoph Busch · 2020
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A step toward more inclusive people annotations for fairness
Candice Schumann, Caroline Rebecca Pantofaru, Susanna Ricco, Utsav Prabhu, and Vittorio Ferrari · 2021
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Fairness indicators for systematic assessments of visual feature extractors
Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, and Nicolas Usunier · 2022
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Casual conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu, Parisa Assar, Bilal Porgali, Vítor Albiero, Stefan Hermanek, Jacqueline Pan, Emily McReynolds, Miranda Bogen, Pascale Fung, and Cristian Canton Ferrer · 2022
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Towards measuring fairness in ai: The casual conversations dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, and Cristian Canton Ferrer · 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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Issues related to face recognition accuracy varying based on race and skin tone
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Gendered differences in face recognition accuracy explained by hairstyles, makeup, and facial morphology
Vítor Albiero, Kai Zhang, Michael C King, and Kevin W Bowyer · 2021
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Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
Kimmo Karkkainen and Jungseock Joo · 2021
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Does face recognition error echo gender classification error?
Ying Qiu, Vítor Albiero, Michael C King, and Kevin W Bowyer · 2021
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Face recognition accuracy across demographics: Shining a light into the problem
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Ethical considerations for collecting human-centric image datasets
Jerone T. A. Andrews, Dora Zhao, William Thong, Apostolos Modas, Orestis Papakyriakopoulos, Shruti Nagpal, and Alice Xiang · 2023
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The Artificial Intelligence Act
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