J.-H. Lee, Y.-M. Chan, T.-Y. Chen, and C.-S. Chen, “Joint estimation of age and gender from unconstrained face images using lightweight multi-task cnn for mobile applications,” in IEEE Conference on Multimedia Information Processing and Retrieval , 2018
2018
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T. de Vries, I. Misra, C. Wang, and L. van der Maaten, “Does object recognition work for everyone?” in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , June 2019
2019
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K. Kärkkäinen and J. Joo, “Fairface: Face attribute dataset for balanced race, gender, and age,” 2019
2019
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I. D. Raji and J. Buolamwini, “Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products,” in AAAI/ACM Conference on AI, Ethics, and Society , 2019
2019
Cited alongside, same era.
S. Barocas, M. Hardt, and A. Narayanan, Fairness and Machine Learning . fairmlbook.org, 2019, http://www.fairmlbook.org
2019
Cited alongside, same era.
C. M. Cook, J. J. Howard, Y. B. Sirotin, J. L. Tipton, and A. R. Vemury, “Demographic effects in facial recognition and their dependence on image acquisition: An evaluation of eleven commercial systems,” IEEE Transactions on Biometrics, Behavior, and Identity Science , 2019
2019
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J. J. Howard, Y. B. Sirotin, and A. R. Vemury, “The effect of broad and specific demographic homogeneity on the imposter distributions and false match rates in face recognition algorithm performance,” in International Conference on Biometrics Theory, Applications and Systems , 2019
2019
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T. T. Nguyen, C. M. Nguyen, D. T. Nguyen, D. T. Nguyen, and S. Nahavandi, “Deep learning for deepfakes creation and detection: A survey,” 2019
2019
Cited alongside, same era.
B. Dolhansky, R. Howes, B. Pflaum, N. Baram, and C. Canton Ferrer, “The DeepFake Detection Challenge (DFDC) Preview Dataset,” 2019
2019
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K. Yang, K. Qinami, L. Fei-Fei, J. Deng, and O. Russakovsky, “Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy,” in Conference on Fairness, Accountability, and Transparency , Jan 2020
2020
Cited alongside, same era.
A. J. Larrazabal, N. Nieto, V. Peterson, D. H. Milone, and E. Ferrante, “Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis,” Proceedings of the National Academy of Sciences , 2020
2020
Cited alongside, same era.
I. D. Raji, T. Gebru, M. Mitchell, J. Buolamwini, J. Lee, and E. Denton, “Saving face: Investigating the ethical concerns of facial recognition auditing,” in AAAI/ACM Conference on AI, Ethics, and Society , 2020
2020
Cited alongside, same era.