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As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern.
The foundations of cost-sensitive learning
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Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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Smoteboost: Improving prediction of the minority class in boosting
Chawla, N. V., Lazarevic, A., Hall, L. O., and Bowyer, K. W · 2003
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Special issue on learning from imbalanced data sets
Chawla, N. V., Japkowicz, N., and Kotcz, A · 2004
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E · 2007
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Cost-sensitive boosting for classification of imbalanced data
Sun, Y., Kamel, M. S., Wong, A. K., and Wang, Y · 2007
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Experimental perspectives on learning from imbalanced data
Van Hulse, J., Khoshgoftaar, T. M., and Napolitano, A · 2007
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Cost-sensitive learning and the class imbalance problem
Ling, C. X. and Sheng, V. S · 2008
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Exploratory undersampling for class-imbalance learning
Liu, X.-Y., Wu, J., and Zhou, Z.-H · 2008
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Reject options and confidence measures for knn classifiers
Dalitz, C · 2009
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Frvt 2006 and ice 2006 large-scale experimental results
Phillips, P. J., Scruggs, W. T., O’Toole, A. J., Flynn, P. J., Bowyer, K. W., Schott, C. L., and Sharpe, M · 2009
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An other-race effect for face recognition algorithms
Phillips, P. J., Jiang, F., Narvekar, A., Ayyad, J., and O’Toole, A. J · 2011
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Face recognition performance: Role of demographic information
Klare, B. F., Burge, M. J., Klontz, J. C., Bruegge, R. W. V., and Jain, A. K · 2012
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The impact of imbalanced training data for convolutional neural networks
Hensman, P. and Masko, D · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Learning from imbalanced data: open challenges and future directions
Krawczyk, B · 2016
Cited alongside, same era.
Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning
Lee, H., Park, M., and Kim, J · 2016
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Training deep neural networks on imbalanced data sets
Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., and Kennedy, P. J · 2016
Cited alongside, same era.
Algorithmic bias in autonomous systems
Danks, D. and London, A. J · 2017
Cited alongside, same era.
Face recognition vendor test part 3: demographic effects
Grother, P. J., Ngan, M. L., Hanaoka, K. K., et al · 2019
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Survey on deep learning with class imbalance
Johnson, J. M. and Khoshgoftaar, T. M · 2019
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A framework for understanding unintended consequences of machine learning
Suresh, H. and Guttag, J. V · 2019
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Analysis of gender inequality in face recognition accuracy
Albiero, V., KS, K., Vangara, K., Zhang, K., King, M. C., and Bowyer, K. W · 2020
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Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?
Cavazos, J. G., Phillips, P. J., Castillo, C. D., and O’Toole, A. J · 2020
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A snapshot of the frontiers of fairness in machine learning
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Cost-sensitive learning of deep feature representations from imbalanced data
Khan, S. H., Hayat, M., Bennamoun, M., Sohel, F. A., and Togneri, R · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
Cited alongside, same era.
Class-balanced training for deep face recognition
Zhang, Y. and Deng, W · 2017
Cited alongside, same era.
Turning a blind eye: Explicit removal of biases and variation from deep neural network embeddings
Alvi, M., Zisserman, A., and Nellåker, C · 2018
Cited alongside, same era.
A systematic study of the class imbalance problem in convolutional neural networks
Buda, M., Maki, A., and Mazurowski, M. A · 2018
Cited alongside, same era.
Empirically analyzing the effect of dataset biases on deep face recognition systems
Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Morel-Forster, A., and Vetter, T · 2018
Cited alongside, same era.
Chouldechova, A. and Roth, A · 2020
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Robustness disparities in commercial face detection
Dooley, S., Goldstein, T., and Dickerson, J. P · 2020
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Face recognition: too bias, or not too bias?
Robinson, J. P., Livitz, G., Henon, Y., Qin, C., Fu, Y., and Timoner, S · 2020
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Mitigating bias in face recognition using skewness-aware reinforcement learning
Wang, M. and Deng, W · 2020
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Comparing human and machine bias in face recognition
Dooley, S., Downing, R., Wei, G., Shankar, N., Thymes, B., Thorkelsdottir, G., Kurtz-Miott, T., Mattson, R., Obiwumi, O., Cherepanova, V., et al · 2021
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Rethinking common assumptions to mitigate racial bias in face recognition datasets
Gwilliam, M., Hegde, S., Tinubu, L., and Hanson, A · 2021
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A survey on bias and fairness in machine learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A · 2021
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