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This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision.
Addressing artificial intelligence bias in retinal disease diagnostics
Burlina, P., Joshi, N., Paul, W., Pacheco, K. D., and Bressler, N. M. (2020a) · 2004
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Tara: Training and representation alteration for ai fairness and domain generalization
Paul, W., Hadzic, A., Joshi, N., Alajaji, F., and Burlina, P. (2020) · 2012
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Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013) · 2013
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From the information bottleneck to the privacy funnel
Makhdoumi, A., Salamatian, S., Fawaz, N., and Médard, M. (2014) · 2014
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Notes on information-theoretic privacy
Asoodeh, S., Alajaji, F., and Linder, T. (2015) · 2015
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Diabetic retinopathy detection
EyePACS (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R. B., and Sun, J. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., and Kalai, A. T. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S. K., Girshick, R. B., and Farhadi, A. (2015) · 2016
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Data decisions and theoretical implications when adversarially learning fair representations
Beutel, A., Chen, J., Zhao, Z., and Chi, E. H. (2017) · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D. (2017) · 2017
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J., Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B., Langlotz, C. P., Patel, B. N., Lungren, M. P., and Ng, A. Y. (2019) · 2019
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Differentially private fair learning
Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifi-Malvajerdi, S., and Ullman, J. (2019) · 2019
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Learning controllable fair representations
Song, J., Kalluri, P., Grover, A., Zhao, S., and Ermon, S. (2019) · 2019
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Fairness in machine learning: A survey
Caton, S. and Haas, C. (2020) · 2020
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Fairness of classifiers across skin tones in dermatology
Kinyanjui, N. M., Odonga, T., Cintas, C., Codella, N. C., Panda, R., Sattigeri, P., and Varshney, K. R. (2020) · 2020
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Mastering the game of go without human knowledge
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Achieving fairness through adversarial learning: an application to recidivism prediction
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Mitigating unwanted biases with adversarial learning
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Evaluation of retinal image quality assessment networks in different color-spaces
Fu, H., Wang, B., Shen, J., Cui, S., Xu, Y., Liu, J., and Shao, L. (2019) · 2019
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Low-shot deep learning of diabetic retinopathy with potential applications to address artificial intelligence bias in retinal diagnostics and rare ophthalmic diseases
Burlina, P., Paul, W., Mathew, P., Joshi, N., Pacheco, K. D., and Bressler, N. M. (2020b)
Cited in the paper.
Barbano, C. A., Tartaglione, E., and Grangetto, M. (2021) · 2021
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Pass: Protected attribute suppression system for mitigating bias in face recognition
Dhar, P., Gleason, J., Roy, A., Castillo, C. D., and Chellappa, R. (2021) · 2021
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Practical blind membership inference attack via differential comparisons
Hui, B., Yang, Y., Yuan, H., Burlina, P., Gong, N. Z., and Cao, Y. (2021) · 2021
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Defending medical image diagnostics against privacy attacks using generative methods
Paul, W., Cao, Y., Zhang, M., and Burlina, P. (2021) · 2021
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Balancing biases and preserving privacy on balanced faces in the wild
Robinson, J. P., Qin, C., Henon, Y., Timoner, S., and Fu, Y. (2021) · 2021
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