Built on

Huo, J.y., Chang, Y.l., Wang, J., Wei, X.x.: Robust automatic white balance algorithm using gray color points in images. IEEE Transactions on Consumer Electronics 52
2006
Earlier work this paper cites.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214–226 (2012)
2012
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Original
2014
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Original
2015
Earlier work this paper cites.
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Beutel, A., Chen, J., Zhao, Z., Chi, E.H.: Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017)
Original
2017
Earlier work this paper cites.
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457 (2017)
Original
2017
Earlier work this paper cites.
Bansal, N., Chen, X., Wang, Z.: Can we gain more from orthogonality regularizations in training deep cnns? arXiv preprint arXiv:1810.09102 (2018)
Original
2018
Earlier work this paper cites.
Bellamy, R.K.E., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K.N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K.R., Zhang, Y.: AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias (Oct 2018), https://arxiv.org/abs/1810.01943
Original
2018
Earlier work this paper cites.

Similar

Bellamy, R.K., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al.: Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)
Original
2018
Cited alongside, same era.
Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 168–172. IEEE (2018)
2018
Cited alongside, same era.
Hendricks, L.A., Burns, K., Saenko, K., Darrell, T., Rohrbach, A.: Women also snowboard: Overcoming bias in captioning models. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 771–787 (2018)
2018
Cited alongside, same era.

Then

Wu, Y., Yang, F., Xu, Y., Ling, H.: Privacy-protective-gan for privacy preserving face de-identification. Journal of Computer Science and Technology (2019)
2019
Later among the works it cites.
Abbasi-Sureshjani, S., Raumanns, R., Michels, B.E.J., Schouten, G., Cheplygina, V.: Risk of training diagnostic algorithms on data with demographic bias. In: Cardoso, J., Van Nguyen, H., Heller, N., Henriques Abreu, P., Isgum, I., Silva, W., Cruz, R., Pereira Amorim, J., Patel, V., Roysam, B., Zhou, K., Jiang, S., Le, N., Luu, K., Sznitman, R., Cheplygina, V., Mateus, D., Trucco, E., Abbasi, S. (eds.) Interpretable and Annotation-Efficient Learning for Medical Image Computing. pp. 183–192. Springer International Publishing, Cham (2020)
2020
Later among the works it cites.
Barron, J.T.: A generalization of otsu’s method and minimum error thresholding. ECCV (2020)
2020
Later among the works it cites.
Chzhen, E., Denis, C., Hebiri, M., Oneto, L., Pontil, M.: Fair regression via plug-in estimator and recalibration with statistical guarantees (2020)

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2021

Estimating and Improving Fairness with Adversarial Learning

Li, Xiaoxiao, Cui, Ziteng, Wu, Yifan et al.

Understand

Fairness and accountability are two essential pillars for trustworthy Artificial Intelligence (AI) in healthcare.

However, the existing AI model may be biased in its decision marking.
To tackle this issue, we propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system.
Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model.

Built on

Huo, J.y., Chang, Y.l., Wang, J., Wei, X.x.: Robust automatic white balance algorithm using gray color points in images. IEEE Transactions on Consumer Electronics 52
2006
Earlier work this paper cites.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214–226 (2012)
2012
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Original
2014
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Original
2015
Earlier work this paper cites.
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Beutel, A., Chen, J., Zhao, Z., Chi, E.H.: Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017)
Original
2017
Earlier work this paper cites.
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: Reducing gender bias amplification using corpus-level constraints. arXiv preprint arXiv:1707.09457 (2017)
Original
2017
Earlier work this paper cites.
Bansal, N., Chen, X., Wang, Z.: Can we gain more from orthogonality regularizations in training deep cnns? arXiv preprint arXiv:1810.09102 (2018)
Original
2018
Earlier work this paper cites.
Bellamy, R.K.E., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K.N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K.R., Zhang, Y.: AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias (Oct 2018), https://arxiv.org/abs/1810.01943
Original
2018
Earlier work this paper cites.

Similar

Bellamy, R.K., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al.: Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)
Original
2018
Cited alongside, same era.
Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 168–172. IEEE (2018)
2018
Cited alongside, same era.
Hendricks, L.A., Burns, K., Saenko, K., Darrell, T., Rohrbach, A.: Women also snowboard: Overcoming bias in captioning models. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 771–787 (2018)
2018
Cited alongside, same era.

Then

Wu, Y., Yang, F., Xu, Y., Ling, H.: Privacy-protective-gan for privacy preserving face de-identification. Journal of Computer Science and Technology (2019)
2019
Later among the works it cites.
Abbasi-Sureshjani, S., Raumanns, R., Michels, B.E.J., Schouten, G., Cheplygina, V.: Risk of training diagnostic algorithms on data with demographic bias. In: Cardoso, J., Van Nguyen, H., Heller, N., Henriques Abreu, P., Isgum, I., Silva, W., Cruz, R., Pereira Amorim, J., Patel, V., Roysam, B., Zhou, K., Jiang, S., Le, N., Luu, K., Sznitman, R., Cheplygina, V., Mateus, D., Trucco, E., Abbasi, S. (eds.) Interpretable and Annotation-Efficient Learning for Medical Image Computing. pp. 183–192. Springer International Publishing, Cham (2020)
2020
Later among the works it cites.
Barron, J.T.: A generalization of otsu’s method and minimum error thresholding. ECCV (2020)
2020
Later among the works it cites.
Chzhen, E., Denis, C., Hebiri, M., Oneto, L., Pontil, M.: Fair regression via plug-in estimator and recalibration with statistical guarantees (2020)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Qi, G.J., Zhang, L., Hu, H., Edraki, M., Wang, J., Hua, X.S.: Global versus localized generative adversarial nets. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1517–1525 (2018)

2018

Cited alongside, same era.

Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5

2018

Cited alongside, same era.

Xu, D., Yuan, S., Zhang, L., Wu, X.: Fairgan: Fairness-aware generative adversarial networks. In: 2018 IEEE International Conference on Big Data (Big Data). pp. 570–575. IEEE (2018)

2018

Cited alongside, same era.

Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. pp. 335–340 (2018)

2018

Cited alongside, same era.

Liu, F., Avci, B.: Incorporating priors with feature attribution on text classification. arXiv preprint arXiv:1906.08286 (2019)

Original

2019

Cited alongside, same era.

Suteu, M., Guo, Y.: Regularizing deep multi-task networks using orthogonal gradients. arXiv preprint arXiv:1912.06844 (2019)

Original

2019

Cited alongside, same era.

Wang, T., Zhao, J., Yatskar, M., Chang, K.W., Ordonez, V.: Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5310–5319 (2019)

2019

Cited alongside, same era.

2020