Abstract
Recent studies have revealed the importance of fairness in machine learning and computer vision systems, in accordance with the concerns about the unintended social discrimination produced by the systems. In this work, we aim to tackle the fairness-aware image classification problem, whose goal is to classify a target attribute (e.g., attractiveness) in a fair manner regarding protected attributes (e.g., gender, age, race). To this end, existing methods mainly rely on protected attribute labels for training, which are costly and sometimes unavailable for real-world scenarios. To alleviate the restriction and enlarge the scalability of fair models, we introduce a new framework where a fair classification model can be trained on datasets without protected attribute labels (i.e., target datasets) by exploiting knowledge from pre-built benchmarks (i.e., source datasets). Specifically, when training a target attribute encoder, we encourage its representations to be independent of the features from the pre-trained encoder on a source dataset. Moreover, we design a Group-wise Fair loss to minimize the gap in error rates between different protected attribute groups. To the best of our knowledge, this work is the first attempt to train the fairness-aware image classification model on a target dataset without protected attribute annotations. To verify the effectiveness of our approach, we conduct experiments on CelebA and UTK datasets with two settings: the conventional and the transfer settings. In the both settings, our model shows the fairest results when compared to the existing methods.
S. Hwang, S. Park, P. Lee—Equal contributions.
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References
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Barenstein, M.: Propublica’s compas data revisited. arXiv preprint arXiv:1906.04711 (2019)
Farnadi, G., Babaki, B., Getoor, L.: Fairness in relational domains. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. AIES 2018, pp. 108–114. Association for Computing Machinery, New York (2018)
Brandao, M.: Age and gender bias in pedestrian detection algorithms. In: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)
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: The IEEE International Conference on Computer Vision (ICCV) (2019)
Hwang, S., Byun, H.: Unsupervised image-to-image translation via fair representation of gender bias. In: The IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1953–1957. IEEE (2020)
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 2979–2989. Association for Computational Linguistics (2017)
Wang, M., Deng, W., Hu, J., Tao, X., Huang, Y.: Racial faces in the wild: reducing racial bias by information maximization adaptation network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 692–702 (2019)
Garcia, M.: Racist in the machine: the disturbing implications of algorithmic bias. World Policy J. 33, 111–117 (2016)
Park, S., Kim, D., Hwang, S., Byun, H.: Readme: representation learning by fairness-aware disentangling method. arXiv preprint arXiv:2007.03775 (2020)
Hutchinson, B., Mitchell, M.: 50 years of test (un)fairness: lessons for machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. FAT* 2019, pp. 49–58. Association for Computing Machinery, New York (2019)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS 2016, pp. 3323–3331. Curran Associates Inc., Red Hook (2016)
Quadrianto, N., Sharmanska, V., Thomas, O.: Discovering fair representations in the data domain. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
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. AIES 2018, pp. 335–340. Association for Computing Machinery, New York (2018)
Kim, B., Kim, H., Kim, K., Kim, S., Kim, J.: Learning not to learn: training deep neural networks with biased data. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M.: Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)
Creager, E., et al.: Flexibly fair representation learning by disentanglement. In Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Volume 97 of Proceedings of Machine Learning Research, Long Beach, California, USA, pp. 1436–1445. PMLR (2019)
Hendricks, L.A., Burns, K., Saenko, K., Darrell, T., Rohrbach, A.: Women also snowboard: overcoming bias in captioning models. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 793–811. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_47
Sattigeri, P., Hoffman, S.C., Chenthamarakshan, V., Varshney, K.R.: Fairness GAN: generating datasets with fairness properties using a generative adversarial network. IBM J. Res. Dev. 63(4/5), 3:1–3:9 (2019)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Edwards, H., Storkey, A.: Censoring representations with an adversary. International Conference on Learning Representations (2016)
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172–2180 (2016)
West, J., Ventura, D., Warnick, S.: Spring research presentation: a theoretical foundation for inductive transfer. Brigham Young University, College of Physical and Mathematical Sciences 1 (2007)
Wang, C., Yang, H., Meinel, C.: Image captioning with deep bidirectional LSTMs and multi-task learning. ACM Trans. Multimedia Comput. Commun. Appl. 14, 1–20 (2018)
Lee, K.H., He, X., Zhang, L., Yang, L.: CleanNet: transfer learning for scalable image classifier training with label noise. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Gamrian, S., Goldberg, Y.: Transfer learning for related reinforcement learning tasks via image-to-image translation. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Volume 97 of Proceedings of Machine Learning Research, Long Beach, California, USA, pp. 2063–2072. PMLR (2019)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Zafar, M.B., Valera, I., Gomez Rodriguez, M., Gummadi, K.P.: Fairness beyond disparate treatment & disparate impact: learning classification without disparate mistreatment. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1171–1180 (2017)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)
Zhang, Z., Song, Y., Qi, H.: Age progression/regression by conditional adversarial autoencoder. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Acknowledgement
This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017M3C4A7069370) and Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (Development of framework for analyzing, detecting, mitigating of bias in AI model and training data) under Grant 2019-0-01396 and (Artificial Intelligence Graduate School Program (YONSEI UNIVERSITY)) under Grant 2020-0-01361.
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Hwang, S., Park, S., Lee, P., Jeon, S., Kim, D., Byun, H. (2021). Exploiting Transferable Knowledge for Fairness-Aware Image Classification. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_2
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