Abstract
With the recent growth in computer vision applications, the question of how fair and unbiased they are has yet to be explored. There is abundant evidence that the bias present in training data is reflected in the models, or even amplified. Many previous methods for image dataset de-biasing, including models based on augmenting datasets, are computationally expensive to implement. In this study, we present a fast and effective model to de-bias an image dataset through reconstruction and minimizing the statistical dependence between intended variables. Our architecture includes a U-net to reconstruct images, combined with a pre-trained classifier which penalizes the statistical dependence between target attribute and the protected attribute. We evaluate our proposed model on CelebA dataset, compare the results with two state-of-the-art de-biasing method, and show that the model achieves a promising fairness-accuracy combination.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alvi, M., Zisserman, A., Nellåker, C.: Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pages 77–91. PMLR (2018)
Choi, K., Grover, A., Singh, T., Shu, R., Ermon, S.: Fair generative modeling via weak supervision. In: International Conference on Machine Learning, pp. 1887–1898. PMLR (2020)
Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)
Denton, E., Hutchinson, B., Mitchell, M., Gebru, T., Zaldivar, A.: Image counterfactual sensitivity analysis for detecting unintended bias. arXiv preprint arXiv:1906.06439 (2019)
Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63–77. Springer, Heidelberg (2005). https://doi.org/10.1007/11564089_7
Gretton, A., Herbrich, R., Smola, A., Schölkopf, B., et al.: Kernel methods for measuring independence, Olivier Bousquet (2005)
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural. Inf. Process. Syst. 29, 3315–3323 (2016)
Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (2012)
Hwang, S., Park, S., Kim, D., Do, M., Byun, H.: Fairfacegan: fairness-aware facial image-to-image translation. arXiv preprint arXiv:2012.00282 (2020)
Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1–33 (2012)
Lambrecht, A., Tucker, C.: Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads. Manage. Sci. 65(7), 2966–2981 (2019)
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)
Lokhande, V.S., Akash, A.K., Ravi, S.N., Singh, V.: FairALM: augmented Lagrangian method for training fair models with little regret. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 365–381. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_22
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54(6), 1–35 (2021)
Pessach, D., Shmueli, E.: Algorithmic fairness. arXiv preprint arXiv:2001.09784 (2020)
Quadrianto, N., Sharmanska, V., Thomas, O.: Discovering fair representations in the data domain. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8227–8236 (2019)
Rajabi, A., Garibay, O.O.: Tabfairgan: fair tabular data generation with generative adversarial networks. arXiv preprint arXiv:2109.00666 (2021)
Ramaswamy, V.V., Kim, S.S.Y., Russakovsky, O.: Fair attribute classification through latent space de-biasing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9301–9310 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
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)
Sharmanska, V., Hendricks, L.A., Darrell, T., Quadrianto, N.: Contrastive examples for addressing the tyranny of the majority. arXiv preprint arXiv:2004.06524 (2020)
Tommasi, T., Patricia, N., Caputo, B., Tuytelaars, T.: A deeper look at dataset bias. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 37–55. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58347-1_2
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR 2011, pp. 1521–1528. IEEE (2011)
Wang, A., Narayanan, A., Russakovsky, O.: Revise: a tool for measuring and mitigating bias in visual datasets. In: European Conference on Computer Vision, pp. 733–751. Springer (2020)
Wang, M., Deng, W.: Mitigate bias in face recognition using skewness-aware reinforcement learning. arXiv preprint arXiv:1911.10692 (2019)
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/CVF International Conference on Computer Vision, pp. 692–702 (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: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5310–5319 (2019)
Wang, T., Ding, Z., Shao, W., Tang, H., Huang, K.: Towards fair cross-domain adaptation via generative learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 454–463 (2021)
Wang, Z., et al.: Towards fairness in visual recognition: effective strategies for bias mitigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8919–8928 (2020)
Xu, X., et al.: Consistent instance false positive improves fairness in face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 578–586 (2021)
Yang, K., Qinami, K., Fei-Fei, L., Deng, J., Russakovsky, O.: Towards fairer datasets: filtering and balancing the distribution of the people subtree in the imagenet hierarchy. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 547–558 (2020)
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)
Zhang, Q., Wang, W., Zhu, S.-C.: Examining CNN representations with respect to dataset bias. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, (2018)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rajabi, A., Yazdani-Jahromi, M., Garibay, O.O., Sukthankar, G. (2023). Through a Fair Looking-Glass: Mitigating Bias in Image Datasets. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14050. Springer, Cham. https://doi.org/10.1007/978-3-031-35891-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-35891-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35890-6
Online ISBN: 978-3-031-35891-3
eBook Packages: Computer ScienceComputer Science (R0)