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Efficient Bias Mitigation Without Privileged Information

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15130))

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Abstract

Deep neural networks trained via empirical risk minimization often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e.g., “grassy background” and “cows”). Existing bias mitigation methods that aim to address this issue often either rely on group labels for training or validation, or require an extensive hyperparameter search. Such data and computational requirements hinder the practical deployment of these methods, especially when datasets are too large to be group-annotated, computational resources are limited, and models are trained through already complex pipelines. In this paper, we propose Targeted Augmentations for Bias Mitigation (TAB), a simple hyperparameter-free framework that leverages the entire training history of a helper model to identify spurious samples, and generate a group-balanced training set from which a robust model can be trained. We show that TAB improves worst-group performance without any group information or model selection, outperforming existing methods while maintaining overall accuracy.

M. E. Zarlenga—Work done while the author was an intern at Sony AI.

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Notes

  1. 1.

    Our code is available at https://github.com/SonyResearch/tab_bias_mitigation.

References

  1. Ahmed, F., Bengio, Y., Van Seijen, H., Courville, A.: Systematic generalisation with group invariant predictions. In: International Conference on Learning Representations (2020)

    Google Scholar 

  2. Andrews, J.T., et al.: Ethical considerations for collecting human-centric image datasets. ICML (2023)

    Google Scholar 

  3. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28(2), 49–60 (1999)

    Article  Google Scholar 

  4. Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning, pp. 233–242. PMLR (2017)

    Google Scholar 

  5. Asgari, S., et al.: Masktune: mitigating spurious correlations by forcing to explore. In: Advances in Neural Information Processing Systems, vol. 35, pp. 23284–23296 (2022)

    Google Scholar 

  6. Badue, C., et al.: Self-driving cars: a survey. Expert Syst. Appl. 165, 113816 (2021)

    Article  Google Scholar 

  7. Bahng, H., Chun, S., Yun, S., Choo, J., Oh, S.J.: Learning de-biased representations with biased representations. In: International Conference on Machine Learning, pp. 528–539. PMLR (2020)

    Google Scholar 

  8. Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., Rennen, G.: Robust solutions of optimization problems affected by uncertain probabilities. Manage. Sci. 59(2), 341–357 (2013)

    Article  Google Scholar 

  9. Birhane, A., Prabhu, V.U., Kahembwe, E.: Multimodal datasets: misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963 (2021)

  10. Bommasani, R., et al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021)

  11. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on Fairness, Accountability and Transparency, pp. 77–91. PMLR (2018)

    Google Scholar 

  12. Crawford, K., Paglen, T.: Excavating AI: the politics of images in machine learning training sets. AI Soc. 36(4), 1105–1116 (2021)

    Article  Google Scholar 

  13. Creager, E., Jacobsen, J.H., Zemel, R.: Environment inference for invariant learning. In: International Conference on Machine Learning, pp. 2189–2200. PMLR (2021)

    Google Scholar 

  14. De Vries, T., Misra, I., Wang, C., Van der Maaten, L.: Does object recognition work for everyone? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 52–59 (2019)

    Google Scholar 

  15. DeGrave, A.J., Janizek, J.D., Lee, S.I.: AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3(7), 610–619 (2021)

    Article  Google Scholar 

  16. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  17. Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141–142 (2012)

    Article  Google Scholar 

  18. d’Eon, G., d’Eon, J., Wright, J.R., Leyton-Brown, K.: The spotlight: a general method for discovering systematic errors in deep learning models. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 1962–1981 (2022)

    Google Scholar 

  19. Duchi, J.C., Glynn, P.W., Namkoong, H.: Statistics of robust optimization: a generalized empirical likelihood approach. Math. Oper. Res. 46(3), 946–969 (2021)

    Article  MathSciNet  Google Scholar 

  20. Eyuboglu, S., et al.: Domino: discovering systematic errors with cross-modal embeddings. ICLR (2022)

    Google Scholar 

  21. Fabbrizzi, S., Papadopoulos, S., Ntoutsi, E., Kompatsiaris, I.: A survey on bias in visual datasets. Comput. Vis. Image Underst. 223, 103552 (2022)

    Article  Google Scholar 

  22. Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2(11), 665–673 (2020)

    Article  Google Scholar 

  23. Goodman, B.W.: A step towards accountable algorithms?: algorithmic discrimination and the european union general data protection. In: 29th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona. NIPS Foundation (2016)

    Google Scholar 

  24. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. 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)

    Google Scholar 

  27. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851 (2020)

    Google Scholar 

  28. Hu, W., Niu, G., Sato, I., Sugiyama, M.: Does distributionally robust supervised learning give robust classifiers? In: International Conference on Machine Learning, pp. 2029–2037. PMLR (2018)

    Google Scholar 

  29. Kay, M., Matuszek, C., Munson, S.A.: Unequal representation and gender stereotypes in image search results for occupations. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3819–3828 (2015)

    Google Scholar 

  30. Kim, M.P., Ghorbani, A., Zou, J.: Multiaccuracy: black-box post-processing for fairness in classification. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 247–254 (2019)

    Google Scholar 

  31. Kim, Y., Mo, S., Kim, M., Lee, K., Lee, J., Shin, J.: Bias-to-text: debiasing unknown visual biases through language interpretation. arXiv preprint arXiv:2301.11104, vol. 2 (2023)

  32. Kirichenko, P., Izmailov, P., Wilson, A.G.: Last layer re-training is sufficient for robustness to spurious correlations. ICLR (2023)

    Google Scholar 

  33. Kulkarni, A., Dery, L.M., Setlur, A., Raghunathan, A., Talwalkar, A., Neubig, G.: Multitask learning can improve worst-group outcomes. Trans. Mach. Learn. Res. (2023)

    Google Scholar 

  34. LaBonte, T., Muthukumar, V., Kumar, A.: Towards last-layer retraining for group robustness with fewer annotations. In: NeurIPS (2023)

    Google Scholar 

  35. Levy, D., Carmon, Y., Duchi, J.C., Sidford, A.: Large-scale methods for distributionally robust optimization. In: Advances in Neural Information Processing Systems, vol. 33, pp. 8847–8860 (2020)

    Google Scholar 

  36. Li, Z., et al.: A WHAC-a-mole dilemma: shortcuts come in multiples where mitigating one amplifies others. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20071–20082 (2023)

    Google Scholar 

  37. Li, Z., Hoogs, A., Xu, C.: Discover and mitigate unknown biases with debiasing alternate networks. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13673, pp. 270–288. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19778-9_16

    Chapter  Google Scholar 

  38. Liu, E.Z., et al: Just train twice: improving group robustness without training group information. In: International Conference on Machine Learning, pp. 6781–6792. PMLR (2021)

    Google Scholar 

  39. Liu, Z., Luo, P., Wang, X., Tang, X.: Large-scale celebfaces attributes (CelebA) dataset (2018). Retrieved 15 Aug 2018

    Google Scholar 

  40. Ma, X., Wang, Z., Liu, W.: On the tradeoff between robustness and fairness. In: Advances in Neural Information Processing Systems, vol. 35, pp. 26230–26241 (2022)

    Google Scholar 

  41. Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: de-biasing classifier from biased classifier. In: Advances in Neural Information Processing Systems, vol. 33, pp. 20673–20684 (2020)

    Google Scholar 

  42. Nam, J., Kim, J., Lee, J., Shin, J.: Spread spurious attribute: improving worst-group accuracy with spurious attribute estimation. ICLR (2022)

    Google Scholar 

  43. Oakden-Rayner, L., Dunnmon, J., Carneiro, G., Ré, C.: Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: Proceedings of the ACM Conference on Health, Inference, and Learning, pp. 151–159 (2020)

    Google Scholar 

  44. O’neil, C.: Weapons of math destruction: how big data increases inequality and threatens democracy. Crown (2017)

    Google Scholar 

  45. Oren, Y., Sagawa, S., Hashimoto, T.B., Liang, P.: Distributionally robust language modeling. arXiv preprint arXiv:1909.02060 (2019)

  46. Paranjape, B., Dasigi, P., Srikumar, V., Zettlemoyer, L., Hajishirzi, H.: AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization. arXiv preprint arXiv:2212.00921 (2022)

  47. Pezeshki, M., Kaba, O., Bengio, Y., Courville, A.C., Precup, D., Lajoie, G.: Gradient starvation: a learning proclivity in neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 1256–1272 (2021)

    Google Scholar 

  48. Prabhu, V.U., Birhane, A.: Large image datasets: a pyrrhic win for computer vision? arXiv preprint arXiv:2006.16923 (2020)

  49. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  50. Ramakrishnan, S., Agrawal, A., Lee, S.: Overcoming language priors in visual question answering with adversarial regularization. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  51. Ramesh, A., et al.: Zero-shot text-to-image generation. In: International Conference on Machine Learning, pp. 8821–8831. PMLR (2021)

    Google Scholar 

  52. Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: International Conference on Machine Learning, pp. 4334–4343. PMLR (2018)

    Google Scholar 

  53. Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., Zhong, C.: Interpretable machine learning: Fundamental principles and 10 grand challenges. arXiv preprint arXiv:2103.11251 (2021)

  54. Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731 (2019)

  55. Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1177–1178 (2010)

    Google Scholar 

  56. Shah, H., Tamuly, K., Raghunathan, A., Jain, P., Netrapalli, P.: The pitfalls of simplicity bias in neural networks. In: Advances in Neural Information Processing Systems, vol. 33, pp. 9573–9585 (2020)

    Google Scholar 

  57. Shi: Multiclass spectral clustering. In: Proceedings Ninth IEEE International Conference on Computer Vision, pp. 313–319. IEEE (2003)

    Google Scholar 

  58. Shrestha, R., Kafle, K., Kanan, C.: Occamnets: mitigating dataset bias by favoring simpler hypotheses. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13680, pp. 702–721. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20044-1_40

    Chapter  Google Scholar 

  59. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Google Scholar 

  60. Singh, K.K., Mahajan, D., Grauman, K., Lee, Y.J., Feiszli, M., Ghadiyaram, D.: Don’t judge an object by its context: learning to overcome contextual bias. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11070–11078 (2020)

    Google Scholar 

  61. Sohoni, N., Dunnmon, J., Angus, G., Gu, A., Ré, C.: No subclass left behind: fine-grained robustness in coarse-grained classification problems. In: Advances in Neural Information Processing Systems, vol. 33, pp. 19339–19352 (2020)

    Google Scholar 

  62. Taghanaki, S.A., Choi, K., Khasahmadi, A.H., Goyal, A.: Robust representation learning via perceptual similarity metrics. In: International Conference on Machine Learning, pp. 10043–10053. PMLR (2021)

    Google Scholar 

  63. Tommasi, T., Patricia, N., Caputo, B., Tuytelaars, T.: A deeper look at dataset bias. In: Domain Adaptation in Computer Vision Applications, pp. 37–55 (2017)

    Google Scholar 

  64. Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR 2011, pp. 1521–1528. IEEE (2011)

    Google Scholar 

  65. Tsirigotis, C., Monteiro, J., Rodriguez, P., Vazquez, D., Courville, A.: Group robust classification without any group information. In: NeurIPS (2023)

    Google Scholar 

  66. Valle-Perez, G., Camargo, C.Q., Louis, A.A.: Deep learning generalizes because the parameter-function map is biased towards simple functions. ICLR (2019)

    Google Scholar 

  67. Vapnik, V.: Principles of risk minimization for learning theory. In: Advances in Neural Information Processing Systems, vol. 4 (1991)

    Google Scholar 

  68. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  69. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD birds-200-2011 dataset. Technical report, CNS-TR-2011-001, California Institute of Technology (2011)

    Google Scholar 

  70. Wang, A., et al.: Revise: a tool for measuring and mitigating bias in visual datasets. Int. J. Comput. Vision 130(7), 1790–1810 (2022)

    Article  Google Scholar 

  71. Wang, A., Russakovsky, O.: Directional bias amplification. In: International Conference on Machine Learning, pp. 10882–10893. PMLR (2021)

    Google Scholar 

  72. Wang, A., Russakovsky, O.: Overwriting pretrained bias with finetuning data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3957–3968 (2023)

    Google Scholar 

  73. 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)

    Google Scholar 

  74. Ward, J.H., Jr.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)

    Article  MathSciNet  Google Scholar 

  75. Wei, J., Narasimhan, H., Amid, E., Chu, W.S., Liu, Y., Kumar, A.: Distributionally robust post-hoc classifiers under prior shifts. ICLR (2023)

    Google Scholar 

  76. Wu, T., Ribeiro, M.T., Heer, J., Weld, D.S.: Errudite: scalable, reproducible, and testable error analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 747–763 (2019)

    Google Scholar 

  77. Yenamandra, S., Ramesh, P., Prabhu, V., Hoffman, J.: Facts: first amplify correlations and then slice to discover bias. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4794–4804 (2023)

    Google Scholar 

  78. Yu, R., Alì, G.S.: What’s inside the black box? AI challenges for lawyers and researchers. Leg. Inf. Manag. 19(1), 2–13 (2019)

    Google Scholar 

  79. 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)

    Google Scholar 

  80. Zhao, D., Andrews, J., Xiang, A.: Men also do laundry: multi-attribute bias amplification. In: International Conference on Machine Learning, pp. 42000–42017. PMLR (2023)

    Google Scholar 

  81. 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, pp. 2979–2989 (2017)

    Google Scholar 

  82. Zhu, X., Anguelov, D., Ramanan, D.: Capturing long-tail distributions of object subcategories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 915–922 (2014)

    Google Scholar 

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Acknowledgements

This work was funded by Sony Research. The authors would like to thank Apostolos Modas, William Thong, Wiebke Hutiri, and Andrei Margeloiu for their insightful comments and feedback on previous iterations of this manuscript. MEZ acknowledges further support from the Gates Cambridge Trust via a Gates Cambridge Scholarship.

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Authors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Mateo Espinosa Zarlenga, Zohreh Shams & Mateja Jamnik

  2. Sony AI, Tokyo, Japan

    Swami Sankaranarayanan, Jerone T. A. Andrews & Alice Xiang

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  1. Mateo Espinosa Zarlenga
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  2. Swami Sankaranarayanan
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  3. Jerone T. A. Andrews
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  4. Zohreh Shams
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  5. Mateja Jamnik
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  6. Alice Xiang
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Corresponding author

Correspondence to Mateo Espinosa Zarlenga .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Espinosa Zarlenga, M., Sankaranarayanan, S., Andrews, J.T.A., Shams, Z., Jamnik, M., Xiang, A. (2025). Efficient Bias Mitigation Without Privileged Information. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15130. Springer, Cham. https://doi.org/10.1007/978-3-031-73220-1_9

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