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Large language models associate Muslims with violence

Large language models, which are increasingly used in AI applications, display undesirable stereotypes such as persistent associations between Muslims and violence. New approaches are needed to systematically reduce the harmful bias of language models in deployment.

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Fig. 1: GPT-3 exhibits Muslim–violence bias.
Fig. 2: Debiasing GPT-3 completions.

References

  1. Mikolov, T., Chen, K., Corrado, G. & Dean, J. in Proc. International Conference on Learning Representations (ICLR, 2013).

  2. Dai, A. M. & Le, Q. V. in Advances in Neural Information Processing Systems Vol. 28, 3079–3087 (NeurIPS, 2015).

  3. Brown, T. et al. in Advances in Neural Information Processing Systems Vol. 33, 1877–1901 (NeurIPS, 2020).

  4. Kitaev, N., Kaiser, L. & Levskaya, A. in Proc. International Conference on Learning Representations (ICLR, 2020).

  5. Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V. & Kalai, A. T. in Advances in Neural Information Processing Systems Vol. 29, 4349–4357 (NeurIPS, 2016).

  6. Nadeem, M., Bethke, A. & Reddy, S. Preprint at https://arxiv.org/abs/2004.09456 (2020).

  7. Sheng, E., Chang, K.-W., Natarajan, P. & Peng, N. in Proc. Conference on Empirical Methods in Natural Language Processing 3407–3412 (ACL, 2019).

  8. Bordia, S. & Bowman, S. R. in Proc. Conference of the North American Chapter of the Association for Computational Linguistics (ACL, 2019).

  9. Lu, K., Mardziel, P., Wu, F., Amancharla, P. & Datta, A. in Logic, Language, and Security (eds Nigam, V. et al.) 189–202 (Springer, 2020).

  10. Lewis, M. et al. in Proc. 58th Annual Meeting of the Association for Computational Linguistics 7871–7880 (ACL, 2020).

  11. Wallace, E., Feng, S., Kandpal, N., Gardner, M. & Singh, S. in Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP) 2153–2162 (ACL, 2019).

  12. Qian, Y., Muaz, U., Zhang, B. & Hyun, J. W. Preprint at https://arxiv.org/abs/1905.12801 (2019).

  13. Bender, E. M., Gebru, T., McMillan-Major, A. & Mitchell, S. in ACM Conference on Fairness, Accountability, and Transparency 610–623 (ACM, 2021).

  14. Li, X. L. & Liang, P. Preprint at https://arxiv.org/abs/2101.00190 (2021).

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Acknowledgements

We thank A. Abid, A. Abdalla, D. Khan, and M. Ghassemi for the helpful feedback on the manuscript and experiments. J.Z. is supported by NSF CAREER 1942926.

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Correspondence to James Zou.

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Peer review information Nature Machine Intelligence thanks Arvind Narayaran for their contribution to the peer review of this work.

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Abid, A., Farooqi, M. & Zou, J. Large language models associate Muslims with violence. Nat Mach Intell 3, 461–463 (2021). https://doi.org/10.1038/s42256-021-00359-2

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