Artificial intelligence systems copy and amplify existing societal biases, a problem that by now is widely acknowledged and studied. But is current research of gender bias in natural language processing actually moving towards a resolution, asks Marta R. Costa-jussà.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2016).
Calders, T. & Verwer, S. Data Min. Knowl. Discov. 21, 277–292 (2010).
LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).
Cislak, A., Formanowicz, M. & Saguy, T. Scientometrics 115, 189–200 (2018).
Ross, K., Boyle, K., Carter, C. & Ging, D. Journalism Stud. 19, 824–845 (2018).
Sweeney, L. Queue 11, 29 (2013).
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V. & Kalai, A. In Proc. 30th International Conference on Neural Information Processing Systems 4356–4364 (Curran Associates, 2016).
Caliskan, A., Bryson, J. J. & Narayanan, A. Science 356, 183–186 (2017).
Zhao, J., Zhou, Y., Li, Z., Wang, W. & Chang, K.-W. Proc. 2018 Conference on Empirical Methods in Natural Language Processing 4847–4853 (Association for Computational Linguistics, 2018) (2018).
Nissim, M., van Noord, R. & van der Goot, R. Preprint at http://arxiv.org/abs/1905.09866 (2019).
Gonen, H. & Goldberg, Y. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 609–614 (Association for Computational Linguistics, 2019).
Basta, C., Costa-jussà, M. R. & Casas, N. In Proc. First Workshop on Gender Bias in Natural Language Processing 33–39 (Association for Computational Linguistics, 2019).
Vanmassenhove, E., Hardmeier, C. & Way, A. In Proc. 2018 Conference on Empirical Methods in Natural Language Processing 3003–3008 (Association for Computational Linguistics, 2018).
Escudé Font, J. & Costa-jussà, M. R. In Proc. First Workshop on Gender Bias in Natural Language Processing 147–154 (Association for Computational Linguistics, 2019).
Stanovsky, G., Smith, N. A. & Zettlemoyer, L. In Proc. 57th Annual Meeting of the Association for Computational Linguistics 1679–1684 (Association for Computational Linguistics, 2019).
Menegatti, M. & Rubini, M. Oxford Research https://doi.org/10.1093/acrefore/9780190228613.013.470 (2017).
Lewis, M. & Lupyan, G. Preprint at https://psyarxiv.com/7qd3g (2019).
Lindqvist, A., Renström, E. A. & Gustafsson Sendén, M. Sex Roles 81, 109–117 (2019).
Aharoni, R., Johnson, M. & Firat, O. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 3874–3884 (Association for Computational Linguistics, 2019).
This work is supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MINECO/ERDF, EU) through the programme Ramón y Cajal.
Rights and permissions
About this article
Cite this article
Costa-jussà, M.R. An analysis of gender bias studies in natural language processing. Nat Mach Intell 1, 495–496 (2019). https://doi.org/10.1038/s42256-019-0105-5
This article is cited by
Towards universal translation
Nature Machine Intelligence (2021)
Extensive study on the underlying gender bias in contextualized word embeddings
Neural Computing and Applications (2021)
Machine Learning of Dislocation-Induced Stress Fields and Interaction Forces