[638] Blodgett, S. L., S. Barocas, H. D. III, et al. Language (technology) is power: A critical survey
of "bias" in NLP. In D. Jurafsky, J. Chai, N. Schluter, J. R. Tetreault, eds., Proceedings of the
58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online,
July 5-10, 2020, pages 5454–5476. Association for Computational Linguistics, 2020.

[639] Guo, W., A. Caliskan. Detecting emergent intersectional biases: Contextualized word embed-
dings contain a distribution of human-like biases. In M. Fourcade, B. Kuipers, S. Lazar, D. K.
Mulligan, eds., AIES ’21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event,
USA, May 19-21, 2021, pages 122–133. ACM, 2021.
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