- Models that are trained with certain datasets affects which people the models work for
- Racial classifications can easily become widespread if the dataset is used frequently

University of Virginia, Charlottesville

- Racial bias occurs in datasets when it associates names with a certain race
- In addition to what the dataset contains, it is important to consider who the data was authored by

Racial bias in NLP datasets

Anjalie Field, Su Lin Blodgett, Zeerak Waseem, and Yulia Tsvetkov. 2021. A Survey of Race, Racism, and Anti-Racism in NLP. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1905–1925, Online. Association for Computational Linguistics.

A Survey of Race, Racism, and Anti-Racism in NLP

Consequences of Racial Bias in NLP Datasets

It is important to analyze datasets to prevent the spread of racial bias because:

- The type of person creating the data determines who the data will work for (ex. certain facial recognition algorithms working better on lighter-skinned people)
- NLP research produces technology that affects people's lives. Because of this, the prevention of racially-motivated technologies is beneficial.

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