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Surfacing Racial Stereotypes through Identity Portrayal

Authors:

Gauri Kambhatla,

Rada MihalceaAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 1604 - 1615

https://doi.org/10.1145/3531146.3533217

Published: 20 June 2022 Publication History

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Abstract

Content warning: this paper discusses and contains content that may be offensive or upsetting.

People express racial stereotypes through conversations with others, increasingly in a digital format; as a result, the ability to computationally identify racial stereotypes could be beneficial to help mitigate some of the harmful effects of stereotyping. In this work, we seek to better understand how we can computationally surface racial stereotypes in text by identifying linguistic features associated with differences in racial identity portrayal, focused on two races (Black and White). We collect novel data of individuals’ self-presentation via crowdsourcing, where each crowdworker answers a set of prompts from their own perspective (real identity), and from the perspective of another racial identity (portrayed identity), keeping the gender constant. We use these responses as a dataset to identify stereotypes. Through a series of experiments based on classifications between real and portrayed identities, we show that generalizations and stereotypes appear to be more prevalent amongst white participants than black participants. Through analyses of predictive words and word usage patterns, we find that some of the most predictive features of an author portraying a different racial identity are known stereotypes, and reveal how people of different identities see themselves and others.

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Index Terms

Surfacing Racial Stereotypes through Identity Portrayal
1. Computing methodologies
2. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

June 2022

2351 pages

ISBN:9781450393522

DOI:10.1145/3531146

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June 21 - 24, 2022

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Jaime SKern C(2024)Ethnic Classifications in Algorithmic Fairness: Concepts, Measures and Implications in PracticeProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658902(237-253)Online publication date: 3-Jun-2024
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https://doi.org/10.1016/j.eswa.2024.125082
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