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Differential Tweetment: Mitigating Racial Dialect Bias in Harmful Tweet Detection

Authors:

Ari Ball-Burack,

Michelle Seng Ah Lee,

Jennifer Cobbe,

Jatinder SinghAuthors Info & Claims

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Pages 116 - 128

https://doi.org/10.1145/3442188.3445875

Published: 01 March 2021 Publication History

Abstract

Automated systems for detecting harmful social media content are afflicted by a variety of biases, some of which originate in their training datasets. In particular, some systems have been shown to propagate racial dialect bias: they systematically classify content aligned with the African American English (AAE) dialect as harmful at a higher rate than content aligned with White English (WE). This perpetuates prejudice by silencing the Black community. Towards this problem we adapt and apply two existing bias mitigation approaches: preferential sampling pre-processing and adversarial debiasing in-processing. We analyse the impact of our interventions on model performance and propagated bias. We find that when bias mitigation is employed, a high degree of predictive accuracy is maintained relative to baseline, and in many cases bias against AAE in harmful tweet predictions is reduced. However, the specific effects of these interventions on bias and performance vary widely between dataset contexts. This variation suggests the unpredictability of autonomous harmful content detection outside of its development context. We argue that this, and the low performance of these systems at baseline, raise questions about the reliability and role of such systems in high-impact, real-world settings.

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https://dl.acm.org/doi/10.1145/3630106.3658902
Lyu YCai JCallis ACotter KCarroll J(2024)"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers’ Content Moderation ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642148(1-15)Online publication date: 11-May-2024
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Index Terms

Differential Tweetment: Mitigating Racial Dialect Bias in Harmful Tweet Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Social and professional topics
  1. Computing / technology policy
    1. Censorship
  2. User characteristics
    1. Race and ethnicity

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cover image ACM Conferences

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

March 2021

899 pages

ISBN:9781450383097

DOI:10.1145/3442188

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Published: 01 March 2021

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March 3 - 10, 2021

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https://dl.acm.org/doi/10.1145/3630106.3658902
Lyu YCai JCallis ACotter KCarroll J(2024)"I Got Flagged for Supposed Bullying, Even Though It Was in Response to Someone Harassing Me About My Disability.": A Study of Blind TikTokers’ Content Moderation ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642148(1-15)Online publication date: 11-May-2024
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Wang YSingh L(2023)Mitigating demographic bias of machine learning models on social mediaProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3617694.3623244(1-12)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3617694.3623244
Harris CJohnson APalmer SYang DBruckman A(2023)"Honestly, I Think TikTok has a Vendetta Against Black Creators": Understanding Black Content Creator Experiences on TikTokProceedings of the ACM on Human-Computer Interaction10.1145/36101697:CSCW2(1-31)Online publication date: 4-Oct-2023
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