Title:
Mitigating Racial Biases in Toxic Language Detection
Mitigating Racial Biases in Toxic Language Detection
Author(s)
Halevy, Matan
Advisor(s)
Howard, Ayanna M.
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Abstract
Recent research has demonstrated how racial biases against users who write African
American English exists in popular toxic language datasets. While previous work has focused on a single fairness criteria, we propose to use additional descriptive fairness metrics
to better understand the source of these biases. We demonstrate that different benchmark
classifiers, as well as two in-process bias-remediation techniques, propagate racial biases
even in a larger corpus. We then propose a novel ensemble-framework that uses a specialized classifier that is fine-tuned to the African American English dialect. We show that our
proposed framework substantially reduces the racial biases that the model learns from these
datasets. We demonstrate how the ensemble framework improves fairness metrics across
all sample datasets with minimal impact on the classification performance, and provide
empirical evidence to its ability to unlearn the annotation biases towards authors who use
African American English.
** Please note that this work may contain examples of offensive words and phrases.
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Date Issued
2022-05-05
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