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Discriminatory language and biases are often present in hate speech duringconversations, which usually lead to negative impacts on targeted groups suchas those based on race, gender, and religion. To tackle this issue, we proposean approach that involves a two-step process: first, detecting hate speechusing a classifier, and then utilizing a debiasing component that generatesless biased or unbiased alternatives through prompts. We evaluated our approachon a benchmark dataset and observed reduction in negativity due to hate speechcomments. The proposed method contributes to the ongoing efforts to reducebiases in online discourse and promote a more inclusive and fair environmentfor communication.

Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser with Prompts