Mitigating Bias in Conversations: A Hate Speech Classifier and Debiaser
  with Prompts
Shaina Raza Chen Ding Deval Pandya
Discriminatory language and biases are often present in hate speech during
conversations, which usually lead to negative impacts on targeted groups such
as those based on race, gender, and religion. To tackle this issue, we propose
an approach that involves a two-step process: first, detecting hate speech
using a classifier, and then utilizing a debiasing component that generates
less biased or unbiased alternatives through prompts. We evaluated our approach
on a benchmark dataset and observed reduction in negativity due to hate speech
comments. The proposed method contributes to the ongoing efforts to reduce
biases in online discourse and promote a more inclusive and fair environment
for communication.