With the rise of online hate speech, automatic detection of Hate Speech,
Offensive texts as a natural language processing task is getting popular.
However, very little research has been done to detect unintended social bias
from these toxic language datasets. This paper introduces a new dataset
ToxicBias curated from the existing dataset of Kaggle competition named "Jigsaw
Unintended Bias in Toxicity Classification". We aim to detect social biases,
their categories, and targeted groups. The dataset contains instances annotated
for five different bias categories, viz., gender, race/ethnicity, religion,
political, and LGBTQ. We train transformer-based models using our curated
datasets and report baseline performance for bias identification, target
generation, and bias implications. Model biases and their mitigation are also
discussed in detail. Our study motivates a systematic extraction of social bias
data from toxic language datasets. All the codes and dataset used for
experiments in this work are publicly available

本研究介绍了一个新的数据集 ToxicBias，这是从 Kaggle 竞赛中现有的数据集 “Jigsaw Unintended Bias in Toxicity Classification” 中精心策划的，旨在检测社交偏见及其类别和目标群体。我们使用我们策划的数据集对基于转换器的模型进行训练，并报告了偏见识别、目标生成和偏见影响的基线性能。研究还详细讨论了模型偏见及其缓解。