Detecting Unintended Social Bias in Toxic Language Datasets
This work addresses the issue of social bias detection in toxic language for NLP researchers and practitioners, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of detecting unintended social biases in toxic language datasets by introducing a new dataset, ToxicBias, curated from an existing Kaggle competition dataset, and reports baseline performance for bias identification, target generation, and bias implications using transformer-based models.
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