A Study on Bias Detection and Classification in Natural Language Processing

TL;DR

This paper investigates bias detection in NLP, focusing on hate speech classification, by combining datasets and analyzing their limitations to improve model performance.

Contribution

It introduces methods for combining datasets for hate speech detection and analyzes dataset issues affecting bias detection in NLP.

Findings

Dataset combination significantly impacts model accuracy

Identified scarcity and skewness as key dataset issues

Proposed strategies to mitigate dataset limitations

Abstract

Human biases have been shown to influence the performance of models and algorithms in various fields, including Natural Language Processing. While the study of this phenomenon is garnering focus in recent years, the available resources are still relatively scarce, often focusing on different forms or manifestations of biases. The aim of our work is twofold: 1) gather publicly-available datasets and determine how to better combine them to effectively train models in the task of hate speech detection and classification; 2) analyse the main issues with these datasets, such as scarcity, skewed resources, and reliance on non-persistent data. We discuss these issues in tandem with the development of our experiments, in which we show that the combinations of different datasets greatly impact the models' performance.