Dbias: Detecting biases and ensuring Fairness in news articles

TL;DR

Dbias is an open-source Python tool that detects and reduces bias in news articles by identifying biased words and suggesting less biased alternatives, outperforming existing fairness models.

Contribution

Introduction of Dbias, a novel tool for bias detection and mitigation in news articles, with extensive experiments demonstrating its superior performance.

Findings

Dbias outperforms baseline models in accuracy and fairness.

The tool effectively identifies biased words in news articles.

It provides bias-reduced sentence suggestions.

Abstract

Because of the increasing use of data-centric systems and algorithms in machine learning, the topic of fairness is receiving a lot of attention in the academic and broader literature. This paper introduces Dbias (https://pypi.org/project/Dbias/), an open-source Python package for ensuring fairness in news articles. Dbias can take any text to determine if it is biased. Then, it detects biased words in the text, masks them, and suggests a set of sentences with new words that are bias-free or at least less biased. We conduct extensive experiments to assess the performance of Dbias. To see how well our approach works, we compare it to the existing fairness models. We also test the individual components of Dbias to see how effective they are. The experimental results show that Dbias outperforms all the baselines in terms of accuracy and fairness. We make this package (Dbias) as publicly available for the developers and practitioners to mitigate biases in textual data (such as news articles), as well as to encourage extension of this work.