An Approach to Ensure Fairness in News Articles

TL;DR

This paper presents Dbias, a Python tool that detects biased language in news articles, masks biased words, and suggests less biased alternatives to promote fairness in news content.

Contribution

Introduces Dbias, a machine learning pipeline that detects and mitigates bias in news articles, improving fairness over existing neural network approaches.

Findings

Dbias effectively detects biased words in news articles.

The pipeline outperforms common neural network architectures in bias mitigation.

Reproducible and user-friendly implementation for fairness in news content.

Abstract

Recommender systems, information retrieval, and other information access systems present unique challenges for examining and applying concepts of fairness and bias mitigation in unstructured text. This paper introduces Dbias, which is a Python package to ensure fairness in news articles. Dbias is a trained Machine Learning (ML) pipeline that can take a text (e.g., a paragraph or news story) and detects if the text is biased or not. Then, it detects the biased words in the text, masks them, and recommends a set of sentences with new words that are bias-free or at least less biased. We incorporate the elements of data science best practices to ensure that this pipeline is reproducible and usable. We show in experiments that this pipeline can be effective for mitigating biases and outperforms the common neural network architectures in ensuring fairness in the news articles.