An Approach to Ensure Fairness in News Articles
This addresses fairness issues in news articles for users of recommender and information retrieval systems, but it is incremental as it builds on existing bias mitigation methods.
The paper tackles the problem of bias in news articles by introducing Dbias, a Python package that detects biased text, masks biased words, and recommends less biased alternatives, showing it outperforms common neural network architectures in fairness mitigation.
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.