It's All Relative: Interpretable Models for Scoring Bias in Documents

TL;DR

This paper introduces an interpretable pairwise comparison model to score bias in documents, trained on Wikipedia revisions, and applicable across domains like news and legal texts, providing insights into bias indicators.

Contribution

The paper presents a novel interpretable pairwise bias scoring model trained on Wikipedia revisions, capable of analyzing bias across various domains with high accuracy and interpretability.

Findings

The model accurately compares bias levels in document pairs.

Words most indicative of bias can be identified from the model.

Bias levels vary across domains, with legal texts least biased and news most biased.

Abstract

We propose an interpretable model to score the bias present in web documents, based only on their textual content. Our model incorporates assumptions reminiscent of the Bradley-Terry axioms and is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. While prior approaches based on absolute bias classification have struggled to obtain a high accuracy for the task, we are able to develop a useful model for scoring bias by learning to perform pairwise comparisons of bias accurately. We show that we can interpret the parameters of the trained model to discover the words most indicative of bias. We also apply our model in three different settings - studying the temporal evolution of bias in Wikipedia articles, comparing news sources based on bias, and scoring bias in law amendments. In each case, we demonstrate that the outputs of the model can be explained and validated, even for the two domains that are outside the training-data domain. We also use the model to compare the general level of bias between domains, where we see that legal texts are the least biased and news media are the most biased, with Wikipedia articles in between. Given its high performance, simplicity, interpretability, and wide applicability, we hope the model will be useful for a large community, including Wikipedia and news editors, political and social scientists, and the general public.