It's All Relative: Interpretable Models for Scoring Bias in Documents
This provides a practical tool for editors, scientists, and the public to assess bias in texts, though it is incremental as it builds on existing pairwise comparison ideas.
The authors tackled the problem of scoring bias in documents by developing an interpretable model based on pairwise comparisons of Wikipedia article revisions, achieving high accuracy where prior absolute classification methods struggled. They demonstrated its applicability across domains like news and law, showing legal texts are least biased and news most biased.
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.