Epistemological Bias As a Means for the Automated Detection of Injustices in News Media

Kenya S. Andrews, Lamogha Chiazor

Keywords: testimonial injustice, character injustice, framing bias, epistemological bias, news media

TL;DR: We leverage the combined use of a fine-tuned epistemological detection model, two stereotype detection models, and a lexicon-based approach to show that epistemological biases can assist with the automatic detection of injustices in text.

Abstract: Injustice occurs when someone experiences unfair treatment or their rights are violated. In the context of news media, injustices represent a form of bias through which discriminatory narratives can arise and spread. The automated identification of injustice in text has received little attention, due in part to the fact that underlying stereotypes are rarely explicitly stated and that instances often occur unconsciously due to the pervasive nature of prejudice in society. Here, we leverage the combined use of a fine-tuned BERT-based bias detection model, two stereotype detection models, and a lexicon-based approach to show that epistemological biases (i.e., words, which through their use, presupposes, entails, asserts, hedges, or boosts text to erode or assert a person's capacity as a knower) can assist with the automatic detection of injustice in text.

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