  Injustice occurs when someone experiences unfair treatment or their rights
are violated and is often due to the presence of implicit biases and prejudice
such as stereotypes. The automated identification of injustice in text has
received little attention, due in part to the fact that underlying implicit
biases or stereotypes are rarely explicitly stated and that instances often
occur unconsciously due to the pervasive nature of prejudice in society. Here,
we describe a novel framework that combines the 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 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. The
news media has many instances of injustice (i.e. discriminatory narratives),
thus it is our use case here. We conduct and discuss an empirical qualitative
research study which shows how the framework can be applied to detect
injustices, even at higher volumes of data.
