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.

这篇研究论文描述了一个结合使用经过微调的基于BERT的偏见检测模型、两个刻板印象检测模型和基于词汇的方法的新型框架，用于自动检测文本中的不公正，并通过一个实证定性研究展示了该框架在检测不公正时的应用，即使在大量数据的情况下。

使用认识论偏见作为文本中不公正性的自动检测手段