Injustices in text are often subtle since implicit biases or stereotypes frequently operate unconsciously due to the pervasive nature of prejudice in society. This makes automated detection of injustices more challenging which leads to them being often overlooked. We introduce a novel framework that combines knowledge from epistemology to enhance the detection of implicit injustices in text using NLP models to address these complexities and offer explainability. Our empirical study shows how our framework can be applied to effectively detect these injustices. We validate our framework using a human baseline study which mostly agrees with the choice of implicit bias, stereotype, and sentiment. The main feedback from the study was the extended time required to analyze, digest, and decide on each component of our framework. This highlights the importance of our automated framework pipeline that assists users in detecting implicit injustices while offering explainability and reducing time burdens on humans.

翻译：文本中的不公正现象往往具有隐蔽性，这是由于社会偏见普遍存在，导致隐性偏见或刻板印象常在无意识状态下运作。这种特性使得不公正的自动检测更具挑战性，因而常被忽视。本文提出一种创新框架，该框架整合认识论知识，通过自然语言处理模型增强对文本中隐性不公正现象的检测能力，以应对上述复杂性并提供可解释性。实证研究表明，该框架能有效检测此类不公正现象。我们通过人工基线研究验证了框架的有效性，该研究结果在隐性偏见、刻板印象和情感倾向的判断上具有较高一致性。研究反馈的主要问题在于分析、消化及判定框架各组件所需时间较长，这凸显了我们自动化框架流程的重要性：该流程既能辅助用户检测隐性不公正现象并提供解释，又可减轻人类的时间负担。