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

TL;DR

This paper presents a novel epistemology-inspired framework that enhances automated detection of subtle, implicit injustices in text, providing explainability and reducing human analysis time.

Contribution

It introduces a new epistemology-based approach for NLP models to detect implicit biases and injustices, improving explainability and efficiency.

Findings

Effective detection of implicit injustices demonstrated

High agreement with human baseline study

Framework reduces analysis time for users

Abstract

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.