EPISTEMOLOGICAL BIAS AS A MEANS FOR THE AU-TOMATED DETECTION OF INJUSTICES IN TEXT

Abstract

Injustice occurs when someone experiences unfair treatment or their rights are violated. 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 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.

1. INTRODUCTION

The most basic duty of the media is knowledge sharing. Yet, the tool necessary for wide-spread knowledge sharing is influence. With this influence, the media is able to shape how one will understand the intricacy of a story-line in a news story with little effort. This often results in the use of epistemological biases which involves propositions that are presupposed, entailed, asserted, hedged, or boosted in text (Recasens et al., 2013) to erode or assert a person's capacity as a knower, leading to framing issues and injustice within the text. Particularly, some of these word choices lead to testimonial injustice and what we define as character injustice. Character assassination is "the deliberate destruction of an individual's reputation or credibility" (Icks et al., 2019) . This often leads to character injustice, which is an unjustified attack on a person's character that results in an unfair criticism or inaccurate representation of them. Testimonial injustice occurs when modified believability is assigned to the statement of a subject based on widely known stereotypes (Fricker, 2007) . Both of these types of injustices can lead to affirming or perpetuating stereotypes concerning the subject. Though it has always been a harsh reality with various ringing consequences, in recent years we have publicly witnessed how the affirmation of stereotypes can lead to physical violence, prejudice, and negative self-image [(Harrison & Esqueda, 1999) , (Gover et al., 2020 ), (Kuykendall, 1989) ]. These experiences are harmful and dangerous, explicitly for the victims but for all members of our society. We also consider framing injustice. According to (Entman, 2007) framing bias happens when the use of subjective, one-sided words that reveals the stance of an author occurs. Which means that an individual's choice from a set of options is influenced more by how the information is worded, rather than by the information itself. We recognise that news content can be positively or negatively framed to influence the narratives. In this paper, we aim to show how negative cases, when it affects particular subjects or individuals, can lead to framing injustice occurring toward those subjects. In this work, we seek to make room for subjects of text, even in creative writing, to not have their credibility shot or character assumed due to well-known stereotypes which are harmful and unfounded. Our proposed framework will be used to detect character, testimonial, and framing injustices. The framework includes a fine-tuned BERT model based on work from (Pryzant et al., 2020b) to automatically tag words associated with epistemological bias from an input text, use the (Kwon & Gopalan, 2021) CO-STAR model and (Sap et al., 2020) Social-Bias Frames to find stereotypes and the concepts of those stereotypes associated with the input text, and show when the tagged words (associated with some epistemological bias) or less credibility of a person are correlated with a stereotype which causes injustice. Though we could use examples from various fields (e.g. politics, marketing, medicine, etc...), we will use news media as a use-case throughout this work. Thus, we present the following contributions:

