Measuring a Texts Fairness Dimensions Using Machine Learning Based on Social Psychological Factors

TL;DR

This paper develops a machine learning approach to quantify fairness perceptions in texts by leveraging social psychology principles and word embeddings, achieving high accuracy in fairness assessment.

Contribution

It introduces a novel method combining social psychology insights with word embeddings to measure fairness perceptions in texts, moving beyond rule-based approaches.

Findings

Pro-social bias in word embeddings yields F1=81.0.

ML approach with fairness approximation vector achieves F1=86.2.

Methodology can be improved by subspace projection of sentence embeddings.

Abstract

Fairness is a principal social value that can be observed in civilisations around the world. A manifestation of this is in social agreements, often described in texts, such as contracts. Yet, despite the prevalence of such, a fairness metric for texts describing a social act remains wanting. To address this, we take a step back to consider the problem based on first principals. Instead of using rules or templates, we utilise social psychology literature to determine the principal factors that humans use when making a fairness assessment. We then attempt to digitise these using word embeddings into a multi-dimensioned sentence level fairness perceptions vector to serve as an approximation for these fairness perceptions. The method leverages a pro-social bias within word embeddings, for which we obtain an F1= 81.0. A second approach, using PCA and ML based on the said fairness approximation vector produces an F1 score of 86.2. We detail improvements that can be made in the methodology to incorporate the projection of sentence embedding on to a subspace representation of fairness.