Abstract
AbstractFairness is a principal social value that is observable in civilisations around the world. Yet, a fairness metric for digital texts that describe even a simple social interaction, e.g., ‘The boy hurt the girl’ has not been developed. We address this by employing word embeddings that use factors found in a new social psychology literature review on the topic. We use these factors to build fairness vectors. These vectors are used as sentence level measures, whereby each dimension reflects a fairness component. The approach is employed to approximate human perceptions of fairness. The method leverages a pro-social bias within word embeddings, for which we obtain an F1 = 79.8 on a list of sentences using the Universal Sentence Encoder (USE). A second approach, using principal component analysis (PCA) and machine learning (ML), produces an F1 = 86.2. Repeating these tests using Sentence Bidirectional Encoder Representations from Transformers (SBERT) produces an F1 = 96.9 and F1 = 100 respectively. Improvements using subspace representations are further suggested. By proposing a
first-principles approach, the paper contributes to the analysis of digital
texts along an ethical dimension.
Funder
NGI Trust
The Psychometrics Centre, Cambridge Judge Business School Small Grants Scheme
The Isaac Newton Trust
Publisher
Springer Science and Business Media LLC
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