  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.
