Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal
  Stakeholder's Perspective
Anmol Singhal Preethu Rose Anish Shirish Karande Smita Ghaisas
Commercial contracts are known to be a valuable source for deriving
project-specific requirements. However, contract negotiations mainly occur
among the legal counsel of the parties involved. The participation of non-legal
stakeholders, including requirement analysts, engineers, and solution
architects, whose primary responsibility lies in ensuring the seamless
implementation of contractual terms, is often indirect and inadequate.
Consequently, a significant number of sentences in contractual clauses, though
legally accurate, can appear unfair from an implementation perspective to
non-legal stakeholders. This perception poses a problem since requirements
indicated in the clauses are obligatory and can involve punitive measures and
penalties if not implemented as committed in the contract. Therefore, the
identification of potentially unfair clauses in contracts becomes crucial. In
this work, we conduct an empirical study to analyze the perspectives of
different stakeholders regarding contractual fairness. We then investigate the
ability of Pre-trained Language Models (PLMs) to identify unfairness in
contractual sentences by comparing chain of thought prompting and
semi-supervised fine-tuning approaches. Using BERT-based fine-tuning, we
achieved an accuracy of 84% on a dataset consisting of proprietary contracts.
It outperformed chain of thought prompting using Vicuna-13B by a margin of 9%.