Title: Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder's Perspective Show Chinese title

Title: 从非法律利益相关者的角度减轻合同中的感知不公平性

Abstract: 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%. Show Chinese abstract

Abstract: 商业合同被认为是获取项目特定需求的宝贵来源。 然而，合同谈判主要在各方的法律顾问之间进行。 非法律利益相关者，包括需求分析师、工程师和解决方案架构师，他们的主要职责是确保合同条款的无缝实施，但他们的参与通常是间接且不足的。 因此，合同条款中的许多句子虽然在法律上是准确的，但从非法律利益相关者的实施角度来看可能显得不公平。 这种看法存在问题，因为条款中指出的需求是强制性的，如果不按合同中的承诺执行，可能会涉及惩罚措施和罚款。 因此，识别合同中可能存在不公平的条款变得至关重要。 在本研究中，我们进行了一项实证研究，以分析不同利益相关者对合同公平性的观点。 然后，我们研究了预训练语言模型（PLMs）通过比较思维链提示和半监督微调方法来识别合同句子中的不公平性的能力。 使用基于BERT的微调，在由专有合同组成的数据集上达到了84%的准确率。 与使用Vicuna-13B的思维链提示相比，其准确率高出9%。