Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder’s Perspective (2023)

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%.

## introduction contracts represent legally binding agreements among all participating stakeholders. contract drafts typically undergo several iterations before they can be finalized, owing to their business criticality. the iterations involve extensive negotiations among legal professionals on accepting or rejecting parts of drafts put forth by other parties. this collaborative exchange of perspectives is critical in ensuring that all involved parties ultimately view the resulting contracts as acceptable. commercial contracts between enterprises are invaluable sources for deriving project-specific requirements @xcite . the requirements indicated in the contractual clauses are obligatory. therefore, it is important that they are perceived as practical and implementable by stakeholders responsible for meeting those commitments. however, these non-legal stakeholders, such as project managers, requirement analysts, engineers, solution architects, and delivery personnel, are not directly involved in contract negotiations. while in-house legal experts do consult various teams to understand the feasibility and scope of individual projects before drafting a contract, there is considerable room for oversight in the high-level discussions that take place @xcite . the issue is further aggravated because the non-legal stakeholders share a limited understanding of legalese, the specialized language used in contract writing. consequently, many clauses can seem unfair to non-legal stakeholders despite being the results of iterative negotiations by legal experts. the perception of all involved stakeholders needs to be adequately captured during contract drafting for smooth governance @xcite . in this work, we examine contracts from an angle of fairness, or lack thereof, from non-legal stakeholders' perspectives who must implement the obligatory requirements outlined within contracts. to achieve this goal, we conducted an empirical study involving legal and non-legal stakeholders who are often participants in contract negotiations and/or responsible for implementing the projects according to contract obligations. our primary objective was to gain insights into their perceptions regarding fairness within contracts. our analysis revealed multiple scenarios of unfairness that can occur within contract text. some clauses are interpreted as 'clearly unfair' by both legal and nonlegal stakeholders because they seem to be causing an obvious imbalance in parties' rights or obliga-tions. such clear unfairness is often encountered in clauses that address critical project management aspects, including unilateral termination, unilateral modification of terms, restrictions on indemnity claims, and jurisdictional matters. our study also revealed that some clauses containing requirements may be considered 'potentially unfair' by non-legal stakeholders because of a risk of non-compliance arising due to the inclusion of ambiguous language. such clauses can make certain parties vulnerable by significantly impacting the cost and effort associated with fulfilling the obligatory requirements. for instance, the sentence the vendor should implement appropriate measures to ensure a level of security commensurate to the risk vaguely specifies the measures required to implement the security controls. if the expectation regarding what constitutes 'appropriate' security controls differs between the vendor and the customer, the customer may perceive the vendor's enforcement of security controls as unfair and a breach of contract. based on the findings of the empirical study, we envisage building an automated assistant tailored to streamline the process of contract review by bringing in the perspectives of non-legal stakeholders. the assistant would: (1) identify contractual sentences likely to be perceived as unfair by non-legal stakeholders; (2) distinguish between clearly unfair and potentially unfair clauses; (3) recommend questions to be asked to the parties involved, thereby assisting in the resolution of the unfairness within the sentences. as a step towards building such a system, this paper presents our work on automating the classification of sentences as fair, potentially unfair, and clearly unfair. we investigate the ability of pre-trained language models (plms) to perform the classification by comparing chain of thought (cot) prompting @xcite and semi-supervised fine-tuning. to date, prior work has primarily focused on detecting potentially unfair clauses within online terms of service (tos) agreements, emphasizing fairness from a consumer-centric standpoint @xcite . in comparison, our work delves into the intricate organizational dynamics and business context inherent with respect to commercial contracts. in our knowledge, this is the first work considering fairness in commercial contracts from the perspective of non-legal stakeholders. furthermore, our research introduces a novel dimension by acknowledging ambiguity as a potential trigger for a perception of unfairness within contractual clauses-an aspect hitherto unexplored in the literature. finally, making the perspectives of nonlegal stakeholders available to legal teams while negotiating contracts is a first attempt, and building an automated assistant for this purpose contributes to both legal and non-legal stakeholders. ## contract fairness from a non-legal stakeholder's perspective our initial course of action was to empirically delineate the concept of unfairness in commercial contracts, particularly from a non-legal standpoint, and analyze if it differed from the viewpoint of legal stakeholders. moreover, we sought to ascertain the extent of participation of non-legal stakeholders in contract negotiations. ## task formulation a key finding of our study was that the fairness perception of non-legal stakeholders might get overlooked during contract negotiations. therefore, we decided to build an automated assistant that can leverage the reasoning capabilities of plms to unfairness class definition example rationale for unfairness potentially unfair this category includes all contractual sentences containing ambiguous language, which can potentially cause a risk of noncompliance. data processor shall keep personal information sufficiently isolated from other data on the server in an appropriate manner to prevent it from being misused. the example is potentially unfair because what constitutes sufficient isolation and appropriate manner is vague and could be interpreted in multiple ways. this ambiguity makes it difficult for the data processor to comply with the obligation and makes personal information susceptible to misuse. clearly unfair this category includes all clauses that deal with situations that lead to a direct imbalance in parties' rights or obligations. purchaser may modify the policies incorporated with this order anytime, and supplier will be obligated to implement such modifications. the clause provides the purchaser unilateral rights to modify the policies of the agreement without any prior notice to the supplier. in order to build a contract negotiation assistant, we first analyzed if the unfairness scenarios highlighted by the empirical study participants could be detected automatically. while language models can be used to detect ambiguities @xcite and imbalances in rights and obligations @xcite , the automated detection of project-specific details necessitates knowledge of contextual features such as organizational rules, accepted industry standards, and domain expertise. these aspects can differ across projects, and therefore, cannot be generalized to build a universal negotiation assistant. therefore, we considered unfair project-specific details out of scope for automation. furthermore, language models are presently incapable of reasoning about humanlevel attributes affecting fairness @xcite . consequently, we also did not include such attributes in our classification. we plan to analyze project-specific and human-level attributes affecting the fairness of contracts in future work. with this background, we formulated three distinct classes for manually labeling our dataset from a non-legal stakeholder's perspective-fair, potentially unfair, and clearly unfair. each of these classes can be explained as follows: fair: all contractual sentences that follow the definition of fairness mentioned in section 3.3 are labeled as fair. additionally, sentences containing ambiguous terms that do not directly impact implementation are marked fair. for instance, the clause while visiting the buyer's sites, representatives of the vendor shall conduct themselves in a businesslike manner is vague because the term 'businesslike manner' can be interpreted by the vendor in multiple ways. however, this clause is not unfair because it does not hold business criticality concerning the deliverables of the project. potentially unfair: all sentences containing ambiguous language that can potentially cause a risk of non-compliance are labeled as potentially unfair. for instance, any clause dealing with change requests and issue management in the service industry is particularly susceptible to ambiguities. the accepted practice in the industry is that the vendor responsible for providing services charges extra to implement change requests. however, no extra charges can be levied by the vendor if the change request is raised due to an identified bug in the services provided. if vague terms such as 'appropriate' are used in clauses that contain requirements, the boundaries of the services remain ill-defined and may cause conflicts between the parties. such conflicts can be avoided if ambiguous terms are clarified before implementation. we note here that we do not expect the clarified details to be explicitly included in the contract drafts. as pointed out by legal stakeholders in the empirical study, contracts are written and will continue to be written in a language that is not over-specific and granular @xcite . in this work, we focus on detecting potentially unfair sentences to clarify them between legal and nonlegal stakeholders before the contracts are signed to mitigate any negative consequences. clearly unfair: all sentences that cause a direct imbalance in parties' rights and obligations are labeled as clearly unfair. @xcite identified scenarios that can lead to an imbalance in online tos agreements. based on the empirical study responses, we discovered that some of these scenarios also occur in commercial contracts. these include: (1) jurisdiction-related restrictions; ## experiments we performed the ternary classification using two methods-(1) data augmentation and self-training and (2) cot prompting. ## conclusion in this work, we analyzed and automatically detected unfairness in commercial contracts from the perspective of non-legal stakeholders. we conducted an empirical study to determine what constitutes unfairness according to non-legal stakeholders and how it differs from the perception of their legal counterparts. we discovered that non-legal stakeholders consider certain ambiguous sentences potentially unfair, even though they are legally accurate. since these viewpoints are not directly highlighted during negotiations, we proposed building an automated negotiation assistant that can flag potentially unfair sentences according to non-legal stakeholders. as a step towards building such an assistant, we formulated a novel ternary classification task to detect unfairness in contracts. our experiments using different plms and techniques revealed that self-trained bert performed the classification with an accuracy of 84% on a proprietary contract dataset. it outperformed cot prompting using vicuna by a margin of 9%. in the future, we plan to build an end-toend assistant that can reason about unfairness in contracts from the perspective of non-legal stakeholders and recommend automatically generated questions to be asked during contract negotiations. moreover, we plan to incorporate different hybrid and prompting-driven techniques to minimize misclassification. we also plan to analyze how project-specific attributes and human-level concerns can be incorporated into the assistant to generate more personalized questions for stakeholders.