Toward an Optimized Human-AI Reviewing Strategy for Contract Inspection

Abstract

Contracts are high-value documents that mediate many day-to-day business transactions. Knowledge workers, such as auditors and financial analysts often need to review large collections of contracts containing complex clauses. While prior work across other applications has evaluated the benefits of human-AI collaboration when dealing with large amounts of data, there is a lack of human-centered approaches for contract inspection tools. To address this gap, we present findings from qualitative interviews conducted with six knowledge workers at a large enterprise and discuss their reviewing strategies, usage of tools and perception of AI. We identify that an important but often overlooked aspect of contracts is their cross-functional use as a knowledge base for revenue recognition and forecasting, which can in turn impact business decisions. We propose a framework and preliminary tool that strives to support knowledge workers in adopting a reviewing strategy that creates a more efficient and optimal business pipeline. We believe that this framework may provide a foundation to bridge the gap between knowledge acquisition and decision-making and encourage researchers to diversify their design and evaluation methods.