Abstract
Traditional contract review becomes increasingly time-consuming when attorneys face a surge in the number of contracts. Artificial Intelligence (AI) offers a solution to enhance the efficiency of reviews, but its uncertainty raises concerns among legal professionals. In this paper, we explore innovative clues and interaction design for trust calibration through a scenario-centric approach. Conducting formative experiments and semi-structured interviews, we conducted a contextual investigation with 24 attorneys, uncovering mismatches and trust calibration challenges between commercial AI tools and manual review processes in practical use. Based on these findings, we collaboratively designed seven key design components with attorneys and developed the ContractMind system prototype using a Wizard-of-Oz design approach. Through evaluation with 16 attorneys, we calculated and analyzed the differences between participants’ perceived trustworthiness and AI system capabilities. Compared to commercial artificial intelligence tools, our system is more conducive to trust calibration, enabling them to smoothly review contracts and make informed decisions. This work takes the first step in exploring trust calibration design for AI contract review tools and designs and evaluates ContractMind system prototype. We also provide design considerations for future AI contract review tools and trust calibration.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call