Tasks and Roles in Legal AI: Data Curation, Annotation, and Verification

Abstract

The application of AI tools to the legal field feels natural: large legal document collections could be used with specialized AI to improve workflow efficiency for lawyers and ameliorate the "justice gap" for underserved clients. However, legal documents differ from the web-based text that underlies most AI systems. The challenges of legal AI are both specific to the legal domain, and confounded with the expectation of AI's high performance in high-stakes settings. We identify three areas of special relevance to practitioners: data curation, data annotation, and output verification. First, it is difficult to obtain usable legal texts. Legal collections are inconsistent, analog, and scattered for reasons technical, economic, and jurisdictional. AI tools can assist document curation efforts, but the lack of existing data also limits AI performance. Second, legal data annotation typically requires significant expertise to identify complex phenomena such as modes of judicial reasoning or controlling precedents. We describe case studies of AI systems that have been developed to improve the efficiency of human annotation in legal contexts and identify areas of underperformance. Finally, AI-supported work in the law is valuable only if results are verifiable and trustworthy. We describe both the abilities of AI systems to support evaluation of their outputs, as well as new approaches to systematic evaluation of computational systems in complex domains. We call on both legal and AI practitioners to collaborate across disciplines and to release open access materials to support the development of novel, high-performing, and reliable AI tools for legal applications.