Leveraging Artificial Intelligence and Natural Language Processing in Legal Epidemiology Studies: Opportunities and Challenges

Abstract

Legal epidemiology — the study of how laws influence health outcomes — is an emerging field with potential to inform policy and improve public health. Shortages of specialists who can commit the extensive time required for these analyses has hindered progress in this area. Features of laws make them ideal candidates for artificial intelligence and natural language processing (AI/NLP) to address these constraints: standardized format, defined terms, and common terminology. However, there are concerns regarding valid AI/NLP application, especially given current underreporting of legal research in published policy evaluation studies. Drawing on lessons learned from case deployments and recent literature, we review opportunities and challenges for methods using AI/NLP in scientific legal research: assessing the scope of legal documents and data, identifying and collecting primary legal data, developing and applying coding schemes, and implementing quality controls to ensure the validity of produced legal datasets. We highlight methodologic research areas and innovation in the application of AI/NLP for scientific studies regarding effects of law on health and indicate methodologic reporting elements that will be essential for the field’s innovation. This review provides a method-focused research agenda for AI/NLP in scientific legal epidemiology studies that might accelerate the field’s growth and effects on evidence-based policy.