GEI: Advancing qualitative data analysis efficiency with artificial intelligence for fisheries management

Artificial intelligence (AI) presents an opportunity to revolutionize the analysis of qualitative data in fisheries-focused social science research. Many social science studies rely on qualitative data, such as stakeholder interviews, surveys, observer reports, and public testimony. However, the use of qualitative data has been limited by the amount of time and labor it has traditionally taken to conduct qualitative data analysis. Advances in AI, including large language models (LLMs), offer an opportunity to speed up and improve efficiency of these analyses. This project will utilize existing NOAA Fisheries qualitative data sets for three case studies to explore how AI-driven methods can enhance social science research in fisheries management. Each case study will address a different methodological approach, including conceptual modeling, qualitative thematic coding, and stakeholder-driven management strategy evaluation. Additionally, each study will explore a different research question relevant for different fisheries management needs such as stock assessment, community vulnerability assessments, and social impact assessments. By convening an interdisciplinary team to synthesize the state of the science and execute the described case studies, this project will produce a road map for how to use various AI tools for different data types to deliver more timely and rigorous insights for future decision-making.