LEVERAGING AI FOR IN-DEPTH ANALYSIS OF QUALITATIVE DATA AT SCALE

Although complex and labor-intensive when done at scale, qualitative data holds critical insights for research supporting food system transformations. AI now presents opportunities to change and enable the efficient and effective analysis of large amounts of qualitative data. By integrating AI into content analysis and enriching data models, there is also opportunity to use qualitive research more strategically to support food system transformation. 

On Thursday April 4, 2024 a group of scientists and food systems experts who either directly engage in AI and/or benefit from the findings of AI-enhanced qualitative research came together to explore these issues. Over the day, they shared and developed potential user cases for AI in qualitative research and mixed methods approaches, pinpointed existing gaps in qualitative research using AI, and identified steps to integrate AI into qualitative research effectively.  The event was cohosted by Juno Evidence Alliance, FACT Alliance, and IFPRI. 

Among many themes, the group touched on the potential applications for AI tools to link social, climatic, and biological data for informing the introduction and scaling of new crop breeds. They also explored how these tools could provide insight into policy processes at the country level, by highlighting policy priorities, bottlenecks, and opportunities. Some members also noted the incredible potential to analyze equity and inclusion in policy discourse with AI tools along with ways to elevate the voices of marginalized groups. Based on these rich discussions the working group plans to further explore and document these use cases as a first step in better integrating AI into food systems research.