AI-Based Text Analysis for Evaluating Food Waste Policies

John Aitken (The MITRE Corporation), Denali Rao (The MITRE Corporation), Balca Alaybek (The MITRE Corporation), Amber Sprenger (The MITRE Corporation), Grace Mika (The MITRE Corporation), Rob Hartman (The MITRE Corporation) and Laura Leets (The MITRE Corporation)

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Natural Language Processing Agriculture & Food


Food waste is a major contributor to climate change, making the reduction of food waste one of the most important strategies to preserve threatened ecosystems and increase economic benefits. To evaluate the impact of food waste policies in this arena and provide actionable guidance to policymakers, we conducted an AI-based text analysis of food waste policy provisions. Specifically, we a) identified commonalities across state policy texts, b) clustered states by shared policy text, and c) examined relationships between state cluster memberships and food waste . This approach generated state clusters but demonstrated very limited convergent validity with policy ratings provided by subject matter experts and no predictive validity with food waste. We discuss the potential of using supervised machine learning to analyze food waste policy text as a next step.