Abstract
Food waste remains a critical global challenge with significant environmental, economic, and social implications. While much of the existing research has focused on supply chain inefficiencies and institutional waste, consumer-level food waste remains underexplored. This paper investigates the potential of artificial intelligence (AI), particularly large language models and computer vision, to address food waste at the consumer level. Building on prior work on consumer attitudes and behaviors, we identify behavioral drivers of food waste and develop targeted intervention strategies. These strategies may include personalized nudges, awareness campaigns, or digital tools that promote sustainable consumption habits. The paper outlines a preliminary framework for integrating AI-driven analysis with experimental testing, using real-world settings such as university cafeterias or online platforms. This interdisciplinary approach bridges marketing, sustainability, and AI, offering a scalable and data-driven pathway to reduce food waste through consumer engagement and behavior change.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
Economic, Social, and Cultural Context
KEYWORDS
Food Waste, Artificial Intelligence, Intervention
