ARTIFICIAL INTELLIGENCE AS A DECISION PARTNER IN FOOD CHOICE: TYPOLOGY AND RESEARCH AGENDA

Research examining artificial intelligence's (AI) role in food choice largely asks whether AI helps people make better decisions, treating it as an information source that improves the accuracy or personalization of recommendations. We suggest that this framing captures one point on a broader continuum of how AI impacts food decisions. AI can act, often simultaneously, as an advisor consulted for information, a decision partner that actively shares in constructing the choice, or an ambient influence that shapes what appears desirable and available before deliberate choice begins. As AI becomes more conversational and more embedded across the food environment, this continuum is increasingly populated in less visible regions, yet the field remains concentrated on the advisor role. We focus on the decision-partner role, where AI participates in value construction, goal clarification, attribute weighting, and choice justification, raising questions about agency, responsibility, and delegation. We articulate four mechanisms that describe this participation, namely preference offloading, responsibility reallocation, confidence calibration, and food-decision skill erosion. Because food choice is frequent, often habitual, and commonly involves trade-offs among hedonic and utilitarian goals, it is an ideal domain in which these process-level effects can compound. We draw on evidence from consumer psychology, automation research, and cognitive science, noting where boundary conditions remain untested in food contexts, and close with a research agenda organized around agency, reliance, responsibility, confidence, and cognitive effort. When AI shares in constructing a food choice, the values being optimized may no longer be entirely the consumer's own.