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arXiv · 2608.12359

Can We Trust AI Agents in the Supermarket? Sugar Content Inference from Product Images

Jose Berengueres
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Abstract

Nutritional labels are legally permitted to appear in very small print, reducing real-world readability and encouraging consumers to rely on 'AI nutrition lens' and vision-capable conversational agents for dietary guidance. We evaluate whether such AI-mediated advice can meaningfully substitute for regulated labeling using a bounded, verifiable task: inferring which of two packaged foods contains less sugar from front-of-pack images alone. A Two-Alternative Forced Choice game was used to evaluate AI agent systems across four national supermarket contexts: Sweden, the USA, Australia, and Kazakhstan. The results (N=132 comparisons) across both agents reveal a significant performance divide contingent on context. For global products the agents achieved 88.9% accuracy (p < 0.0001 against chance). For local products (Sweden), accuracy dropped to 59.5% (p = 0.29), rendering the AI's guidance statistically indistinguishable from random guessing. These findings indicate a cross-market bias consistent with uneven training-data coverage, raising concerns about trust, equity, and accountability, particularly when nutritional judgment shifts from auditable public labels to proprietary inference pipelines. We conclude that AI nutrition lens applications are better framed as assistive, educational tools rather than as replacements for regulated labels, and we highlight the need for auditable datasets and evaluation benchmarks aligned with local food ecosystems.

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Jose Berengueres. 2026-07-04. Can We Trust AI Agents in the Supermarket? Sugar Content Inference from Product Images. https://arxiv.org/abs/2608.12359

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