A Preliminary Investigation Into the Use of <scp>AI</scp>‐Generated Food Images in a Survey Asking About Consumer Perception of Appeal, Naturalness, Healthiness, and Willingness to Consume
Bibliographic record
Abstract
ABSTRACT Food images generated using artificial intelligence (AI) are becoming more common in research, and in the everyday world. The objective of this study was to identify how consumers' perception of a food image (AI‐generated or a genuine image), influenced their perception and emotional response to the food. Participants ( n = 154) were asked to look at ten different images (five were AI‐generated and five were genuine (referred to as standard images)) of food items common to those living in Atlantic Canada. The participants were asked to evaluate their willingness to consume, the healthiness, the naturalness, the appeal, and their perception of AI use for each image. The study also assessed their emotional response to the images. The results found the participants were able to identify when an image was created using an AI generator. The participants' perception of AI was negatively correlated to participants' willingness to consume the food product, as well as their perception of the healthiness, naturalness, and appeal of the product. Furthermore, the participants' emotional response was different when evaluating AI generated images compared to standard images. The results highlight the use of AI‐generated images in surveys can influence the participants perception, but this topic needs to be further explored in future studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".