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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it