Artificial Intelligence Approximates Human Affect Ratings of Cannabis Images

Cannabis imagery is proliferating online and can elicit affective responses related to use. Scalable tools are needed to evaluate how this proliferation could influence population health. This pilot study tested whether multimodal generative artificial intelligence (MGAI) can reproduce subjective human affect ratings of cannabis images. Four MGAI agents (model: gpt-4o-2024-11-20) were created to parallel the four human participant subgroups from Macatee et al. 2021, defined by primary method of cannabis administration (bong, bowl, joint/blunt, vaporizer). Using Macatee et al.'s participant instructions and standardized image set, each agent rated images of its primary method of administration on valence, arousal, and urge constructs. For each image-construct pair, n=100 ratings were generated in separate conversational threads using zero-shot prompting. Image-level MGAI mean ratings were compared with human mean ratings using Two One-Sided Tests of equivalence and Spearman correlations. Although formal statistical equivalence was rare (4% valence, 11% arousal, 3% urge), MGAI ratings approximated human ratings closely (Mean difference of mean ratings = - 0.31, SD = 1.23) and correlations between MGAI and human mean ratings were moderate to high: r_s(valence) = 0.55, r_s(arousal) = 0.34, r_s(urge) = 0.56. MGAI also reproduced the parabolic relation between rating means and standard deviations observed in human data. These preliminary results indicate that MGAI can approximate human cannabis cue-reactivity patterns closely enough to justify continued refinement. MGAI could potentially be developed into a Cannabis Regulatory Science tool to aid regulatory oversight of online cannabis marketing.