Artificial intelligence, body image comparison, and perceived aesthetic bias: a correlational study of weight-based digital exclusion

This study examines how perceptions of aesthetic discrimination based on body weight are associated with self-reported experiences in digital environments mediated by artificial intelligence (AI). Drawing on social comparison and objectification theories and on the sociotechnical literature on algorithmic bias, the study adopts a cross-sectional correlational design with a non-probability sample of 284 adult social media users in Ecuador. The analysis addresses associations among perceived algorithmic aesthetic intervention, body comparison with AI-generated representations, and self-reported feelings of body-related exclusion. Perceived AI-based photographic enhancement was positively associated with feelings of body-related exclusion (ρ = 0.24, p < .001), and comparison of one’s own body image with AI-generated representations was positively associated with the perception of automated aesthetic enhancement (ρ = 0.32, p < .001). Participants who compared themselves with AI-generated images, and those reporting greater exclusion, were also the most likely to identify these systems as generating predominantly thin or athletic bodies (ρ = −0.20, p < .001, and ρ = −0.17, p = .005, on a scale whose lowest code is that response). Taken together, the results indicate that participants who perceive AI as intervening aesthetically in bodily images also report greater engagement in body comparison and stronger feelings of exclusion, and that those most engaged with AI-generated representations are also the most likely to recognise the narrowness of the bodies these systems produce. The findings are interpreted as perceptual evidence relevant to debates on algorithmic aesthetic standardisation, and they indicate the need for audit-based, longitudinal and instrument-validated designs.