Abstract
There has been a proliferation of artificial intelligence (AI) systems that provide classifications of people (i.e., feedback) based on personal attributes such as personality, looks, and qualifications (Ryan-Mosley, 2021). We refer to AI applications that provide feedback on personal attributes as judgy AI. A commercial application of such systems, used by cosmetic companies, is to assess for skin conditions such as redness, pores, and wrinkles and use the feedback to suggest purchases (Eriksson & Kenalemang, 2023). Many of these systems use AI to assess images people upload, classifying their appearance based on models trained on images of others. The AI literature has examined how people react to algorithms, and in doing so, has found that people can be both averse to and appreciative of them (Logg et al., 2019; Turel & Kalhan, 2023), which suggests that context (e.g., how the AI is used) may play a role in people’s feelings toward them. Our interest, in this study, is in examining how people react to personal judgments from an algorithm, and specifically, whether those reactions influence their purchase intentions. Such findings are useful in understanding not only user reactions to algorithmic feedback but also how such feedback may drive consumption. Drawing on expectation-confirmation theory, we tested a model exploring how confirmatory feedback drives perceptions of usefulness of the algorithm, and how both directly, and indirectly through satisfaction and trust of the algorithm, these perceptions influence purchase intentions. Participants used a classification algorithm that provided feedback on their appearance, and our preliminary findings illustrate how such algorithmic feedback may ultimately influence purchasing behaviors. This study contributes to the AI literature by examining how people react to judgmental feedback from AI systems and how these reactions may influence their consumption behaviors. For practice, findings from this study speak to the effectiveness of using such systems as sales tools.
Recommended Citation
Chaput, Amy Christine; Clark, Autumn; Bullock, Taylor; and James, Tabitha, "Judgy AI: I Trust It If It Tells Me What I Want to Hear" (2026). AMCIS 2026 TREOs. 163.
https://aisel.aisnet.org/treos_amcis2026/163