Personalizing AI Art Boosts Credit, Not Beauty

While artificial intelligence increasingly democratises art creation, people tend to devalue AI-generated content—a phenomenon known as algorithm aversion. Recent work suggests that personalized AI models, trained on a user's past work, can increase credit attribution in text generation. We investigated whether this effect extends to visual art and examined the relationship between credit attribution and aesthetic appreciation. Across two studies (N=774), UK participants evaluated identical paintings that were described as being created either by hand, with a standard text-to-image generative AI system, or with an AI system personalized to the artist. While personalization significantly improved credit attribution and perceived authorship and commercial rights compared to standard AI use, it failed to enhance either aesthetic appreciation or willingness to categorise the outputs as "true art"—revealing a striking disconnect between judgments of artistic contribution and artistic value. Our findings suggest that although personalized AI may help bridge the "achievement gap" in credit attribution, it cannot overcome fundamental barriers to aesthetic appreciation of AI art. This challenges assumptions about the relationship between perceived effort and aesthetic value, with implications for understanding art categorization and human-AI cooperation in creative pursuits.