Abstract This article examines generative AI models as sociocultural actors, focusing on how they reproduce and constrain notions of authorship and identity within the contemporary U.S. literary field. Through a simulation of 101 “AI authors” modeled on real-world counterparts, we analyze how language models articulate literary distinction across race, gender, and publishing contexts. Our findings show that generative outputs are dominated by a reductive black–white binary that both flattens intra-group diversity and marginalizes alternative axes of distinction. Comparative analysis with human-authored works highlights the homogenizing tendencies of AI systems, suggesting that the model operationalizes identity as a limited categorical variable rather than as a dynamic cultural practice. Prompt-based interventions reduce racial fixation but often efface race altogether, raising critical questions about the limits of technical fixes for bias. We argue that these dynamics illuminate how AI systems participate in cultural production by encoding and reshaping social categories of difference. This article contributes to debates on AI agency, algorithmic bias, and cultural equity, and points toward the need for design practices attentive to identity, representation, and creative diversity.
Roland et al. (2025) studied this question.