AI and Inclusive Marketing: Detecting and Reducing Stereotypes in Algorithmically Generated Creative Content

Abstract

This article examines how AI generated creative content interacts with inclusive marketing goals, asking whether and how generative systems encode, detect, and reduce stereotypes in brand communications. Positioned within debates on diversity in advertising and algorithmic bias, the study conducts a systematic literature review of peer reviewed work published between 2019 and 2024 on text to image and other creative AI tools used in marketing contexts. The synthesis shows that common generative models tend to overrepresent majority identities in high status roles, under represent marginalized groups, and reproduce traditional gendered and racialized associations, with these patterns flowing into campaigns that rely on AI assets. Audience research further indicates that AI based diversity initiatives can be perceived as inauthentic or tokenistic, dampening trust and brand attitudes. By integrating technical audits, consumer studies and inclusive branding research, the article highlights key methodological gaps and outlines research and governance priorities for designing creative AI that supports more equitable representation.