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When Language Becomes Targeting: How Gender Cues Shape AI Recommendations

When Language Becomes Targeting: How Gender Cues Shape AI Recommendations

  • ARF

As generative AI tools increasingly influence product discovery and decision-making, subtle cues in user language can shape what consumers are shown—and how options are framed. This research examines how implicit and explicit gender signals affect AI-generated product recommendations, revealing systematic differences in categories, brand repetition, descriptive language and price information. The findings raise important questions for advertisers and researchers about bias, brand visibility and the growing cultural role of AI in shaping consumer norms.

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When Style Becomes Signal: How Gendered Language Shapes Generative AI Output

When Style Becomes Signal: How Gendered Language Shapes Generative AI Output

  • ARF

As generative AI tools become embedded in advertising and marketing research workflows, questions about bias increasingly extend beyond outputs to the interaction itself. This study examines whether gendered patterns can enter AI through subtle differences in how prompts are phrased. By systematically varying linguistic styles using psychologically grounded traits, the research shows that implicit, style-based, gender cues shape AI prompt construction more strongly than explicit, gender labels, with important implications for how bias may propagate upstream in AI-assisted marketing and research applications.

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