Explore how language models perpetuate intersectional biases across three critical life domains in this 15-minute conference talk from the Association for Computing Machinery. Examine research findings that reveal how AI systems generate biased narratives related to learning, labor, and love, with particular attention to how these biases compound when multiple identity factors intersect. Discover the methodology used to identify these patterns and understand the implications for AI fairness and representation. Learn about the challenges of addressing intersectional bias in natural language generation systems and consider the broader societal impacts of biased AI-generated content across educational, professional, and personal contexts.