Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models

ArXi:2604.06213v1 Announce Type: new Large Language Models (LLMs) excel at human-like language generation but often embed and amplify implicit, intersectional biases, especially under persona-driven contexts. Existing bias audits rely on static, embedding-based tests (CEAT, I-WEAT, I-SEAT) that quantify absolute association strengths. We show that they have limitations in capturing dynamic shifts when models adopt social roles. We address this gap by