[2604.06213] Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models

Computer Science > Computation and Language arXiv:2604.06213 (cs) [Submitted on 16 Mar 2026] Title:Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models Authors:Nandini Arimanda, Achyuth Mukund, Sakthi Balan Muthiah, Rajesh Sharma View a PDF of the paper titled Invisible Influences: Investigating Implicit Intersectional Biases through Persona Engineering in Large Language Models, by Nandini Arimanda and 3 other authors View PDF HTML (experimental) Abstract: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 introducing the Bias Amplification Differential and Explainability Score (BADx): a novel, scalable metric that measures persona-induced bias amplification and integrates local explainability insights. BADx comprises three components - differential bias scores (BAD, based on CEAT, I-WEAT, I-SEAT),Persona Sensitivity Index (PSI), and Volatility (Standard Deviation), augmented by LIME-based analysis for emphasizing explainability. This study is divided and performed as two different tasks. Task 1 establishes static bias baselines, and Task 2 applies...