Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios

• URL: http://arxiv.org/abs/2503.01532v2
• Date: Tue, 22 Apr 2025 07:22:45 GMT
• Title: Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios
• Authors: Bryan Chen Zhengyu Tan, Roy Ka-Wei Lee,
• Abstract summary: We introduce a novel framework using cosine distance to measure semantic shifts in responses.<n>We assess how demographic prompts affect response quality across power-disparate social scenarios.<n>Our findings suggest a "default persona" bias toward middle-aged, able-bodied, native-born, Caucasian, atheistic males with centrist views.
• Score: 4.626073646852022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in simulating human behaviour and social intelligence. However, they risk perpetuating societal biases, especially when demographic information is involved. We introduce a novel framework using cosine distance to measure semantic shifts in responses and an LLM-judged Preference Win Rate (WR) to assess how demographic prompts affect response quality across power-disparate social scenarios. Evaluating five LLMs over 100 diverse social scenarios and nine demographic axes, our findings suggest a "default persona" bias toward middle-aged, able-bodied, native-born, Caucasian, atheistic males with centrist views. Moreover, interactions involving specific demographics are associated with lower-quality responses. Lastly, the presence of power disparities increases variability in response semantics and quality across demographic groups, suggesting that implicit biases may be heightened under power-imbalanced conditions. These insights expose the demographic biases inherent in LLMs and offer potential paths toward future bias mitigation efforts in LLMs.

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