Empirical Study and Mitigation Methods of Bias in LLM-Based Robots

Abstract

Our study provides a comprehensive analysis of biased behaviors exhibited by robots utilizing large language models (LLMs) in real-world applications, focusing on five experimental scenarios: customer service, education, healthcare, recruitment, and social interaction. The analysis reveals significant differences in user experiences based on race, health status, work experience, and social status. For instance, the average satisfaction score for white customers is 4.2, compared to 3.5 for black customers, and the response accuracy for white students is 92%, versus 85% for black students. To address these biases, we propose several mitigation methods, including data resampling, model regularization, post-processing techniques, diversity assessment, and user feedback mechanisms. These methods aim to enhance the fairness and inclusivity of robotic systems, promoting healthy human-robot interactions. By combining our quantitative data analysis with existing research, we affirm the importance of bias detection and mitigation, and propose various improvement strategies. Future research should further explore data balancing strategies, fairness-constrained models, real-time monitoring and adjustment mechanisms, and cross-domain studies to comprehensively evaluate and improve the performance of LLM-based robotic systems across various tasks.