谁的旅程重要？调查用于旅行规划协助的大型语言模型（LLM）中的身份偏见
Whose Journey Matters? Investigating Identity Biases in Large Language Models (LLMs) for Travel Planning Assistance
AI总结：
本文基于社会认同与社会技术系统理论，用公平性探测分析三款开源LLM的旅行推荐，发现其存在种族、性别刻板印象偏见，且少数群体推荐更易出现幻觉。
摘要：
随着大型语言模型（LLM）日益成为酒店业和旅游业不可或缺的一部分，人们对其在服务不同身份群体时的公平性仍存担忧。本研究以社会认同理论和社会技术系统理论为基础，考察LLM生成的旅行推荐中的种族和性别偏见。我们使用公平性探测，分析了三个领先开源LLM的输出。结果显示，种族和性别分类器的测试准确率均超过随机概率。对最具影响力特征的分析揭示，LLM生成的推荐中存在刻板印象偏见。我们还在这些特征中发现了幻觉，且在面向少数群体的推荐中出现得更频繁。这些发现表明，LLM在充当旅行规划助手时表现出种族和性别偏见。本研究强调，需要采取偏见缓解策略，以提升生成式AI驱动的旅行规划协助的包容性和可靠性。
英文摘要：
As large language models (LLMs) become increasingly integral to the hospitality and tourism industry, concerns about their fairness in serving diverse identity groups persist. Grounded in social identity theory and sociotechnical systems theory, this study examines ethnic and gender biases in travel recommendations generated by LLMs. Using fairness probing, we analyze outputs from three leading open-source LLMs. The results show that test accuracy for both ethnicity and gender classifiers exceed random chance. Analysis of the most influential features reveals the presence of stereotype bias in LLM-generated recommendations. We also found hallucinations among these features, occurring more frequently in recommendations for minority groups. These findings indicate that LLMs exhibit ethnic and gender bias when functioning as travel planning assistants. This study underscores the need for bias mitigation strategies to improve the inclusivity and reliability of generative AI-driven travel planning assistance.
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