检测大模型推荐中性别种族偏见，发现明显差异
Gender and Race Bias in Consumer Product Recommendations by Large Language Models
- 用提示工程诱导模型对不同群体推荐商品
- 三种方法均发现男女、不同种族推荐差异显著
- 提醒开发者关注推荐系统公平性
大型语言模型在生成消费者产品推荐中的应用日益广泛，但其可能隐含并放大性别与种族偏见的问题尚未得到充分研究。本文是首批系统考察此类偏见的研究之一。我们采用提示工程引导模型为不同性别和种族群体生成产品推荐，并运用标记词分析、支持向量机及詹森-香农散度三种方法识别与量化偏差。结果表明，针对不同人口群体的推荐存在显著差异，凸显出构建更公平的LLM推荐系统的重要性。
原文摘要 · Abstract (English)
Large Language Models are increasingly employed in generating consumer product recommendations, yet their potential for embedding and amplifying gender and race biases remains underexplored. This paper serves as one of the first attempts to examine these biases within LLM-generated recommendations. We leverage prompt engineering to elicit product suggestions from LLMs for various race and gender groups and employ three analytical methods-Marked Words, Support Vector Machines, and Jensen-Shannon Divergence-to identify and quantify biases. Our findings reveal significant disparities in the recommendations for demographic groups, underscoring the need for more equitable LLM recommendation systems.
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