With the wide and cross-domain adoption of Large Language Models, it becomes crucial to assess to which extent the statistical correlations in training data, which underlie their impressive performance, hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of works, hobbies, and emotions typically associated with a specific gender. In this study, we introduce a novel perspective. We investigate whether LLMs can predict an individual's gender based solely on online shopping histories and whether these predictions are influenced by gender biases and stereotypes. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender and we then analyze their reasoning and products-gender co-occurrences. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender. Furthermore, explicit instructions to avoid bias reduce the certainty of model predictions, but do not eliminate stereotypical patterns. Our findings highlight the persistent nature of gender biases in LLMs and emphasize the need for robust bias-mitigation strategies.

翻译：随着大语言模型在各领域的广泛采用，评估其训练数据中统计关联所隐藏的微妙且可能有害的偏见变得至关重要。这些统计关联正是模型卓越性能的基础。大语言模型中的性别偏见已从职业、爱好和情感等通常与特定性别相关的角度得到广泛研究。本研究引入了一个新颖的视角：我们探究大语言模型能否仅基于在线购物历史预测个体性别，以及这些预测是否受到性别偏见和刻板印象的影响。利用美国用户的历史在线购物数据集，我们评估了六种大语言模型的性别分类能力，并分析了其推理过程及产品与性别的共现关系。结果表明，尽管模型能以中等准确度推断性别，但其决策往往植根于产品类别与性别之间的刻板关联。此外，明确指示模型避免偏见会降低其预测的确定性，但并未消除刻板模式。我们的发现凸显了大语言模型中性别偏见的顽固性，并强调了制定强健的偏见缓解策略的必要性。