The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data

With the wide and cross-domain adoption of Large Language Models (LLMs), it becomes crucial to assess the extent to which the statistical correlations in training data hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of work, 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 five LLMs to predict the buyer’s gender, and we analyze their reasoning and product–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 that differ from the actual behaviors of the individuals present in the dataset. 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 as LLMs are currently amplifying the stereotypes analyzed.