CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System

In the evolving landscape of recommender systems, the integration of Large Language Models (LLMs) such as ChatGPT marks a new era, introducing the concept of Recommendation via LLM (RecLLM). While these advancements promise unprecedented personalization and efficiency, they also bring to the fore critical concerns regarding fairness, particularly in how recommendations might inadvertently perpetuate or amplify biases associated with sensitive user attributes. In order to address these concerns, our study introduces a comprehensive evaluation framework, CFaiRLLM, aimed at evaluating (and thereby mitigating) biases on the consumer side within RecLLMs. Our research methodically assesses the fairness of RecLLMs by examining how recommendations might vary with the inclusion of sensitive attributes such as gender, age, and their intersections, through both similarity alignment and true preference alignment. By analyzing recommendations generated under different conditions-including the use of sensitive attributes in user prompts-our framework identifies potential biases in the recommendations provided. A key part of our study involves exploring how different detailed strategies for constructing user profiles (random, top-rated, recent) impact the alignment between recommendations made without consideration of sensitive attributes and those that are sensitive-attribute-aware, highlighting the bias mechanisms within RecLLMs. The findings in our study highlight notable disparities in the fairness of recommendations, particularly when sensitive attributes are integrated into the recommendation process, either individually or in combination. The analysis demonstrates that the choice of user profile sampling strategy plays a significant role in affecting fairness outcomes, highlighting the complexity of achieving fair recommendations in the era of LLMs.