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The remarkable achievements of Large Language Models (LLMs) have led to theemergence of a novel recommendation paradigm -- Recommendation via LLM(RecLLM). Nevertheless, it is important to note that LLMs may contain socialprejudices, and therefore, the fairness of recommendations made by RecLLMrequires further investigation. To avoid the potential risks of RecLLM, it isimperative to evaluate the fairness of RecLLM with respect to various sensitiveattributes on the user side. Due to the differences between the RecLLM paradigmand the traditional recommendation paradigm, it is problematic to directly usethe fairness benchmark of traditional recommendation. To address the dilemma,we propose a novel benchmark called Fairness of Recommendation via LLM(FaiRLLM). This benchmark comprises carefully crafted metrics and a datasetthat accounts for eight sensitive attributes1 in two recommendation scenarios:music and movies. By utilizing our FaiRLLM benchmark, we conducted anevaluation of ChatGPT and discovered that it still exhibits unfairness to somesensitive attributes when generating recommendations. Our code and dataset canbe found at https://github.com/jizhi-zhang/FaiRLLM.

Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation