  Recent advances in Foundation Models such as Large Language Models (LLMs)
have propelled them to the forefront of Recommender Systems (RS). Despite their
utility, there is a growing concern that LLMs might inadvertently perpetuate
societal stereotypes, resulting in unfair recommendations. Since fairness is
critical for RS as many users take it for decision-making and demand
fulfillment, this paper focuses on user-side fairness for LLM-based
recommendation where the users may require a recommender system to be fair on
specific sensitive features such as gender or age. In this paper, we dive into
the extent of unfairness exhibited by LLM-based recommender models based on
both T5 and LLaMA backbones, and discuss appropriate methods for promoting
equitable treatment of users in LLM-based recommendation models. We introduce a
novel Counterfactually-Fair-Prompt (CFP) method towards Unbiased Foundation
mOdels (UFO) for fairness-aware LLM-based recommendation. Experiments are
conducted on two real-world datasets, MovieLens-1M and Insurance, and compared
with both matching-based and sequential-based fairness-aware recommendation
models. Results show that CFP achieves better recommendation performance with a
high level of fairness. Data and code are open-sourced at
https://github.com/agiresearch/UP5.
