  This paper presents a framework for evaluating fairness in recommender
systems powered by Large Language Models (RecLLMs), addressing the need for a
unified approach that spans various fairness dimensions including sensitivity
to user attributes, intrinsic fairness, and discussions of fairness based on
underlying benefits. In addition, our framework introduces counterfactual
evaluations and integrates diverse user group considerations to enhance the
discourse on fairness evaluation for RecLLMs.
  Our key contributions include the development of a robust framework for
fairness evaluation in LLM-based recommendations and a structured method to
create \textit{informative user profiles} from demographic data, historical
user preferences, and recent interactions. We argue that the latter is
essential for enhancing personalization in such systems, especially in
temporal-driven scenarios. We demonstrate the utility of our framework through
practical applications on two datasets, LastFM-1K and ML-1M. We conduct
experiments on a subsample of 80 users from each dataset, testing and assessing
the effectiveness of various prompt construction scenarios and in-context
learning, comprising more than 50 scenarios. This results in more than 4000
recommendations (80 * 50 = 4000). Our study reveals that while there are no
significant unfairness issues in scenarios involving sensitive attributes, some
concerns remain. However, in terms of intrinsic fairness, which does not
involve direct sensitivity, unfairness across demographic groups remains
significant. The code and data used for this paper are available at:
\url{https://shorturl.at/awBFM}.
