  Recently, Large Language Models (LLMs) have enhanced user interaction,
enabling seamless information retrieval and recommendations. However, concerns
emerge as these LLMs have shown tendencies to display discrimination related to
users' sensitive characteristics (such as gender), leading to explicit user
unfairness. Furthermore, our analysis uncovers a more discreet variant of bias
in LLMs, defined as implicit user unfairness, wherein these models demonstrate
discriminatory recommendation behaviors based solely on non-sensitive user
details, like usernames or email addresses. This subtle form of unfairness,
while more pervasive, poses a significant threat to the ethical integrity and
rights of minority user groups. To comprehensively explore implicit user
unfairness, our analysis unfolds in three key steps: (1) We uncover the reasons
for this implicit user unfairness: LLMs can infer users' sensitive attributes
from non-sensitive attributes (e.g. user names) due to their extensive world
knowledge. (2) Our findings expose that the magnitude of implicit user
unfairness within LLMs surpasses the level of explicit user unfairness observed
in traditional recommender models, signifying a more alarming issue of
unfairness, i.e. some non-sensitive features of users like names may result in
more serious discrimination phenomena. (3) We analyze the long-term effect of
implicit user unfairness, identifying that it will reinforce information
bubbles at an accelerated rate compared to traditional RS. We emphasize the
need to identify and mitigate implicit user unfairness, aiming to avert the
potential human-LLMs recommendation systems deterioration.
