  Ranking systems have an unprecedented influence on how and what information
people access, and their impact on our society is being analyzed from different
perspectives, such as users' discrimination. A notable example is represented
by reputation-based ranking systems, a class of systems that rely on users'
reputation to generate a non-personalized item-ranking, proved to be biased
against certain demographic classes. To safeguard that a given sensitive user's
attribute does not systematically affect the reputation of that user, prior
work has operationalized a reputation independence constraint on this class of
systems. In this paper, we uncover that guaranteeing reputation independence
for a single sensitive attribute is not enough. When mitigating biases based on
one sensitive attribute (e.g., gender), the final ranking might still be biased
against certain demographic groups formed based on another attribute (e.g.,
age). Hence, we propose a novel approach to introduce reputation independence
for multiple sensitive attributes simultaneously. We then analyze the extent to
which our approach impacts on discrimination and other important properties of
the ranking system, such as its quality and robustness against attacks.
Experiments on two real-world datasets show that our approach leads to less
biased rankings with respect to multiple users' sensitive attributes, without
affecting the system's quality and robustness.
