Robust Reputation Independence in Ranking Systems for Multiple Sensitive Attributes

Abstract

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.

A reputation assessment model for trustful service recommendation

“…Ramos and Boratto in [43] divide users into classes based on the demographic attributes that define them and introduce the concept of disparate reputation (DR), capturing if users belonging to different classes are given systematically lower/higher reputation values. The authors propose an algorithm that ensures that reputation is independent of users sensitive attributes, and additionally they propose a step to introduce reputation independence that may be included in any ranking system which computes rankings as a weighted average of ratings.…”