Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities
This addresses fairness issues in reputation-based ranking systems for users, though it is incremental as it builds on existing work on exposure disparities.
The paper tackles the problem of disparate reputation in ranking systems, where users' sensitive attributes like gender and age systematically affect their reputation and thus the final ranking, and proposes a mitigation approach that ensures reputation independence and improves ranking effectiveness on real-world data.
Recent literature on ranking systems (RS) has considered users' exposure when they are the object of the ranking. Although items are the object of reputation-based RS, users have a central role also in this class of algorithms. Indeed, when ranking the items, user preferences are weighted by how relevant this user is in the platform (i.e., their reputation). In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences. We consider two demographic attributes, i.e., gender and age, and show that DR systematically occurs. Then, we propose mitigation, which ensures that reputation is independent of the users' sensitive attributes. Experiments on real-world data show that our approach can overcome DR and also improve ranking effectiveness.