FairRARI: A Plug and Play Framework for Fairness-Aware PageRank
Emmanouil Kariotakis, Aritra Konar

TL;DR
FairRARI is a flexible convex optimization framework that computes fair PageRank vectors satisfying various group fairness criteria, ensuring fairness without sacrificing computational efficiency.
Contribution
It introduces a unified, in-processing convex optimization approach for fairness-aware PageRank, with guarantees on fairness levels and computational efficiency.
Findings
Outperforms existing methods in utility and fairness
Achieves fairness with the same asymptotic complexity as original PageRank
Effectively handles multiple group fairness criteria
Abstract
PageRank (PR) is a fundamental algorithm in graph machine learning tasks. Owing to the increasing importance of algorithmic fairness, we consider the problem of computing PR vectors subject to various group-fairness criteria based on sensitive attributes of the vertices. At present, principled algorithms for this problem are lacking - some cannot guarantee that a target fairness level is achieved, while others do not feature optimality guarantees. In order to overcome these shortcomings, we put forth a unified in-processing convex optimization framework, termed FairRARI, for tackling different group-fairness criteria in a ``plug and play'' fashion. Leveraging a variational formulation of PR, the framework computes fair PR vectors by solving a strongly convex optimization problem with fairness constraints, thereby ensuring that a target fairness level is achieved. We further introduce…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Graph Neural Networks · Ethics and Social Impacts of AI · Mobile Crowdsensing and Crowdsourcing
