Abstract Summary
Modern ranking systems must comply with emerging AI regulations while resisting manipulation attacks. The EU AI Act's prohibition on individual user scoring creates a critical gap: reputation-based systems often violate compliance requirements, while user-agnostic approaches lack resistance to bribery, and both approaches still remain vulnerable to demographic bias. We propose a user-agnostic multipartite ranking framework addressing regulatory compliance, security, personalization and bias. Our approach clusters users based on rating patterns and applies localized statistical filtering within clusters to remove anomalous ratings, thereby eliminating individual profiling while preserving personalization and enhancing manipulation resistance. Evaluation across three datasets shows substantial bribery resistance improvements, with profitable attacks in only 7 of 18 scenarios versus 8--11 for state-of-the-art baselines. The framework also achieves demographic bias values reduced by a factor of 100 compared to a user-agnostic bipartite approach. These results are achieved while designed to avoid individual user scoring as prohibited by the EU AI Act. Robustness analysis reveals enhanced spam resistance on two datasets, with computational overhead as the primary trade-off.
