Detecting Collusion in Peer Review: Drawing Inspiration from VCG Principle

Abstract

The peer-review process, the bedrock of scientific advancement, is increasingly undermined by sophisticated collusion rings that systematically manipulate review outcomes to favor in-group members. While existing detection methods struggle to untangle obfuscated social ties in explicit co-authorship graphs, we introduce a new direction: Exclusion Based Anomaly Detection. Similar to the way VCG auctions work, we formally measure the marginal influence of suspected reviewer groups, exposing their signature even when explicit social graphs are hidden. To apply this at scale without prior knowledge of colluding groups, we introduce the Embedding Based Discovery Framework, which leverages continuous semantic embeddings to isolate latent collusive communities directly from their semantic profile, bypassing the adversarial limitations of explicit network analysis. Unlike traditional heuristic-based approaches, our framework functions as an automated auditor, requiring no prior knowledge of group membership. It achieves this by executing a decoupled search across independent diagnostic algorithms and combining their findings into distinct consensus formations, allowing organizers to dynamically balance detection precision and recall. Evaluating our technique with large-scale datasets (based on ICLR 2021) shows our method's capacity to identify both overt and subtle adversarial tactics with high sensitivity and strict Family-Wise Error Rate (FWER) control, effectively providing conference organizers with a scalable, robust, and privacy-preserving tool to secure the scientific integrity of academic publishing.