Identity Theft in AI Conference Peer Review (cacm.acm.org)

Recent investigations have unveiled alarming instances of identity theft within the peer-review process of AI-related scientific conferences. These cases reveal how dishonest researchers manipulate the system by creating fake identities—often using the credentials of legitimate scholars—to secure favorable reviews for their own papers. This exploit is facilitated by the reliance on online self-nomination forms, where researchers provide details like affiliations and publication histories that are not robustly verified, leading to significant vulnerabilities. The implications for the AI/ML community are profound, as the integrity of the peer-review process is critical for maintaining scientific standards. With AI conferences facing an unprecedented influx of submissions—accepting only 15% to 25% of papers—there is an urgent need for improved identity verification methods. Recommended measures include linking reviewer identities to verified past publications and instituting stronger vetting protocols, such as using institutional email addresses and integrating more reliable digital identifiers like ORCID. By addressing these gaps, the academic community can better protect against fraud and preserve trust in the peer-review system, ultimately ensuring the integrity of AI research.