The AI Error Trap: Editorial Discretion, Machine Hallucination, and the Future of Research Integrity

This paper investigates the epistemic and ethical risks posed by the integration of generative AI into academic publishing. It focuses on the growing prevalence of hallucinated citations—syntactically correct but fabricated references—produced by large language models like GPT-4. Drawing on a detailed first-person case study of a lifetime journal ban triggered by such hallucinations, the paper critiques the lack of proportionality, transparency, and due process in editorial sanctioning practices. We propose a three-part governance framework: (1) progressive editorial sanctions for AI-induced errors, (2) machine-verification protocols for citation integrity, and (3) a due process model for academic publishing in the age of algorithmic authorship. Based on empirical tests of hallucination rates in obscure legal domains (e.g., Sámi land rights), we find up to 70% of citations generated by GPT-4o were unverifiable. These results underscore the urgent need for reformed editorial policies that distinguish between human fraud and machine-induced friction. This work contributes to debates on research integrity, AI governance, and the evolving norms of scholarly publishing.