Policy-Conditioned AI-Use Detection: An Evidentiary Framework for Academic Publishing

The proliferation of AI in academic publishing necessitates a re-evaluation of current AI detection methods. Existing tools primarily identify AI-generated text, which often misaligns with specific policy requirements for authors, reviewers, and editors regarding AI use. A new framework, 'policy-conditioned AI-use detection,' is proposed to assess compliance with stated rules by integrating policy specifics directly into the detection process and providing evidentiary reports rather than simple verdicts.

The rapid advancement and integration of AI tools across various professional domains necessitate robust and context-aware frameworks for governance. In sectors reliant on content generation and review, such as academic publishing, ensuring integrity and compliance with evolving AI use policies is critical to maintaining credibility and operational standards. This approach informs how organizations might develop more nuanced enforcement mechanisms for AI-related policies.

Academic venues have established diverse and detailed rules for AI use by authors, reviewers, and area chairs, varying by role, task, and disclosure requirements.

Forward consideration, not a verified fact.