arXiv:2605.21713v1 Announce Type: new Abstract: How can we distinguish whether a peer review was written by a human or generated by an AI model? We argue that, in this setting, authorship should not be attributed solely from the textual features of a review, but also from the ideas, judgments, and claims it expresses. To this end, we propose Sem-Detect, an authorship detection method for peer reviews that operationalizes this principle by combining textual features with claim-level semantic analysis. Sem-Detect compares a target review against multiple AI-generated reviews of the same paper, l
The proliferation of advanced AI models necessitates robust methods for identifying AI-generated content, especially in critical academic processes like peer review.
Distinguishing AI-generated peer reviews from human ones is crucial for maintaining academic integrity, trust in research, and the quality of scientific discourse.
The ability to accurately detect AI-generated content based on semantic claims, not just textual features, provides a more sophisticated approach to authorship verification.
- · Academic publishers
- · Research institutions
- · AI ethicists
- · AI models without proper attribution mechanisms
- · Academics misusing AI for peer review
Increased challenges for AI models to pass off generated content as human-written in academic settings.
Development of more sophisticated AI generation and detection mechanisms, leading to an 'arms race' in content authenticity.
Potential for new standards and regulations around AI assistance in academic processes, redefining scholarly conduct.
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