Collaborative Intelligence in Peer Review: Where AI Meets Expert Judgment
                     AI and Peer Review 
Collaborative 
Intelligence and Human 
Expertise
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Potential Applications 
of AI in Peer Review
• AI supports editors, reviewers, and authors 
across various stages.
• Enables automation of time-consuming 
editorial tasks.
• Enhances accuracy, speed, and fairness in the 
peer review process.
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Potential Problems 
of AI in Peer 
Review
• Data Privacy: Unpublished or confidential data 
may be exposed to AI systems.
• Bias & Over-Reliance: AI may reinforce 
inequities or be trusted without oversight.
• Transparency & Leakage: Black-box models 
and data leaks risk review integrity.
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Current Views and 
Guidelines on AI Use
• Major publishers are setting policies for ethical 
AI use.
• COPE: AI cannot act as reviewers; usage must 
be disclosed.
• Elsevier & Nature: AI tools can assist but not 
replace evaluation.
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Common Guiding 
Principles
• Human responsibility must remain central in 
all review decisions.
• AI should be transparent and supportive, not 
autonomous.
• Maintain a balance between automation and 
expert judgment.
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Looking Ahead: 
Important 
Considerations
• Explainable AI (XAI): Models must justify their 
recommendations.
• Ethical oversight and reviewer training are 
essential.
• Ensure global equity — AI should not 
disadvantage underrepresented researchers.
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