The FAIR framework: ethical hybrid peer review.

Abstract

Objectives: Traditional peer review faces critical challenges including systematic bias, prolonged delays, reviewer fatigue, and lack of transparency. These failures violate ethical obligations of beneficence, justice, and autonomy while hindering scientific progress and costing billions annually in academic labor. To propose an ethically-guided hybrid peer review system that integrates generative artificial intelligence with human expertise while addressing fundamental shortcomings of current review processes.

Methods: We developed the FAIR Framework (Fairness, Accountability, Integrity, and Responsibility) through systematic analysis of peer review failures and integration of AI capabilities. The framework employs standardized prompt engineering to guide AI evaluation of manuscripts while maintaining human oversight throughout all stages.

Results: FAIR addresses bias through algorithmic detection and standardized evaluation protocols, ensures accountability via transparent audit trails and documented decisions, maintains integrity through secure local AI processing and confidentiality safeguards, and upholds responsibility through ethical oversight and constructive feedback mechanisms. The hybrid model automates repetitive tasks including initial screening, methodological verification, and plagiarism detection while preserving human judgment for novelty assessment, ethical evaluation, and final decisions.

Conclusions: The FAIR Framework offers a principled solution to peer review inefficiencies by combining AI-enabled consistency and speed with essential human expertise. This hybrid approach reduces review delays, eliminates systematic bias, and enhances transparency while maintaining confidentiality and editorial control. Implementation could significantly reduce the estimated 100 million hours of global reviewer time annually while improving review quality and equity across diverse research communities.