Peer review has long served as the cornerstone of academic publishing, intended to safeguard quality, accuracy and rigor in scholarly work. The arrival of artificial intelligence (AI) has introduced new dynamics that complicate this process. While AI tools provide efficiency and analytical power, they also raise questions about integrity, bias and the future role of human judgment in scholarly evaluation. This interesting and timely subject came to us from The Scholarly Kitchen in their article, “Peer Review in the Era of AI: Risks, Rewards, and Responsibilities.”

One of the primary challenges lies in verifying authorship and originality. AI systems can now generate text that mimics academic style with surprising fluency. This makes it more difficult for reviewers to distinguish between genuine contributions and machine-assisted production. The traditional reliance on plagiarism detection is no longer sufficient, as AI-written material can evade such tools by creating content that appears novel while lacking true intellectual grounding. Reviewers must develop new strategies for identifying whether the ideas presented stem from authentic research or algorithmic synthesis.

Another concern is the potential overreliance on AI tools by reviewers themselves. Automated systems can summarize manuscripts, check for statistical errors and even suggest improvements. While these functions are useful, they risk narrowing the scope of evaluation. A human reviewer’s role involves more than error detection. It requires assessing the significance of findings, the originality of arguments and the contribution to ongoing academic conversations. If reviewers defer too heavily to AI, the subtle but critical judgments that shape scholarly discourse may be diminished.

Bias is another dimension that AI introduces into the peer review process. Algorithms are trained on existing datasets, which may reflect historical inequities or systemic blind spots. If AI tools are used to screen submissions, recommend reviewers or flag content for further scrutiny, they may reproduce those biases. This threatens the diversity of perspectives in scholarship and can reinforce existing hierarchies rather than challenge them.

The workload pressures on peer reviewers compound the problem. Journals often struggle to secure qualified reviewers willing to commit the time necessary for thorough evaluation. AI offers the promise of alleviating some of that burden, but it also tempts editors to substitute machine assistance for the more time-consuming work of human engagement. The risk is that efficiency may come at the expense of rigor.

Ultimately, the rise of AI in academia requires a reevaluation of the peer review process itself. The challenge is not to reject technological assistance, but to define clear boundaries where human judgment remains indispensable. The credibility of scholarly publishing depends on maintaining that balance.

The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.