Return of the Reviewer

Artificial intelligence (AI) is increasingly being tested as a tool for peer review, and its role brings both promise and concern. On the positive side, AI can process large volumes of manuscripts quickly and with consistency. It is capable of scanning for plagiarism, flagging potential ethical issues and checking adherence to journal guidelines. Its ability to detect statistical errors or overlooked citations can enhance the accuracy of reviews. This efficiency can ease the workload of human reviewers and help journals manage growing submission numbers. The Scholarly Kitchen brought this interesting topic to our attention with a humorous approach, in their article, “Guest Post — May the AI Be With Science.”

At the same time, relying on AI raises important questions. Algorithms are trained on existing data, which means they can reinforce biases present in past publishing trends. An AI system may overlook innovative or unconventional research that does not fit established patterns. It also cannot evaluate creativity, context or the nuance of an argument in the way an experienced human reviewer can. Authors may worry about a lack of transparency in how AI systems reach their decisions, and editors must take care that technology supplements rather than replaces human judgment.

The future of AI in peer review likely depends on balance. When paired with human expertise, it can streamline processes and improve accuracy, but without oversight it risks narrowing rather than enriching scholarly discourse.

But no matter how advanced AI becomes, it often lacks the ability to truly understand the nuances of specialized industries or unique organizational needs. This is where custom taxonomies shine.

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.