Human peer review and artificial intelligence (AI) review are two distinct approaches to evaluating and assessing various types of content, such as research papers, articles, code, or creative works. Each approach has its own advantages and limitations. The Scholarly Kitchen brought this important subject to our attention in their article, “Ending Human-Dependent Peer Review.

There is a rumble in the academic publishing industry that human peer review should be permanently replaced with AI review to eliminate any personal bias. Human peer review involves experts or peers from the relevant field evaluating the content. This process is inherently subjective, as reviewers may have different perspectives, biases, and interpretations.

At the same time, human reviewers bring domain expertise and experience to the evaluation process, which allows them to provide nuanced feedback and spot potential issues that might not be apparent to AI systems.

On the other side, AI review can be automated, allowing for faster and more efficient evaluation of content, which is especially useful when dealing with a large volume of submissions. AI systems provide consistent evaluations and do not have biases. They follow predefined rules and algorithms.

Trust is important in any industry. When it comes to publishing scientific and academic journals, it couldn’t be more important. 

Melody K. Smith

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

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.