A lot of attention has been given to the latest artificial intelligence (AI) chatbot – ChatGPT – which was released in November 2022. Considering that machine learning, natural language processing, and textual creation have made significant advances over the past decade, we should not be dismissive of them. The Scholarly Kitchen brought this interesting information to us in their article, “Thoughts on AI’s Impact on Scholarly Communications? An Interview with ChatGPT.”

There has been an increase in AI tools designed specifically for those performing research, academic writing and technical writing. Similarly, a growing number of AI tools are being designed to perform the specific tasks required by academic publishers. These include plagiarism and copyright checks as well as tools to identify peer reviewers and locate relevant research for authors. As academic publishing struggles to overcome its own biases and past errors, AI has the potential to offer unbiased selection of peer reviewers and increase the diversity of authors who are published.

With any recognized benefits, one recurring issue with AI tools is that they are limited by the data inputs they learn from. While an AI tool that selects peer reviewers may have the potential to be unbiased, it also has the potential to replicate existing biases depending on the selection criteria it has been taught.

Melody K. Smith

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

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