Artificial intelligence (AI) is reshaping nearly every sector of the knowledge economy, and scholarly publishing is no exception. From accelerating peer review to generating plain-language summaries, AI tools are challenging long-standing practices while offering new opportunities to enhance research dissemination. Yet, as with any transformative technology, the integration of AI into scholarly workflows also raises critical questions about transparency, authorship, equity and trust. This very timely and important topic was inspired by the article, “AI Strategy, Governance, and Monetization in Scholarly Publishing: Lessons from Industry Front-Runners“, from The Scholarly Kitchen.

One of the most immediate impacts of AI in scholarly publishing is its ability to streamline editorial and administrative processes. AI-powered tools can now check manuscripts for grammar, plagiarism, reference accuracy and formatting compliance—tasks that traditionally consumed valuable human time. Some publishers are experimenting with AI-assisted peer review, using machine learning to match reviewers with appropriate expertise, identify potential conflicts of interest or flag statistical anomalies before the manuscript even reaches human eyes.

However, the integration of AI in scholarly publishing raises a host of ethical concerns. Who is accountable for errors introduced by AI? How can publishers ensure that AI tools do not propagate bias, misinformation or fabricated content? What rights do authors have if their work is used to train AI models?

Despite the challenges, most experts agree that AI should not replace human expertise in scholarly publishing but rather augment it. The best outcomes will emerge from hybrid models where machines handle the routine, and humans focus on the interpretive, ethical and evaluative dimensions of scholarship.

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.