When Declarations Just Don't Cut it: Building a Risk-Based Framework for AI Guidelines in Publishing

Artificial intelligence (AI) is no longer a peripheral tool in the scientific process; it is rapidly becoming central not only to manuscript preparation, such as writing, editing, and revisions, but also to the core components of research itself, including literature review, data processing and analysis, and identifying significant outcomes. Now that the discussion around the rise of these tools has been covered ad nauseam, the focus must now shift to addressing its risks and opportunities with clear, actionable strategies. Publishers are confronted with a growing need for a robust framework to assess and manage the risks and opportunities associated with AI tools. This article focuses on 4 concrete steps publishers can take to develop and implement an effective risk and opportunity management strategy for AI adoption, and offers clear recommendations for policy, oversight, and education. Addressing the Risks in High Res: Building a Risk Management Framework When the European Union (EU) created its policy on AI, it did not suffice with a one-size-fits-all approach. Rather, the EU AI Act1 established a risk register framework through which new tools and use cases could be reviewed, evaluated, and categorized. The potential risk and harm of these tools are carefully scrutinized, with subsequent reporting, compliance, and regulatory demands imposed in line with the respective profile of respective tools. For example, personal surveillance requires a much higher degree of compliance and oversight than personalized AI restaurant suggestions. The European Commission has taken those guidelines and adopted them into high-level living guidelines for AI in […]