From AI peer review to AI ethics: addressing terminological confusion in science

Purpose This article aims to highlight the problem of unclear, inconsistent and potentially misleading terminology in the rapidly evolving field of artificial intelligence (AI). It underscores the need for the scientific community to establish coherent and precise AI-related terminology. Design/methodology/approach To illustrate the issue of terminological inconsistency, the article analyzes two terms: “AI peer review” and “AI ethics.” The analysis shows that the former is conceptually flawed, while the latter is used in several different ways, even among experts. These two terms serve as examples of a more general terminological issue. While not all AI-related terms are problematic, there is currently no systematic understanding of which ones are accurate and which are not. For this, a case-by-case evaluation is needed, a difficult, complex and time-consuming task – and yet of high importance. Findings The analysis reveals that AI terminology remains underdeveloped and confusing. This creates challenges for both scientific communication and public understanding. Despite the fast pace of AI development, it is not too late to address this issue. However, doing so will require timely and coordinated action from the scientific community, particularly those working directly in AI. Originality/value By focusing on terminology rather than technical or ethical aspects, the article draws attention to a critical but often neglected dimension of AI discourse. It offers a timely call for the scientific community to take responsibility for defining and consistently using clear, accurate language in AI-related contexts.