Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Integrity and Misconduct, Where Does Artificial Intelligence Lead?
2
Zitationen
9
Autoren
2025
Jahr
Abstract
ABSTRACT This paper, part of the third stage of the Harbingers project studying early career researchers (ECRs), focuses on the impact of artificial intelligence (AI) on scholarly communications. It concentrates on research integrity and misconduct, a ‘hot’ topic among the publishing community, in no small part due to the rise of AI. The interview‐based study, supported by an extensive literature review, covers a convenience sample of 91 ECRs from all disciplines and half a dozen countries. It provides a new and fresh take on the subject, using the ‘voices’ of ECRs to describe their views and practices regarding integrity and misconduct. We show that ECRs are clearly aware of research misconduct and questionable practice with three‐quarters saying so. A big indictment of the scholarly system, but, not surprising given a rising number of retractions and questionable journals. The main blame for this is levelled at the haste with which researchers publish and the volume of papers produced. ECRs also feel that things are likely to get worse with the advent of AI. They believe that they are aware of the problems and how to avoid the pitfalls but suspect that things are approaching a cliff‐edge, which can only be avoided with strong policies and an overhaul of the reputational system.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.102 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.468 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.429 Zit.