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AI evidence extraction

Integrity under pressure: on generative AI, fabricated references and ethical publishing

Marina Joubert, Michelle Riedlinger · Journal of Science Communication · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

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This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.22323/388620260304154318

Methodology & findings

Study design

Editorial commentary and literature review of existing scholarship on fabricated references and AI use in academic publishing.

Main result

The paper identifies a "growing problem with fabricated references" stating that "a rise in so-called 'ghost references', fabricated by AI tools" has been observed. Xu et al. [2026] found that "between 14% and 92% of 375,440 AI-generated citations were fabricated to some degree." Case studies demonstrate how "fabricated references can pass undetected through existing publication systems," with examples including Moore [2025] who "analysed a paper published in a reputable journal and found that the majority of its references were fabricated, ultimately leading to the article's retraction."

Research paradigm

Critical/Interpretive

Author conclusions

The authors conclude that "authorship entails ownership, and ownership entails responsibility," emphasizing that "quality control mechanisms cannot replace authors' ethical obligations." They state their commitment: "We will reject, withdraw, or retract manuscripts that contain fabricated" content, grounding this "in safeguarding JCOM's standards and recognising that science communication, as a field of research and practice, has a particular stake in the integrity of knowledge production and sharing."

Risk of bias

Selection bias: The editorial perspective is based on JCOM submissions which may not represent all academic fields or publishing venues; Observation bias: Fabricated references may go undetected, leading to underestimation of the problem; The retrospective nature of case studies limits generalizability

Open questions raised

  • The authors identify that "because this is a rapidly emerging phenomenon, much of the empirical research on fabricated citations currently appears as preprints," suggesting a need for more rigorous, peer-reviewed studies on AI-generated fabricated references. They also note the ongoing development of automated validation systems and the need for better detection mechanisms.
Extracted from: pdf

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