arXiv subjects

Alex Glynn

Publications and source records attributed to Alex Glynn.

3 recordsLinked to original sources

arXiv2025

Guarding against artificial intelligence--hallucinated citations: the case for full-text reference deposit

The tendency of generative artificial intelligence (AI) systems to "hallucinate" false information is well-known; AI-generated citations to non-existent sources have made their way into the reference lists of peer-reviewed publications. Here, I propose a solution to this problem, taking inspiration from the Transparency and Openness Promotion (TOP) data sharing guidelines, the clash of generative AI with the American judiciary, and the precedent set by submissions of prior art to the United States Patent and Trademark Office. Journals should require authors to submit the full text of each cited source along with their manuscripts, thereby preventing authors from citing any material whose full text they cannot produce. This solution requires limited additional work on the part of authors or editors while effectively immunizing journals against hallucinated references.

cs.DL↗

arXiv2024

Academ-AI: documenting the undisclosed use of generative artificial intelligence in academic publishing

Since generative artificial intelligence (AI) tools such as OpenAI's ChatGPT became widely available, researchers have used them in the writing process. The consensus of the academic publishing community is that such usage must be declared in the published article. Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots. This analysis of the first 768 examples collected reveals that the problem is widespread, penetrating the journals, conference proceedings, and textbooks of highly respected publishers. Undeclared AI seems to appear in journals with higher citation metrics and higher article processing charges (APCs), precisely those outlets that should theoretically have the resources and expertise to avoid such oversights. An extremely small minority of cases are corrected post publication, and the corrections are often insufficient to rectify the problem. The 768 examples analyzed here likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable. Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI. This is an updated version of a previous preprint.

cs.DL↗

arXiv2024

The existence of stealth corrections in scientific literature -- a threat to scientific integrity

Introduction: Thorough maintenance of the scientific record is needed to ensure the trustworthiness of its content. This can be undermined by a stealth correction, which is at least one post-publication change made to a scientific article, without providing a correction note or any other indicator that the publication was temporarily or permanently altered. In this paper we provide several examples of stealth corrections in order to demonstrate that these exist within the scientific literature. As far as we are aware, no documentation of such stealth corrections was previously reported in the scientific literature. Methods: We identified stealth corrections ourselves, or found already reported ones on the public database pubpeer.com or through social media accounts of known science sleuths. Results: In total we report 131 articles that were affected by stealth corrections and were published between 2005 and 2024. These stealth corrections were found among multiple publishers and scientific fields. Conclusion: and recommendations Stealth corrections exist in the scientific literature. This needs to end immediately as it threatens scientific integrity. We recommend the following: 1) Tracking all changes to the published record by all publishers in an open, uniform and transparent manner, preferably by online submission systems that log every change publicly, making stealth corrections impossible; 2) Clear definitions and guidelines on all types of corrections; 3) Support sustained vigilance of the scientific community to publicly register stealth corrections.

cs.DL↗