AI evidence extraction

The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts

Mohammad Hosseini, David B. Resnik, Kristi Holmes · Research Ethics · 2023

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

9/10

Relevance

1/4

Quality (LMQS)

Evidence

202

Citations

7.16

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1177/17470161231180449

Methodology & findings

Study design

Conceptual analysis and ethical argumentation based on examination of proposed guidelines and policies regarding LLM use in scholarly publishing.

Main result

The authors argue that "bans are unenforceable and would encourage undisclosed use of LLMs" and that "LLMs can be useful in writing, reviewing and editing text, and promote equity in science." They contend that "naming LLMs as authors or mentioning them in the acknowledgments are both inappropriate forms of recognition because LLMs do not have free will and therefore cannot be held morally or legally responsible for what they do."

Reports effect sizes.

Research paradigm

Normative ethics / Philosophical argumentation

Author conclusions

The authors conclude that researchers using LLMs should "(1) disclose their use in the introduction or methods section to transparently describe details such as used prompts and note which parts of the text are affected, (2) use in-text citations and references (to recognize their used applications and improve findability and indexing), and (3) record and submit their relevant interactions with LLMs as supplementary material or appendices."

Open questions raised

The paper identifies a need to improve APA Style guidelines for referencing ChatGPT and other LLMs, suggesting more specific citation methods that indicate "the contributor who used LLMs," "the used version and model," and "the time of usage."

Data: not_statedCode: not_statedExtracted from: pdfAgreement 79%

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