What question did this study set out to answer?

Examine the risks that generative artificial intelligence poses to research credibility, scientific transparency, and data integrity across scientific communications.

May 21, 2024Proceedings of the National Academy of SciencesOpen Access

Protecting scientific integrity in an age of generative AI

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Examine the risks that generative artificial intelligence poses to research credibility, scientific transparency, and data integrity across scientific communications.
Evaluated emerging applications and risks of generative artificial intelligence systems and automated writing technologies across academic workflows.
Assessed peer review procedures, authorship criteria, and institutional verification frameworks within scientific journals.
Identified substantial vulnerabilities in publishing ecosystems, including automated text fabrication, non-existent citations, and ambiguous attribution of artificial intelligence contributions.
Outlined comprehensive policy recommendations, including mandatory usage disclosures, specialized detection infrastructure, and strict editorial accountability guidelines.

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Implication

Perspective analysis reveals threats to data reliability across scientific publishing, highlighting the necessity of institutional guardrails and verification standards.

Abstract

Issn:0027-8424

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Cite This Study

Blau et al. (2024) studied this question.

synapsesocial.com/papers/6a0efa9437aeb0126447bc8e https://doi.org/10.1073/pnas.2407886121

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Implication

Perspective analysis reveals threats to data reliability across scientific publishing, highlighting the necessity of institutional guardrails and verification standards.

Abstract

Issn:0027-8424

Protecting scientific integrity in an age of generative AI

Key Points

No abstract yet

Implication

Abstract

Discussion

Cite This Study

Protecting scientific integrity in an age of generative AI

Key Points

No abstract yet

Implication

Abstract

Discussion

Cite This Study

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