AI evidence extraction

Un/Sustainable Peer Review and Generative AI: Ethical Gaps, Editorial Acceleration, and the Whitewashing of Technological Solutionism

Angel Gordo, Chris Hables Gray, Elías Said-Hung, Raúl Tabarés · Imaginations Journal of Cross-Cultural Image Studies · 2025

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

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.17742/image29731

Methodology & findings

Study design

Conceptual analysis and critical hermeneutic examination of generative AI's role in peer review systems; literature review and argument-based analysis rather than empirical investigation

Main result

The paper identifies that "Generative AI in peer review raises ethical and environmental concerns and risks deepening existing inequities in scholarly publishing" and that "evidence shows it reproduces human biases while being cast as neutral," suggesting that despite celebrated speed gains, there are serious quality, accountability, and equity implications.

Research paradigm

Critical theory / interpretive analysis

Author conclusions

The authors conclude that "We call for a renewed commitment to open-science principles anchored in human oversight, deep sustainability, and broader justice" and note that "The paper concludes by interrogating sustainability's absence from green-economy debates and mapping the values likely to shape the future of peer review."

Risk of bias

Potential selection bias in which AI applications and peer review systems are discussed; Possible confirmation bias in highlighting problematic cases of AI in peer review; Limited empirical data presented (as this is a critical essay rather than empirical study)

Open questions raised

The paper identifies that "sustainability's absence from green-economy debates" represents a significant gap, and calls for attention to how values shape future peer review structures beyond technological solutions.
The paper identifies that sustainability is absent from green-economy debates related to generative AI and peer review, and maps the values likely to shape future peer review systems.
The authors identify the absence of sustainability considerations from green-economy debates regarding peer review and call for attention to how values will shape the future of peer review.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 77%

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