Authors : Angel Gord, Chris H. Gray, Ana Rodrígue, Elías Said-Hung, Raúl Tabaré
Generative AI in peer review raises ethical and environmental concerns and risks deepening existing inequities in scholarly publishing. Celebrated gains in speed often mask declines in quality and accountability.
Training and deploying large models impose environmental costs. In editorial workflows, AI can privilege technical fixes over structural reform, and evidence shows it reproduces human biases while being cast as neutral. We call for a renewed commitment to open-science principles anchored in human oversight, deep sustainability, and broader justice.
The paper concludes by interrogating sustainability’s absence from green-economy debates and mapping the values likely to shape the future of peer review.
