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

IMAGINATIONS: JOURNAL OF CROSS-CULTURAL IMAGE STUDIES | REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE Publication details, including open access policy and instructions for contributors: https://imaginationsjournal.ca Sustainable Publishing Guest Editors: Brent Ryan Bellamy, Abigail Fields, Rachel Webb Jekanowski, Margot Mellet Image credit: https://speculativespace.wordpress.com To cite this article: Gordo, Angel, Chris Gray, Ana Rodríguez, Elías Said-Hung, and Raúl Tabarés. “Un/Sustainable Peer Review and Generative AI: Ethical Gaps, Editorial Acceleration, and the Whitewashing of Technological Solutionism.” Imaginations: Journal of Cross-Cultural Image Studies, Vol. 16, No. 1, 2025, pp. 25-52. 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UN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI: ETHICAL GAPS, EDITORIAL ACCELERATION, AND THE WHITEWASHING OF TECHNOLOGICAL SOLUTIONISM ANGEL GORDO CHRIS GRAY ANA RODRÍGUEZ ELÍAS SAID-HUNG RAÚL TABARÉS 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 openscience principles anchored in human oversight, deep sustainability, and broader justice. The paper concludes by interrogating sustainability’s absence from green-economy debates Les IA génératives en évaluation par les pairs posent des enjeux éthiques et écologiques et risquent d’accentuer les inégalités de l’édition académique. Les gains de vitesse masquent des reculs de qualité et de responsabilité. L’entraînement et le déploiement des modèles ont des coûts environnementaux. Dans les flux éditoriaux, l’IA privilégie des palliatifs techniques plutôt que des réformes et reproduit des biais tout en se disant neutre. Nous plaidons pour une science ouverte ancrée dans la supervision humaine, la durabilité et la justice, et interrogeons leur absence des discours sur l’« économie verte », avant d’esquisser and mapping the values likely to shape the future of peer review. les valeurs qui guideront l’avenir de l’évaluation. INTRODUCTION A cademia is undergoing a profound transformation driven by rapid technological advances, intensified global collaboration, the spread of for-profit scholarship, and the rise of AI, which we define as Algorithmic Intelligence (Gordo and Gray). Maintaining research integrity has become more important than ever in the face of new challenges and opportunities. The peer review processes, central to ensuring such integrity, is under growing pressure due to the exponential increase in submissions. This has resulted in reviewer overload and significant delays in disseminating scholarly work. As a response, Generative AI (GenAI) technologies are being explored as tools to support and partially automate several dimensions of the peer review processes (PRP). In an effort to enhance the peer review processes, publishers have introduced automated screening tools that allow editors to accelerate manuscript evaluation, verify compliance with journal policies, and identify suitable reviewers based on their expertise and previous performance. At the other end of the publishing chain, academic authors are increasingly employing what we label AI-assisted technologies, particularly generative conversational models such as ChatGPT and large language models (LLMs) more broadly, during the manuscript preparation phase (Dergaa et al. 616). Although these tools offer the potential to enhance and accelerate academic writing, their use also raises pressing ethical concerns around quality, authorship, authenticity, credibility, and accountability, with additional legal implications regarding copyright (Giray et al. 41). There is also the paradox of speed. Accelerationism is popular across the political spectrum, but speeding up processes does not offer a solution to unsustainable dynamics. Yet, accelerating knowledge acquisition drives scholarly publishing as do proliferating crises. Sustainability, having an acceptable homeostasis, assumes relentless expanUN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI JOURNAL OF CROSS-CULTURAL IMAGE STUDIES REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE 16-1, 2025 · 26 sionism on material levels cannot continue. And yet a balance must be reached between the dangers of a new tool such as AI, and what it can offer. The only way to really find it is to act. At our journal Teknokultura we are exploring how to use AI as part of our human-controlled peer review processes, working with the new wave of AI based on a generative process instead of just rejecting it or submitting to it. PRP are widely regarded as crucial for establishing research quality and scholarly legitimacy, while also playing a significant role in distributing academic prestige and recognition (Tennant and Ross-Hellauer 1, 12). In this paper, we address emerging issues, challenges, and ethical considerations surrounding the integration of AI into PRP. After surveying the main ethical and related concerns raised in the literature, we focus on a notable blind spot: the absence, or at best marginal presence, of discussions on sustainability and the environmental impact of GenAI within the broader peer review ecosystem. Our definition of sustainable publishing resonates with Antoine Fauchié’s notion of permapublishing, also featured in this special issue on sustainable publishing. Drawing from permacomputing, Fauchié emphasizes durability, sobriety, and long-term viability in editorial infrastructures. His call to decouple publishing workflows from extractive infrastructures, to empower editors and researchers through minimalist, self-hosted tools, and to depreciate resource-intensive systems, aligns closely with our argument for a slower, more environmentally grounded editorial culture. As we will show, addressing sustainability in PRP and publishing is not just a technical problem, it is political and epistemological as well. GENERATIVE AI IN PEER REVIEW PROCESSES: FUNCTIONS, RISKS AND EMERGING CONCERNS T he integration of GenAI systems into scholarly workflows is reshaping both academic communication and PRP. Louie Giray’s analysis of the views of members of the 170,000-person strong Facebook group Reviewer 2 Must Be Stopped! is an excellent GORDO / GRAY / RODRÍGUEZ / SAID-HUNG / TABARÉS ISSUE 16-1, 2025 · 27 overview of both the promises and perils of automating more of academic PRP, while also proving beyond doubt that the system now is not fit for purpose (146). Since 2023 there have been more and more cases of peer reviews clumsily using AI. It is the same for paper writing. A study of peer-reviewed submissions to AI conferences in 2023 and 2024 estimates that up to 16.9% of them were “substantially modified by LLMs” (Liang et al.1). No doubt the number is growing higher, and in almost every discipline, not just AI research. Although PRP have been historically adapted to technological change in its 300-year history (Drozdz and Ladomery 1; Tennant et al. 5), the rise of GenAI introduces a new set of opportunities and challenges, as numerous opinion articles point out (Salah et al.; Schintler et al.; Sabet et al.). These include new ethical dilemmas (Schintler et al.; Seghier), especially around citation accuracy (Mehregan) and the need for new policies that implement transparency (Mollaki). One of AI’s main benefits in PRP is improving their efficiency. A qualitative analysis of reviews of one paper, comparing human reviewers to AI peer review, found excellent quality with less time committed (Biswas et al.). Another study trained an AI peer review system on 3,300 papers and then compared its reviewing to humans on a new paper. Despite high correlations between machine and human evaluations, the research team had reservations about the quality of machine reviews (Checco et al.). From initial manuscript screening to review report drafting, AI tools are already streamlining various stages, according to a team of researchers in the Philippines who used a strategic planning tool called SWOT (Strengths, Weaknesses, Opportunities, and Threats) (Giray et al.). Some aspects of quality assessment—such as readability checks or formatting—can reasonably be assisted or automated. AI might reduce desk rejections by flagging superficial issues (e.g., layout, graphic quality) and providing early feedback to authors without engaging reviewers unnecessarily. This could help mitigate “first impression” bias and allow reviewers to focus on scientific content (Checco et al.). AI also supports routine editorial tasks like plagiarism UN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI JOURNAL OF CROSS-CULTURAL IMAGE STUDIES REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE 16-1, 2025 · 28 detection, according to a 2022 survey of 685 peer reviewers (Calamur and Ghosh). Two qualitative analyses of tools and reports agreed (Kousha and Thelwall; Jiffriya et al.). Generative AI can assist with paper screening, integrity checks, and issue flagging, thus facilitating more focused and constructive feedback from human reviewers (Miao et al.). Mike Thelwall trained an AI model on 51 of his previously published articles and found its evaluations surprisingly aligned with his own, expressing a generally positive impression of its judgment (9). AI may also shorten review timelines (Mrowinski et al.; Farber), improve tone and clarity in reviewer comments (Verharen) and reduce workload by matching reviewers based on expertise (Kousha and Thelwall). GenAI and other algorithmic decision making, in this sense, promise to alleviate bottlenecks in editorial workflows (Björk and Solomon). More recent developments push even further. Advances in LLMs suggest AI could support—or even replace—some complex human writing tasks. A team led by Lu Sun introduced MetaWriter, trained on five years of open peer review data, capable of highlighting “common topics in the original peer reviews, extracts key points by each reviewer, and on request, provides a preliminary draft of a meta-review that can be further edited” (1) . Similarly, as Lu Sun et alia note somewhere else, other tools like ReviewFlow, “scaffolds novices using contextual reflection cues, in-situ knowledge support, and notesto-outline synthesis” (16). These tools can also enhance clarity and coherence in review reports (Mehta et al.; Mollaki). However, despite these advantages, integrating GenAI into peer review raises significant concerns. Issues around bias and transparency persist (Calamur and Ghosh; Nath et al.; García). Such bias may stem from initial impressions, theoretical or ideological orientations, language choices, social identity markers, or institutional prestige (Checco et al.). Laurie A. Schintler et al. warn that while AI may “alleviate some of the problems that confront peer review today, such as long decision and publication delays”, it can also compromise the ethics of AI use in peer review mainly “to matters related to plagiarism and authorship in academic journal publishing” (2). AdditionGORDO / GRAY / RODRÍGUEZ / SAID-HUNG / TABARÉS ISSUE 16-1, 2025 · 29 al risks include breaches of authorship integrity, threats to confidentiality, and a general lowering of editorial standards (Chauhan and Currie; Mensah). The speed enabled by AI tools might shortcut rigorous peer scrutiny, resulting in weaker publications (Carobene et al.). Furthermore, GenAI systems are prone to fabricating content or references, which undermines trust in the review process (Giray et al.; Khalifa and Ibrahim). There is growing evidence that more advanced models tend to produce more errors—such as hallucinations—than earlier ones, and developers still cannot fully explain why (Metz and Weise). In scholarship, accuracy remains a fundamental ethical issue. Additional ethical risks arise when reviewers rely too heavily on AI, potentially diminishing critical judgment. There is growing concern that human reviewers might be replaced, not just assisted. Overreliance could blur the boundaries between human and machine authorship, threatening academic originality and credibility. As Tiffany I. Leung and collaborators note in their editorial text “Authors must also be cautious of the potential for unintentional plagiarism […] or overt AI plagiarism (the authors passing off or taking credit for the production of statements that were generated by AI). Either form of plagiarism is deemed not acceptable” (par. 8—emphases in original). Because algorithms mirror the biases of their training data, they can perpetuate historical or sociocultural distortions (Limongi). This could lead to automation bias, loss of reviewer skill, and an unintended narrowing of what gets published, reducing epistemic diversity (Giray et al.). Other major concerns include the potential homogenization of academic perspectives through excessive reliance on AI and the lack of robust tools to detect AI-generated or modified content in manuscripts and peer reviews. These issues remain central in the current debates surrounding the integration of AI into the editorial process. PUTTING MACHINES IN A HUMAN LOOP: OVERSIGHT, INTEGRITY AND RESPONSIBILITY IN GENERATIVE AI PEER REVIEW PRP constitute the backbone of academic research, ensuring that scholarly work is evaluated by experts before it is published UN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI JOURNAL OF CROSS-CULTURAL IMAGE STUDIES REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE 16-1, 2025 · 30 (Houghton). Traditionally, peer reviewers engage deeply with manuscripts to identify flaws and provide meaningful feedback. Now, however, with AI increasingly taking over some of these tasks, reservations have emerged about the possibility that reviewers may overly depend on AI outputs without adequately verifying them (Giray et al.). There are already multiple cases of individuals, some with considerable expertise, relying on flawed AI-generated content without appropriate scrutiny. It isn’t just important newspapers recommending for your summer reading books that don’t exist (Blair), it is also a major government report on children’s health from the U.S. Department of Health full of nonexistent science (Mitchell) and a lawyer from Stanford University being paid $600 an hour as an expert, filing official court documents with hallucinated cases for the State of Minnesota in a trial about the constitutionality of legislation on deepfakes and elections (Gray, Deepfakes, par. 18)! This is just the beginning. While AI holds promise, it is clear that human reviewers remain essential for preserving authenticity and intellectual integrity. For instance, Carobene et al. contend that “we must approach the integration of AI in study design with discernment, ensuring that it serves as an adjunct to, rather than a replacement for, the nuanced and innovative contributions of human intellect” (842). From this perspective, academic publishing must center human flourishing, prioritizing equitable and meaningful knowledge creation and dissemination over purely technical optimization. While AI might streamline certain editorial logistics, it cannot replicate the critical judgment and interpretive depth that reviewers bring to research assessment (Mehta et al.). This is why it is not enough just to have a human in the loop (or, as the military says, “man in the loop”). The “loop,” which really means the system, has to be fundamentally human, and machines should be integrated at places where they can be helpful, but always be checked and controlled by people. If we had a scholarship system that mainly consisted of machines and humans were just looking for errors and editing what is fundamentally a machine product, we would have lost. GORDO / GRAY / RODRÍGUEZ / SAID-HUNG / TABARÉS ISSUE 16-1, 2025 · 31 In this context, the OSM has, over time, established a foundational framework—both in terms of infrastructure and terminology—that supports the responsible development and application of GenAI in research. The adoption of GenAI models that align with open science values, such as the transparent disclosure of training data sources, would represent a meaningful step toward encouraging model creators to engage more substantively with open science principles (Hosseini et al.). Nonetheless, the prevalence of commercial models—often closed-source and resource-intensive developments—poses significant challenges to the incorporation of these tools into open science workflows. However, the phenomenon of “openwashing” complicates the genuine implementation of openness, as many AI models are marketed as open but remain functionally closed, withholding critical components such as datasets, model weights, or documentation. The OSM originated in the 17th century alongside the emergence of scientific journals, when public demand for access to knowledge compelled scientific communities to share resources (Machado). At its core, the movement emerged from a conflict between researchers seeking collaborative access to knowledge and institutions seeking profit through control of access (David). In terms of research assessment, OSM promotes open identities, open reports and open participation as key alternatives to traditional peer review. Journals that align with open science often adopt customized combinations of these practices based on their editorial aims. This flexibility allows for a more nuanced peer review processes—one that balances openness with scholarly rigour, bias mitigation, and accountability. Aligned with this ethos, Andreas Finke and Thomas Hensel propose a decentralized, community-based peer review model governed by smart contracts and blockchains, aiming to improve transparency, speed and quality (2). Limongi also suggests that participatory models and open-source AI systems can ensure a fairer and more responsible integration of AI into scientific work (8-9). More broadly, open science and its core practices—open data, open access, and open peer review—could offer one of the most robust antidotes to the ethical and editorial risks of GenAI in PRP. UN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI JOURNAL OF CROSS-CULTURAL IMAGE STUDIES REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE 16-1, 2025 · 38 Under current conditions, the OSM can offer not only an ethical corrective but also a sustainability-oriented framework for hybrid AI publishing models. Reconsidering OSM’s relevance is not incompatible with developing regulatory frameworks for AI in academic publishing. In fact, it may provide an opportunity to reshape ethical standards and rethink regulation for a transformed scholarly communication landscape. From the standpoint of “intelligent governance,” scholars like Pompeu Casanovas propose an AI that helps design an ethically responsible version of itself, capable of responding to the profound social implications of GenAI (15). Yet there are different challenges that neither the technological push nor humanistic or ethical approaches can solve by themselves. In particular, significant problems are still not being addressed in the extractive and exploitative nature of the peer review processes under platform logics and the “AI imperative.” We need to reconsider the use and adoption of AI into the peer review processes and to reflect on the sustainability of current platform business models anchored by the small number of giant forprofit academic publishers who dominate and gatekeep so much human knowledge. EDITORIAL ACCELERATION AND ITS DISCONTENTS: THE UN/ SUSTAINABLE LEGACY OF AI SOLUTIONISM I n an increasingly accelerated world, where urgency defines productivity and velocity eclipses reflection, the publishing industry and the economic logic underpinning scholarly production and impact markets have long embraced the consequences of such pace. This is especially evident in the well-established business of paying to publish in high-impact journals through mechanisms such as publication fees and article processing charges (APCs), which finance open-access publication models. The peak expression of this commodification is found in the proliferation of hijacked and explicitly predatory journals. Despite the flurry of recent discussions around GenAI-assisted peer review, most conversations remain anchored in technical logistics, GORDO / GRAY / RODRÍGUEZ / SAID-HUNG / TABARÉS ISSUE 16-1, 2025 · 39 Figure 1: “Let’s chat … we are now offering a secure hybrid human/AI service in just 5 days.” Chris Leonard, Scalene 36: We are 1! / REMOR / SIFT, 2025. questions of oversight and transparency, or proposals for regulatory frameworks aimed at restoring confidence in the system. Engaging with this literature has allowed us to critically reflect on the material conditions that make GenAI publishing possible and the broader discourse surrounding the future of peer review. It has also helped frame the editorial ecosystem’s obsession with speed, efficiency, and shorter decision making cycles. Figure 1 illustrates the growing push for accelerated review models—often marketed as hybrid human/AI services completed in as little as five days. This drive for ever-faster publishing aligns with the logics of accelerationism, a techno-political stance that sees technological and economic intensification as a catalyst for systemic transformation (Mardones). Two main currents can be distinguished: left accelerationism, which advocates using technology to transcend capitalism through automation and redistribution (Avanessian and Reis); and right accelerationism, which promotes unchecked capitalist expansion, believing acceleration will bring inevitable change (Land, Noumena; Teleoplexia). Critics argue that all forms of accelerationism worsen inequality and hasten planetary ecological collapse (Noys; Arias Gil). UN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI JOURNAL OF CROSS-CULTURAL IMAGE STUDIES REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE 16-1, 2025 · 40 The peer review ecosystem, where GenAI-powered processes are perhaps its most visible accelerationist artifact, exists within this broader ideological terrain. In tandem, many ethical discussions surrounding AI in peer review rely on technological solutionism and disciplinary frameworks that often eclipse deeper issues of sustainability. This fixation on protecting the “human factor” or enhancing transparency risks obscuring the deeper flaws of the peer review system itself—flaws that long predate AI. While AI indeed poses ethical and social risks, including undermining integrity and trust, humanled peer review also suffers from longstanding problems such as bias, exploitation, opacity, and inequity (Resnick and Elmore; Schintler et al.; Tennant and Ross-Hellauer). Moreover, the ethics discourse surrounding GenAI may be complicit in whitewashing other urgent problems, particularly the environmental costs of deploying AI at scale in peer review. Although the carbon footprint of AI models is occasionally mentioned, most literature on GenAI and PRP does not meaningfully engage with the environmental consequences of using LLMs in editorial workflows. As reiterated throughout this paper, dominant concerns remain limited to efficiency, bias, and workflow optimization, with little attention paid to sustainability. Thus, we argue that the current AI boom in academic publishing—driven by accelerationist logics, technological solutionism and ethical minimalism—obscures both the flaws of the peer review system and the ecological footprint of AI integration. These tendencies converge in a form of greenwashing aligned with the extractivist logic of green capitalism, in which environmental goals are superficially reconciled with capital accumulation. Green capitalism posits that growth can continue without ecological degradation, provided that sufficient technological and market-based solutions are implemented. The broader “green economy” similarly claims to balance economic development with sustainability and social justice. Developing and operating even beneficial AI models, extensive deep learning systems, requires substantial computing power and energy. Training advanced models can consume hundreds or even thousands GORDO / GRAY / RODRÍGUEZ / SAID-HUNG / TABARÉS ISSUE 16-1, 2025 · 41 of graphics processing unit hours, resulting in significant electricity usage and considerable carbon dioxide emissions contributing to climate change. Moreover, many commercial AI models are proprietary and lack transparency, making it difficult to fully evaluate their environmental impact and creating challenges for sustainability efforts. For instance, Patterson and his coauthors estimated that training GPT-3, a language model with 175 billion parameters, generated approximately 552 tons of carbon dioxide equivalent (CO₂eq) (7). They compared this to the emissions of a round-trip flight between San Francisco and New York, noting that GPT-3’s training emissions were roughly three times higher (13). In response to these challenges, various tools have been developed to help quantify and address the environmental impact of AI systems. Resources such as the Machine Learning Emissions Calculator (Lacoste et al.) and CodeCarbon (CodeCarbon) aim to raise awareness of AI’s ecological footprint. Alongside newer AI-based benchmarking systems, these tools assist researchers and institutions in tracking and mitigating carbon emissions—despite the paradox that AI is being used to monitor the damage it helps generate. Beyond technical solutions, a collective intervention proposed by Carrie Karsgaard and colleagues—also included in this special issue—offers a more systemic response. Their piece, “The Pedagogy of Manifesto Making” puts forward a Decarbonizing Manifesto that invites us to rethink the carbon-intensive infrastructure of scholarly publishing. Written as a form of situated and relational pedagogy, the manifesto challenges extractivist production models, prestige-driven evaluation, and hypermobility. Instead, it promotes care, slowness, and institutional responsibility as cornerstones of sustainable academic practice. Their proposal resonates strongly with the ethical and ecological imperatives addressed throughout this issue. And if care is not taken, these responses risk reproducing a Green AI narrative—a subdomain that promises sustainable AI by promoting measurement, tuning, and optimization, without challenging underlying extractivist assumptions (Tomlinson et al.; Verdecchia et al.). UN/SUSTAINABLE PEER REVIEW AND GENERATIVE AI JOURNAL OF CROSS-CULTURAL IMAGE STUDIES REVUE D’ÉTUDES INTERCULTURELLES DE L’IMAGE 16-1, 2025 · 42 Recent research calls for standardized protocols to quantify the climate impact of AI models and to prioritize sustainability in AI ethics frameworks (Iqbal et al.). Ultimately, unless sustainability becomes a central axis in both peer review reform and AI integration, we risk replacing one flawed system with another, which is more opaque, extractive, and unsustainable than before. As scholars in the precariat, working for free to make knowledge more democratic and more helpful to humanity’s quest for a just and sustainable world, we understand that sustainability is about more than publishing protocols or even the crucial issue of energy use and climate change. It is about us. We must learn to sustain our work, to have sustainable activism, as Laurence Cox explains in his powerful overview and defense of this concept. He points out that as we burn out, so burns the world (530). There are some good theories (Suzuki; Ede; Boggs and Kurashige) and wonderful practices that have helped us survive and even thrive. Our participation outside of scholarship in mass social movements, for example, has not just taught us a great deal, it has sustained us just as working on Teknokultura sustains us, making our choice to be scholars something we can live with. Grace Lee Boggs was a Chinese American activist who worked for peace until her death at 95. Famous for supporting the Detroit Black Power movement, pioneering sustainable agriculture, and always working to end war, she kept a positive spirit by always remembering that we work for a better world, not just against the evil in this one. Every crisis is an opportunity, and scholarly publishing is certainly in crisis. 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