Aug 2026· Interdisciplinary Journal of Critical Communications· Vol 1, pp. a10· 0 citations· 18 references
TL;DR
It is concluded that originality, authorship, integrity, fairness, and transparency are interdependent concerns rather than separate issues, and that responsible AI use is best understood as a disciplined, disclosed collaboration with a non-author tool.
Abstract
Artificial intelligence (AI) has rapidly transitioned from a research curiosity to a fundamental component of the infrastructure of teaching, research, writing, supervision, and publishing. This conceptual paper contends that the current academic discourse, framed as a binary between prohibition and permission, inadequately addresses the ethical challenges at hand. Drawing upon global AI ethical guidelines, scholarship on academic integrity, and publisher policies, the paper proposes that the ethical utilisation of AI in academic writing should be governed by a coherent system of accountability rather than by isolated regulations. It introduces an original framework, the TRACE model, Transparency, Responsibility, Authorship-accountability, Comprehension, and Equity, as an integrative conceptual tool for educators, researchers, supervisors, editors, and policymakers. This article concludes that originality, authorship, integrity, fairness, and transparency are interdependent concerns rather than separate issues, and that responsible AI use is best understood as a disciplined, disclosed collaboration with a non-author tool.
Examination of the legal and ethical perspectives of use of generative AI in academic backgrounds through a doctrinal and thematic analysis of recent scholarship, institutional guidance and evolving governing instruments concludes that transparent disclosure frameworks, redesigned assessment practices and layered governance models offer a more secure path forward.
Unknown authors· Revue Internationale du Law...· 0 citations
Artificial Intelligence (AI) is rapidly entering scientific research programmes on the assumption that the established ethical frameworks are enough to address failure risks. But that assumption is wrong: AI systems are structurally different from the tools that conventional research ethics was designed to govern. As illustrated by mainstream models, AI systems can be characterized by being stochastic, opaque, resource-intensive, and operate at population scale, meaning that ethical breaches can extend far beyond individual research projects, with potential consequences for public trust in democratic institutions. Taking the case of EU research as reference, this paper proposes the ARCHON framework,a set of ten requirements derived from the technical architecture of AI systems, to facilitate ethical compliance of scientific research for experts and practicioners engaging in roles such as proposal evaluators, project officers, ethics board members, or research administrators. The paper includes a mapping table intended to facilitate the application of the framework to evaluate ethical compliance, both for specific projects and general research programmes. Applied consistently, the ARCHON framework enables substantive rather than merely declarative AI ethical compliance, contributing this way for scientific research policy to continue addressing societal needs while remaining democratically accountable.
Jesus Manuel Benitez Baleato· Open Research Europe· 0 citations
The proliferation of artificial intelligence (AI) writing tools has fundamentally disrupted established conceptions of authorship, knowledge production, and scholarly responsibility across all academic disciplines. As AI systems grow capable of generating coherent academic prose, synthesising literature, and structuring arguments, scholars worldwide face urgent questions about the integrity of their intellectual contributions. This letter was itself produced using AI assistance, in a deliberate and transparent manner, for three purposes: first, to demonstrate that AI has reached a level of capability where it can meaningfully support substantive scholarly work; second, to model an acceptable and ethically responsible method of using AI in academic writing; and third, to accelerate the writing process by delegating minor technical roles including sentence construction, paragraphing, spelling, word choice, and formatting to AI, thereby freeing authors to concentrate on the higher order analytical, theoretical, and contextual roles where human intellectual contribution is irreplaceable. In writing this letter, Claude.ai was prompted to minimise AI typical phrasing and to identify and remove any content that risks perpetuating colonial assumptions or reproducing epistemological hierarchies harmful to knowledge systems from the Global South. The letter argues that AI does not eliminate the author; rather, it radically reconfigures authorship as a form of epistemic stewardship grounded in context, ethics, positionality, and intellectual judgement. Drawing on decolonial theory, epistemic justice frameworks, and Indigenous research methodologies, the letter contends that scholars from the Global South are especially vulnerable to the homogenising tendencies of AI generated text, which risks flattening situated knowledge, marginalising non-Western epistemologies, and reinforcing existing global knowledge hierarchies. A human and AI collaborative model is proposed, repositioning the scholar as the central epistemic authority who governs meaning, ensures contextual integrity, and fulfills ethical accountability. The letter further argues for reforms in peer review and editorial processes to distinguish between linguistic fluency and genuine conceptual depth and advances a new proposition that scholars from the Global South must not remain passive consumers of AI but must actively contribute to its development and disrupt its colonising potential at all levels of society.
Current and previous volumes are available at: https://ajsw.africasocialwork.net
Rugare Mugumbate, Augustina Naami, Kabo Diraditsile et al.· African Journal of Social Wo...· 0 citations
The study contributes an operational, value-based model that complements rather than displaces existing regulatory approaches, offering developers, regulators, and Shariah boards a design vocabulary for anticipating harm before deployment.
Maman Supardi, Hilmiy Hanif, Mursyid Rahman et al.· West Science Islamic Studies· 0 citations
Artificial intelligence is reshaping decisions that affect people, institutions, and societies. Understanding how to design, deploy, and govern AI systems that can be trusted is now essential in many disciplines. This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts. Developed from an internationally applicable educational framework, the book is designed for teaching and learning in computer science, data science, law, policy, business, and related fields. It equips students and professionals with the concepts and judgment needed to engage critically and responsibly with AI in practice. Combining ethics, governance, and practical insight, the book explains key concepts including transparency, fairness, accountability, human oversight, and stakeholder participation. An interdisciplinary approach makes the material accessible to both technical and non-technical audiences, with realistic scenarios and reflection questions so readers connect principles to real-world AI applications.
Andrea Aler Tubella, Virginia Dignum, Marçal Mora-Cantallops et al.· 0 citations
The integration of artificial intelligence (AI) into scholarly peer review represents a fundamental transformation of academic publishing’s quality control mechanisms. This report critically examined the ethical considerations, institutional practices, and emerging technologies associated with AI-assisted peer review. Drawing on recent policy documents from major publishing organizations, empirical research on AI implementation, and critical scholarship on algorithmic bias, this analysis revealed significant tensions between efficiency gains and integrity preservation. While AI tools have demonstrated potential for addressing reviewer burnout and publication delays, their deployment raises critical concerns regarding confidentiality breaches, accountability gaps, algorithmic bias, and the erosion of expert judgment. Major organizations (e.g., International Committee of Medical Journal Editors) and leading publishers such as Elsevier and Taylor & Francis have emphasized disclosure where AI is used, human accountability, and strict confidentiality controls—often prohibiting uploading unpublished manuscripts into generative AI tools. However, empirical evidence has suggested nontrivial, and potentially growing, undisclosed large language model (LLM)-assisted text in peer review in some conference contexts. This report concluded that AI should serve as an augmentative rather than substitutive technology in peer review, with robust governance frameworks, transparent disclosure mechanisms, and continuous evaluation of equity implications essential for responsible implementation.
Aras Bozkurt· International Review of Rese...· 0 citations