A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical veri�cation and ef�ciency, while judgments on scienti�c merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.
A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical verification and efficiency, while judgments on scientific merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.
An AI-enabled lifecycle of Creation, Transformation, Transmission, Evaluation, Evaluation, and Governance is proposed and an AI-eWOM fit perspective is developed and a TCCM-organized research agenda identifies priorities for future research.
A. Joyal· Journal of business and mana...· 0 citations
Peer review is central to scientific decision-making, yet it is rarely evaluated or audited at scale. Growing submission volumes and the increasing use of large language models (LLMs) in drafting reviews have introduced new challenges for transparency, accountability, and quality control. Today, peer reviews are often produced through hybrid human--AI workflows, where a reviewer may develop the core evaluative ideas while using an LLM to refine wording, restructure arguments, or improve fluency. This shift raises new questions beyond authorship detection alone: Are reviews constructive? Are reviewer claims grounded in the submitted paper? How can we quantify collaboration between human reasoning and AI-assisted writing, and distinguish whether the intellectual contribution or the surface text originates from humans or models? In this industry talk, we present Reviewerly's retrieval-centered infrastructure for auditing peer review at scale. We describe three deployed systems: Peeriscope, which evaluates review quality across multiple interpretable dimensions; Peerispect, which uses retrieval-augmented generation (RAG) to verify whether reviewer claims are supported by evidence in the manuscript; and PeerPrism, which analyzes hybrid human--AI authorship by disentangling the origin of ideas from the origin of text in peer reviews, enabling measurement of how human reasoning and AI-generated writing interact within a review. We will share architectural design decisions, lessons learned from real-world deployment, and practical trade-offs among model complexity, interpretability, computational efficiency, and predictive accuracy. The talk will include short live demonstrations1 2 of our systems to illustrate how the combination of retrieval systems and LLM-based pipelines can improve peer review quality and strengthen transparency and research integrity across high-volume decision-making environments.
Negar Arabzadeh, Sajad Ebrahimi, Alireza Daghighfarsoodeh et al.· Annual International ACM SIG...· 0 citations
The rapid adoption of large language models (LLMs) in financial services has generated a growing literature on “responsible AI” in domains such as investment analysis, credit assessment, risk management, compliance, and financial advisory systems. Unlike earlier AI systems, LLMs introduce responsibility challenges that differ in important ways from those addressed by earlier responsible AI frameworks, including hallucinations, prompt injection and manipulation, generative opacity, instruction-following failures, context sensitivity, output inconsistency, and emergent capabilities that arise only at larger model scales. While responsible AI concerns extend across AI systems more broadly, this review focuses specifically on LLMs, which since 2022 have become a major technological force reshaping financial services and have generated a distinctive body of responsibility discourse that existing frameworks are only beginning to address. However, the meaning of responsibility in this literature remains heterogeneous and inconsistently operationalized across studies. This paper provides a structured review of research on responsible LLMs in finance using a broad-to-narrow screening process across Web of Science and Scopus, reported with reference to the applicable PRISMA 2020 items. We combine a descriptive bibliometric overview with qualitative content analysis to examine the corpus. The descriptive overview covers publication trends, publication venues, disciplinary orientations, geographic distributions, and institutional affiliations, while the qualitative analysis codes the corpus on two dimensions: responsibility depth—whether responsibility is constitutive of the paper’s contribution, operative within its design, or peripheral to its framing—and responsibility mode—whether that engagement is normative, technical, evaluative, or mixed. The findings reveal a field concentrated in the Operative–Technical mode. Constitutive contributions cluster in Normative and Mixed modes, while a purely Evaluative orientation remains comparatively uncommon. Addressing these limitations requires moving beyond principle-level discourse toward institution-specific accountability mechanisms, empirical evaluation across clearly documented research and operational settings, and systematic alignment with relevant financial regulatory requirements. These efforts must address the distinctive challenges posed by LLMs, rather than only concerns inherited from the broader responsible AI literature.
The thematic analysis demonstrates that while AI tools offer significant educational benefits — such as learning personalization, enhanced language acquisition, and the automation of administrative workloads — they present critical pedagogical and ethical challenges.
Fatma Maamri· Journal of education and sci...· 0 citations
The public release of large language models (LLMs) in late 2022 has fundamentally altered the landscape of scholarly medical publishing. LLMs now permeate every stage of the academic publishing pipeline, from manuscript drafting and peer review to editorial decision-making, with evidence suggesting that at least 13.5% of biomedical abstracts published in 2024 showed detectable LLM involvement. This rapid adoption has intersected with pre-existing structural vulnerabilities, including escalating article processing charges, publish-or-perish incentives, a chronic peer reviewer shortage, and inadequate editorial resources, creating interconnected challenges that affect all stakeholders. The response from the scholarly publishing community has been substantial but fragmented. International standards bodies, such as the International Committee of Medical Journal Editors (ICMJE), Committee on Publication Ethics (COPE), World Association of Medical Editors (WAME), have established consensus principles prohibiting AI authorship and requiring disclosure, yet individual journals range from restrictive to actively encouraging in their AI policies, and detection-based enforcement approaches have proven fundamentally unreliable, with documented biases against non-native English speakers. This narrative review characterizes the structural challenges of LLM adoption in medical publishing, provides a systematic comparison of editorial policies across major medical journals and publishers, and examines the limitations of current detection and enforcement mechanisms. Rather than focusing on prohibition and policing, the review proposes a forward-looking framework centered on three concrete proposals: 1) a three-tiered disclosure system (assistive, augmentative, substantive) integrated with the CRediT taxonomy; 2) a four-stage artificial intelligence co-editor model for deploying LLM-based tools within editorial workflows under transparent governance principles; and 3) equity-conscious policy design with international coordination through a proposed global editorial summit. The framework aims to shift the editorial paradigm from reactive enforcement to proactive governance, addressing the operational reality that editors are overburdened and underresourced for the expanding expectations placed upon them.
Hyejin Joo, Munkhzul Radnaabaatar, Jaehun Jung· Journal of Korean medical sc...· 3 citations
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