The paper argues that AI governance should rely on ISO-like interoperability protocols rather than solely on jurisdiction-specific laws. It proposes standardized AI nutrition labels that include metrics for bias, energy usage, and data provenance to enable machine‑readable risk communication across borders. These protocols aim to reduce regulatory fragmentation, lower barriers for SMEs, and build public trust while allowing modular evolution with technology.
By Azmine Toushik Wasi, Mst Rafia Islam, Mahfuz Ahmed Anik, Taki Hasan Rafi, Md Manjurul Ahsan, Dong-Kyu Chae
arXiv:2606. 01417v1 Announce Type: new Abstract: Turkey's e-Government Gateway (e-Devlet) serves over 68 million registered users with more than 9,200 government services, and is increasingly integrating artificial intelligence into citizen-facing applications such as chatbot assistants and eligibility assessments.
By Ahmet Kaplan
The paper reviews secondary studies and research agendas on generative AI (GenAI) in information systems, synthesizing evidence from 28 selected papers. It identifies GenAI’s transformative benefits—productivity, innovation, personalization, and democratized expertise—while highlighting challenges such as technical unreliability, ethical risks, and governance gaps. The authors propose a research agenda that shifts IS scholarship toward shaping the co‑evolution of AI capabilities with organizational routines, societal values, and regulatory institutions, emphasizing hybrid human‑AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
By Aleksander Jarz\k{e}bowicz, Adam Przyby{\l}ek, Jacinto Estima, Yen Ying Ng, Jakub Swacha, Beata Zielosko, Lech Madeyski, Noel Carroll, Kai-Kristian Kemell, Bartosz Marcinkowski, Alberto Rodrigues da Silva, Viktoria Stray, Netta Iivari, Anh Nguyen-Duc, Jorge Melegati, Boris Deliba\v{s}i\'c, Emilio Insfran
arXiv:2609.37109v1 Announce Type: cross
Abstract: The rapid, unpredictable advancements in AI system capabilities has seen regulators take adaptive and experimental approaches to policymaking. Establ...
By Idoia Landa-Oregi, Tom Deckenbrunnen, Alessio Buscemi, Daniele Pagani, German Castignani
arXiv:2607. 07612v1 Announce Type: cross Abstract: Artificial intelligence is rapidly evolving from generative systems to agentic AI capable of autonomously planning and executing tasks.
By Mubarak Raji, Masooda Bashir
arXiv:2606. 08323v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these distributed architectures, which have complexities of inter-agent coordination and orchestration.
By Suchismita Naik, Samir Passi, Mihaela Vorvoreanu, Scott Saponas, Amanda Hall
arXiv:2509. 25397v2 Announce Type: replace-cross Abstract: The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem in artificial intelligence (AI).
By Johan Lin{\aa}ker, Cailean Osborne, Jennifer Ding, Ben Burtenshaw
arXiv:2606. 16167v1 Announce Type: new Abstract: AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs.
By Rashid Mushkani
arXiv:2606. 14594v1 Announce Type: cross Abstract: AI-assisted software development has moved from line-level autocomplete to agents that can plan changes, edit files, and submit pull requests with limited human supervision.
By Jassem Manita, Aziz Amari
The essay reviews Matthijs Maas’s framework for global AI governance, highlighting the rapid, border‑less development of AI and the fragmented, non‑binding international responses. It argues that governance cannot rely on a single institutional blueprint but must account for the varied powers of states, international bodies, and private firms. The central challenge is shaping an evolving architecture amid actors with differing incentives and no shared plans.
By Simon Chesterman
arXiv:2601.04094v4 Announce Type: cross
Abstract: Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rap...
By Tom Deckenbrunnen, Alessio Buscemi, Marco Almada, Alfredo Capozucca, German Castignani
arXiv:2606. 26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems.
By Victor Frimpong