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
arXiv:2607. 13040v1 Announce Type: cross Abstract: This paper examines where final authority should sit once capable AI systems are embedded in organizational workflows.
By Zexun Wang
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:2608. 10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSecOps built for deterministic automation, across every scale of agency.
By Srinivas Telukunta, Georgios Nektarios Lilis, Lucio Baron
arXiv:2609.21192v1 Announce Type: new
Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate,...
By John Cuneo, David Chun, Gaurav Khanna
arXiv:2606. 16649v1 Announce Type: new Abstract: Agentic AI marks a new phase of enterprise automation.
By Christopner Koch, Joshua A. Wellbrock
arXiv:2606. 24224v1 Announce Type: new Abstract: Despite the extensive discussions of human-centric AI (HCAI) in Industry 5.
By Zhen-Yuan Ralph Liu (CUMT), Yu-Ting Wang (NFU), Jia-Jia Yan (NEOMA), Shivam Gupta (NEOMA), Mihalis Giannakis
arXiv:2608. 12352v1 Announce Type: cross Abstract: AI governance frameworks can be known, used, and implemented in form without becoming governance in practice.
By Joseph R. Simons, David A. Broniatowski
arXiv:2606. 12713v1 Announce Type: new Abstract: Claims that artificial general intelligence has already arrived and claims that it remains decades away are often defended from overlapping evidence.
By J. E. Aguilera Briones
Despite the extensive discussions of human-centric AI (HCAI) in Industry 5. 0, its effects on firms' idiosyncratic risks (IR) remains underexplored.
The paper introduces a design‑science framework for ensuring legacy, governance, and decision integrity in enterprise AI systems. It defines a normalized Legacy Score based on knowledge retention, governance, oversight, adaptability, feedback learning, and jurisdictional fidelity, along with Decision Confidence and Decision Risk models, authority‑aware retrieval, Decision Memory, Regulatory Change Velocity, and a federated regulatory knowledge‑graph architecture. The authors also propose eight AI Decision Integrity Rules, an evaluation protocol, and a reproducible computational demonstration using stress tests and Monte Carlo simulations to illustrate the framework’s properties.
By Shorab Sarker
Enterprise artificial intelligence is increasingly embedded in decisions that must remain lawful, explainable, adaptable, and accountable despite personnel turnover, model replacement, regulatory chan...