This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicit-organizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledge-representation, thereby increasing range of utility, scope of influence (in terms of differentiating class sectors) and operational efficiency. It will finish with a discussion of these and other historical implications.
OntoMetric: An Ontology-Driven LLM-Assisted Framework for Automated ESG Metric Knowledge Graph Generation
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Faculty of Sciences, Engineering and Technology, The University of Adelaide（阿德莱德大学科学、工程与技术学院）；School of Information Systems and Technology Management, University of New South Wales（新南威尔士大学信息系统与技术管理学院）
We introduce the needs for explainable AI that arise from Standard No. 239 from the Basel Committee on Banking Standards (BCBS 239), which outlines 11 principles for effective risk data aggregation and risk reporting for financial institutions. Of these, explainableAI is necessary for compliance in two key aspects: data quality, and appropriate reporting for multiple stakeholders. We describe the implementation challenges for one specific regulatory requirement:that of having a complete data taxonomy that is appropriate for firmwide use. The constantly evolving nature of financial ontologies n…
Unbox Responsible GeoAI: Navigating Climate Extreme and Disaster Mapping
Department of Geography, National University of Singapore（新加坡国立大学地理系）；GIScience Research Group, Heidelberg University（海德堡大学地理信息科学研究组）；HeiGIT at Heidelberg University（海德堡大学HeiGIT机构）
ABBYY；RWTH Aachen University（亚琛工业大学）；XPAN Law Partners（XPAN律师事务所）
The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability…
Subject Roles in the EU AI Act: Mapping and Regulatory Implications
Studio Legale Fabiano (Italy) - Affiliation: International Institute of Informatics and Systemics (IIIS) - USA (September 28, 2025)（法布iano法律事务所（意大利）- 机构：国际信息与系统研究所（IIIS）- 美国（2025年9月28日））
Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws
Computational Intelligence and Operations Laboratory (CIOL)（计算智能与运筹实验室（CIOL））；Shahjalal University of Science and Technology（沙贾拉尔科技大学）；Independent University（独立大学）；Hanyang University（汉阳大学）；University of Oklahoma（俄克拉荷马大学）
Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown that these risks can range anywhere from regulatory, compliance, reputational, and user trust, to financial and even societal risks. Depending on the nature and size of the organization, AI technologies can pose a significant risk, if not us…
Strategic Governance of AI Models in Earth Science
University of Utah（犹他大学）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；University of Bristol（布里斯托大学）；Stanford University（斯坦福大学）；Center for Astrophysics | Harvard & Smithsonian（哈佛-史密森天体物理中心）；GESTAR II, NASA Global Modeling and Assimilation Office（GESTAR II，美国国家航空航天局全球建模与同化办公室）；University of Southern California（南加州大学）；University of Reading（雷丁大学）
