An ontological approach to foster the convergence, interoperability and operationalization of frameworks for Trustworthy AI

• URL: http://arxiv.org/abs/2604.11033v1
• Date: Mon, 13 Apr 2026 05:57:40 GMT
• Title: An ontological approach to foster the convergence, interoperability and operationalization of frameworks for Trustworthy AI
• Abstract summary: AI-Ethics Ontology (AI-EO) provides an abstracted semantic infrastructure to foster the convergence, interoperability and operationalization of different frameworks for Trustworthy AI.<n>The version 1.0 of the Ontology is freely available and has been designed to be conceptually close to target applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI systems are consistently evolving in terms of both capability and autonomy with an holistic social impact. In this context of proliferation and fast technological evolution, the scientific community is actively engaged to assure Trustworthy AI. However, in general terms, AI safety research is significantly slower and is facing critical challenges in terms of strategy, consensus and operationalisation. This paper presents AI-Ethics Ontology (AI-EO) which, by leveraging Semantic Technologies on the Web infrastructure and ontology-based knowledge representations, provides an abstracted semantic infrastructure to foster the convergence, interoperability and operationalization of the different frameworks for Trustworthy AI. The current implementation results from the analysis of two relevant case studies to establish a dynamic development process in fact, as well as to enable its iterative evolution according to a formally-defined methodology. The version 1.0 of the Ontology is freely available and has been designed to be conceptually close to target applications, in a context of interoperability, adaptability as a natural response to change and usability.

Related papers

• Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles [41.419459280691605]
  Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT.<n>This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness.
  arXiv  Detail & Related papers  (2025-10-24T09:40:38Z)
• AI Exchange Platforms [1.143498094481099]
  The rapid integration of Artificial Intelligence into organizational technology frameworks has transformed how organizations engage with AI-driven models.<n>The importance of structured platforms for AI model exchange has become paramount for organizational efficacy and adaptability.<n>This paper serves as a critical resource for understanding the dynamic interplay between technology, business models, and user engagement in the rapidly growing domain of AI model exchanges.
  arXiv  Detail & Related papers  (2025-10-07T19:19:25Z)
• A cybersecurity AI agent selection and decision support framework [0.0]
  This paper presents a novel, structured decision support framework that aligns AI agent architectures, reactive, cognitive, hybrid, and learning.<n>By integrating agent theory with industry guidelines, this framework provides a transparent and stepwise methodology for selecting and deploying AI solutions.
  arXiv  Detail & Related papers  (2025-10-02T07:38:21Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• Conceptual Framework Toward Embodied Collective Adaptive Intelligence [11.063451220531585]
  Collective Adaptive Intelligence (CAI) represent a transformative approach in embodied AI.<n>This article introduces a conceptual framework for designing and analyzing CAI.
  arXiv  Detail & Related papers  (2025-05-29T06:43:14Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems [132.77459963706437]
  This book provides a comprehensive overview, framing intelligent agents within modular, brain-inspired architectures.<n>It explores self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities.<n>It also examines the collective intelligence emerging from agent interactions, cooperation, and societal structures.
  arXiv  Detail & Related papers  (2025-03-31T18:00:29Z)
• TOAST Framework: A Multidimensional Approach to Ethical and Sustainable AI Integration in Organizations [0.38073142980732994]
  This paper introduces the Trustworthy, Optimized, Adaptable, and Socio-Technologically harmonious (TOAST) framework.<n>It focuses on reliability, accountability, technical advancement, adaptability, and socio-technical harmony.<n>By grounding the TOAST framework in healthcare case studies, this paper provides a robust evaluation of its practicality and theoretical soundness.
  arXiv  Detail & Related papers  (2025-01-07T05:13:39Z)
• Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
  We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER) SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
  arXiv  Detail & Related papers  (2024-04-07T07:05:59Z)
• Levels of AGI for Operationalizing Progress on the Path to AGI [53.28828093836034]
  We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors.<n>This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to AGI.
  arXiv  Detail & Related papers  (2023-11-04T17:44:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.