The AI Risk Ontology (AIRO) is an open-source formal ontology that provides a minimal set of concepts and relations for modelling AI use cases and their associated risks. AIRO has been developed according to the requirements of the EU AI Act and international standards, including ISO/IEC 23894 on AI risk management and ISO 31000 family of standards.

Methodology extending current experiences on the characteristics of 'adaptive networks' such as virtualization, self-organization, self-configuration, self-optimization, self-healing and self-learning offer huge advantages in future networks.

This Work Item will address the aspects of gradual implementation of networked intelligence and specify a categorization framework for systems based on several different levels.

The Work Item will identify and describe use cases and scenarios that are enabled with enhanced experience, through the use of network intelligence.

This document will discuss various design options, in terms of a set of new stand-alone and/or nested Functional Blocks, for using intent with the ENI System Architecture.

This Work Item specifies a framework for use within ETSI ENI ISG to coordinate and promote public demonstrations of Proof of Concept (PoC) validating key technical components developed in ENI.

The present document will describe the motivation, requirements, and challenges of using flow-oriented on-path telemetry techniques which provides relevant measurement or event reports to the AI-enabled network entities.

The purpose of this work item is to draft a GS to revise and enhance the specification of the software functional architecture of ENI.

This Work Item will provide terms and definitions used within the scope of the ISG ENI, in order to achieve a "common language" across all the ISG ENI documentation.

This Recommendation provides a framework for data handling to enable machine learning (ML) in future networks including International Mobile Telecommunications (IMT)-2020.

This Recommendation describes the framework for the AI-assisted analysis of network slicing in IMT-2020 networks.

This Recommendation specifies a functional framework for network service provisioning based on artificial intelligence (AI) in future networks, including international mobile telecommunication-2020 (IMT-2020).

Standard for Ontologies for Robotics and Automation, to represent additional domain-specific concepts, definitions, and axioms commonly used in Autonomous Robotics (AuR).

This document establishes terminology for AI and describes concepts in the field of AI.

This document defines a set of processes and associated concepts for describing the life cycle of AI systems based on machine learning and heuristic systems.

This Recommendation provides high-level requirements and the architecture for integration of ML marketplaces in future networks including IMT-2020.

The scope of the present document is to provide the definition of the Generic Autonomic Network Architecture (GANA) as an architectural reference model for autonomic networking, cognitive networking and self-management that addresses the requirements defined in ETSI TS 103194 - a compilation of example requirements which reflect realworld problems that benefit from the application of automated management, autonomic management and selfmanagement principles for networks and services delivered by the network to applications.

This Supplement analyses use cases for machine learning in future networks including IMT-2020, and presents them in a unified format.