1st revised baseline text for X.dpidm-aAI: Terminology and design guidelines for Agentic AI identity management
ITU-T 1ST Revised baseline text for XSTR.gidi : Globally Interoperable Digital Identity (including Humans/Enterprise/Non-Humans i.e. Agentic AI)
ITU-T SG17 (Study Period 2025) Temporary Document 201-WP1
AI Transformation & Compliance Expert
This standard defines additional requirements for the Autonomous Robot (AuR) ontology to address reasoning on multiple robots and extending the baseline ontology defined in IEEE 1872.2-2021. Ontological concepts and domain-specific axioms are defined for (a) representation, reasoning, and behaviors for autonomy, (b) multiple autonomous robots, (c) artificial intelligence and machine learning for autonomous robots, (d) affordances. The standard also describes use cases and case studies for the design of AuR.
This standard defines mandatory and optional requirements and constraints that need to be satisfied for an AI method, algorithm, application or system to be recognized as explainable. Both partially explainable and fully or strongly explainable methods, algorithms and systems are defined.
This document provides an overview of synthetic data concepts, methods, uses and considerations in the context of AI systems.
The work item will be a domain-specific focus application of the EN 304 223 principle for base cybersecurity requirements for AI models and systems to Generative AI. The TS will focus on the subject of deepfakes, which includes the issues of misinformation, disinformation, loss of confidentiality, copyright, IPR and the training/source material for LLMs. Also, this TS will include more prescriptive specifications for this domain extension to EN 304 223 when necessary.
As an AI specialist and domain expert, I wish to contribute to the standardization efforts of the standardization working group tasked with defining future standards for dataset quality and governance under the EU AI Act.
