ISO/IEC TR 24029-1:2021 addresses the assessment of the robustness of neural networks, focusing on the overview level of how robustness can be evaluated in practice. As a technical document, it is relevant to teams that need a structured view of technical assessment, documented evaluation, and verification activities for AI-based systems. ISO/IEC TR 24029-1:2021 can support engineering documentation, compliance workflows, and procurement review where confidence in model behavior and operational consistency is important.

Overview of ISO/IEC TR 24029-1:2021

ISO/IEC TR 24029-1:2021, Artificial Intelligence (AI) - Assessment of the robustness of neural networks - Part 1: Overview, provides a high-level technical reference for understanding how neural network robustness may be assessed. As a derived document connected to ISO/IEC TR 24029, it is best viewed as a supporting guide for technical review rather than a standalone specification. It is useful where organizations need a common basis for risk management, technical validation, and structured discussion of assessment methods before deeper testing or implementation decisions.

Compliance applications of ISO/IEC TR 24029-1:2021

This reference is practical for organizations evaluating AI-enabled systems in quality workflows, verification activities, and conformity assessment preparation. It may be used when defining review criteria for machine learning components, comparing assessment approaches, or documenting robustness-related expectations across development and testing teams. In procurement and engineering documentation, ISO/IEC TR 24029-1:2021 can help establish a clearer compliance reference for product evaluation, especially where technical validation and evidence-based review are needed before deployment or acceptance.

Importance of compliance with ISO/IEC TR 24029-1:2021

Using ISO/IEC TR 24029-1:2021 in compliance planning can improve consistency in how neural network robustness is discussed, reviewed, and recorded. That matters for safety-oriented applications, interoperability considerations, and risk reduction in systems that depend on AI decisions. The overview format can help teams align testing workflows, identify evaluation gaps, and support procurement decisions with better technical documentation. It also contributes to more disciplined conformity assessment preparation by giving stakeholders a shared reference for robustness-related technical assessment.

• High-level overview of robustness assessment for neural networks
• Supporting reference for technical review and evaluation planning
• Useful in documentation for risk management and quality assurance
• Relevant to verification activities and conformity assessment preparation
• Helps organizations align AI testing workflows with procurement and compliance needs