Overview - ISO/IEC TR 24029-1:2021 (AI - Robustness of Neural Networks - Part 1: Overview)
ISO/IEC TR 24029-1:2021 is a technical report that provides a comprehensive overview of existing methods to assess the robustness of neural networks. It summarizes concepts, workflows and background knowledge rather than prescribing normative requirements. The report focuses on robustness as the ability of an AI system to maintain performance under varying input or operating circumstances and highlights the particular challenges neural networks present (non-linearity, limited explainability, domain shifts).
Key topics and technical coverage
- Robustness concept and definitions
- Clear definitions for terms used in robustness assessment (e.g., neural network, input data, testing, validation, robustness).
- Typical workflow to assess robustness
- Steps such as stating robustness goals, planning tests, defining metrics and test data, and documenting a testing protocol for stakeholders.
- Classification of assessment methods
- Three main classes are described:
- Statistical methods - performance metrics, sampling strategies and statistical measures for interpolation and classification.
- Formal methods - approaches that use proofs, solvers, optimization and abstract interpretation to guarantee properties (e.g., interpolation stability, maximum stable perturbation regions).
- Empirical methods - field trials, a posteriori testing and benchmarking under realistic conditions.
- Robustness metrics and measurement
- Overview of available statistical metrics and contrastive measures used to quantify robustness for different AI tasks.
- Supporting material
- Informative annexes on data perturbation and the principle of abstract interpretation; bibliography for further reading.
- Limitations and research context
- Notes that characterizing robustness for neural networks is an open research area and that combined approaches are commonly used.
Practical applications and target users
This report is useful for:
- AI system architects and machine learning engineers planning robustness validation and testing
- Safety and quality assurance teams integrating AI into regulated systems (automotive, aerospace, healthcare)
- Test engineers designing testing protocols, metrics and field trials for neural-network-based components
- Researchers and tool developers working on robustness metrics, formal verification tools and benchmarking suites
Practical uses include selecting appropriate methods (statistical/formal/empirical), defining robustness objectives, preparing test data/benchmarks, and documenting validation evidence.
Related standards and references
- References in the report include industry standards such as ISO 26262 (automotive functional safety) and other ISO/IEC/IEEE guidance on testing and validation (e.g., ISO/IEC/IEEE 16085, ISO/IEC 25000). These contextualize how robustness assessment integrates into system-level assurance.
Keywords: ISO/IEC TR 24029-1:2021, AI robustness assessment, neural network robustness, robustness testing, statistical methods, formal verification, empirical testing, abstract interpretation, data perturbation.