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ISO/IEC 24029-2

ISO/IEC 24029-2 is the ISO/IEC International Standard for using formal methods to assess robustness properties of neural networks.

Category: AI evaluation / standards Published: July 10, 2026 Modified: July 10, 2026 Last reviewed: July 10, 2026 Tags: ISO, IEC, formal methods, robustness, neural networks, AI evaluation

Definition

ISO/IEC 24029-2:2023 is titled Artificial intelligence (AI) - Assessment of the robustness of neural networks - Part 2: Methodology for the use of formal methods. ISO lists it as reference number ISO/IEC 24029-2:2023, Edition 1, a 23-page International Standard published in August 2023.

ISO's public abstract says the document provides methodology for using formal methods to assess neural-network robustness properties. It focuses on selecting, applying, and managing formal methods to prove robustness properties. That makes the standard narrower and more technical than ISO/IEC TR 24029-1, which gives a broader overview of existing robustness-assessment methods.

Status

As reviewed on July 10, 2026, ISO lists ISO/IEC 24029-2:2023 as published, with publication stage 60.60. ISO's lifecycle record shows the project approved on June 11, 2020, committee-draft work in 2021 and 2022, a Draft International Standard ballot in 2022, DIS approval for FDIS registration on January 9, 2023, final text received on January 11, 2023, proof activity in March and May 2023, and publication on August 1, 2023.

ISO lists ISO/IEC JTC 1/SC 42 as the technical committee, and the SC 42 committee page describes that subcommittee's scope as standardization in artificial intelligence. The ISO page classifies the standard under ICS 35.020, the broad information-technology classification.

Formal Methods Surface

The distinctive feature of ISO/IEC 24029-2 is its focus on formal methods. In this context, the relevant question is not simply whether a neural network performed well on a test set. The question is whether a specified robustness property can be proven, under stated assumptions, for a defined model, input region, perturbation type, and property of interest.

That is useful because robustness claims often slide between empirical evidence and stronger assurance language. A test can show that a model survived a sample of disturbances. A formal method can, when applicable, support a claim about all cases inside a defined mathematical boundary. The boundary is the point. A formal proof that holds for one model, one property, and one input region should not be advertised as proof that the deployed product is broadly safe.

Assurance Use

ISO/IEC 24029-2 should be read as an assurance aid, not as a shortcut around evaluation. Formal methods can help when the robustness property is precise enough to state and the model or subsystem is tractable enough to analyze. They are especially important in high-stakes settings where ordinary testing leaves too much uncertainty about rare or adversarial inputs.

For governance, formal robustness assessment has to connect back to the system's purpose. A proof about a classifier's behavior under bounded input perturbations may be relevant to an autonomous vehicle perception stack, a medical imaging model, a fraud detector, or a content-filtering system, but it is never the whole assurance case. The product also includes sensors, preprocessing, thresholds, monitors, human handoff, data drift, security controls, and organizational decisions about when the model is allowed to operate.

Evidence Record

An ISO/IEC 24029-2-informed record should identify the neural network, model version, formal property, assumptions, input domain, perturbation model, proof or verification method, tool chain, reviewer, result, limits, and decision consequence. It should say exactly what was proven, what was not proven, and what changes would invalidate the assessment.

The record should also preserve failed or partial formal assessments. A proof obligation that cannot be discharged may be as important as a successful proof, because it names a limit on the assurance claim. Formal-methods evidence decays when model weights, preprocessing, input bounds, safety constraints, runtime monitors, or deployment context change.

Boundary With Other Standards

ISO/IEC 24029-2 is not a full AI management-system standard, risk-management framework, or impact-assessment method. It sits beside broader references. ISO/IEC TR 24029-1 gives the robustness-assessment overview, ISO/IEC 23053 helps describe the ML-based AI system, ISO/IEC 23894 gives AI risk-management guidance, and NIST AI TEVV covers testing, evaluation, verification, and validation vocabulary.

Source Discipline

Use the official ISO page for the title, reference number, International Standard status, publication date, stage, edition, page count, technical committee, ICS classification, public abstract, and lifecycle dates. Use the ISO/IEC JTC 1/SC 42 page for committee scope. Do not cite vendor summaries or training pages for the standard's formal status. Do not treat ISO/IEC 24029-2 as a certification mark, universal robustness guarantee, product approval, or legal safe harbor.

Spiralist Reading

Spiralism reads ISO/IEC 24029-2 as a discipline against vague mathematical authority. Formal methods can be powerful, but they can also become a prestige layer that makes a narrow proof sound like institutional certainty. The standard is most useful when it forces the claim to be small enough to inspect.

The danger is proof theater. A team can cite formal verification while leaving the deployment pipeline, operating context, and organizational consequences unexamined. The stricter reading is that every formal robustness claim should point to a versioned model, a formal property, assumptions, tooling, result, limit, owner, and retest trigger.

Open Questions

Sources

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