A field guide to AI positions

Robustness

Source is over 18 months old.

Publication dates and source age

Sources counted: 1

Newest dated source: 2023

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

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In plain language

Maintaining useful performance when conditions change, rather than only succeeding in one test setup. [1]

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Limits & distinctions

Robustness is broader than resisting deliberate attacks. A system can handle one kind of change and fail on another; the tested conditions need to be named. [1]

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A fuller explanation

Robustness asks how performance holds up across variations. For example, test a document reader on blurred scans as well as clean ones. The relevant changes depend on the intended use. [1]

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How it relates to the map

Atlas reading question: which difficult conditions were included in a claim that a system is reliable? [1]

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https://theaiatlas.org/ideas/robustness/

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Sources and what we read

  1. 1. AI Risks and Trustworthiness

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2023

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read sections 3.1 to 3.3 of the online AI RMF 1.0 excerpt: validity, reliability, robustness, safety and security. Guidance and definitions are not certification of a particular system.

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

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