A field guide to AI positions

Confidence calibration

Source is over 18 months old.

Publication dates and source age

Sources counted: 1

Newest dated source: 2017-08

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

Checking whether a system's stated probabilities match how often its predictions turn out right. [1]

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Limits & distinctions

High accuracy and good calibration are different properties. Confident wording in a chatbot reply is not itself a measured probability. [1]

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A fuller explanation

Illustrative example: among many predictions assigned an 80% chance of being correct, about 80% should be correct. Calibration concerns that match across cases, not certainty about one answer. [1]

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How it relates to the map

Atlas reading question: has a confidence number been checked against outcomes? [1]

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https://theaiatlas.org/ideas/calibration/

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Sources and what we read

  1. 1. On Calibration of Modern Neural Networks

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2017-08

    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 the proceedings abstract and publication metadata. The experiments concern image and document classifiers, not the reliability of a chatbot saying it is certain.

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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