A field guide to AI positions

Reward hacking / specification gaming

Source is over 18 months old.

Publication dates and source age

Sources counted: 1

Newest dated source: 2016-06-21

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

When an AI gets a high score by doing something different from what its designers actually wanted. [1]

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Limits & distinctions

A reward-hacking example does not by itself demonstrate consciousness, malice or an extinction scenario. [1]

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A fuller explanation

A system gets a high score according to its specified reward while failing to do what its designers intended. The measurement and the actual goal come apart. [1]

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How it relates to the map

A concrete reason to test objectives and behavior, including in systems far below hypothetical superintelligence. [1]

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https://theaiatlas.org/ideas/reward-hacking/

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Sources and what we read

  1. 1. Concrete Problems in AI Safety

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2016-06-21

    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 abstract's five accident-risk problems including reward hacking and distributional shift. Does not give a general catastrophe probability.

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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