AI bias & fairness
Dated sources are over 18 months old; other dates are unknown.
Publication dates and source age
Sources counted: 2
Newest dated source: 2022-03
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
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.
Open in the glossary Reading notes · Structured record
In plain language
Patterns in an AI system that can produce uneven or unfair outcomes for people. [1]
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Limits & distinctions
In mathematics, 'bias' can also mean an added parameter or a statistical error. That use is separate from a finding of unfair treatment. [1] [2]
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A fuller explanation
Bias can come from data, statistical methods, human judgments or institutions around an application. NIST emphasizes how these sources interact. An evaluation needs to examine the actual task and who may be affected. [1]
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How it relates to the map
Ask which groups and situations were tested, which measure was used and whose experience is missing. [1]
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https://theaiatlas.org/ideas/bias/
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Sources and what we read
1. Towards a Standard for Identifying and Managing Bias in Artificial Intelligence
Source is over 18 months old.
Publication dates and source age
Sources counted: 1
Newest dated source: 2022-03
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 publication metadata, the systemic/statistical/human bias taxonomy, contextual evaluation discussion and conclusion. Used to explain sources and assessment of bias; this does not establish a bias finding for any particular model or actor.
2. Neural networks: Nodes and hidden layers
Publication dates and source age
Sources counted: 1
Publication dates are unavailable.
Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.
Some publication dates are unknown; the newest dated source may not be the newest source overall.
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 explanations of connected layers, numerical weights and biases, and calculations. Did not run the embedded exercises. Used for the mathematical structure, not a claim that an artificial network reproduces a human brain.
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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