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Dictionary termTrack CStablev2026.2

Bias audit (model)

An audit specifically focused on disparate model performance across demographic, geographic, or contextual sub-groups, including testing for direct, proxy, and intersectional disparities.

ByCASRAI Editorial Board

· Last updated 21 May 2026

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Examples

Worked examples

Is an instance
A Gender Shades-style audit of a face-classification model reporting accuracy by skin-tone and gender.
Is an instance
A statutorily required NYC Local Law 144 bias audit of an employment-screening tool.

Counter-examples

Looks similar, but isn't

Not an instance
An aggregate accuracy report with no sub-group breakdown.
Not an instance
A model card section listing potential biases without measurement.

Editorial commentary

Bias audits measure fairness metrics (equal-opportunity, demographic parity, calibration parity, predictive-parity gaps) and qualitative failure-mode analyses across pre-specified groups. The choice of metric is itself ethically loaded (Chouldechova's impossibility theorems show that distinct fairness metrics generally cannot be simultaneously satisfied). NYC's Local Law 144 and several US-state laws now mandate bias audits for automated employment decision tools.

References

Buolamwini, Gebru, 'Gender Shades' (FAccT 2018); Chouldechova, 'Fair prediction with disparate impact' (Big Data, 2017); NYC Local Law 144.

Also known as

fairness audit · algorithmic bias audit

Machine-readable encodings

Use in your systems

JATS XML <role> element

xml

<role vocab="credit"
      vocab-identifier="https://casrai.org/dictionary/"
      vocab-term="Bias audit (model)"
      vocab-term-identifier="https://casrai.org/dictionary/term/bias-audit-model" />

Schema.org DefinedTerm (JSON-LD)

json

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  "name": "Bias audit (model)",
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  "description": "An audit specifically focused on disparate model performance across demographic, geographic, or contextual sub-groups, including testing for direct, proxy, and intersectional disparities.",
  "inDefinedTermSet": "https://casrai.org/dictionary/domain/ai-ml-research-outputs#set",
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  "sameAs": [
    "fairness audit",
    "algorithmic bias audit"
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  "license": "https://creativecommons.org/licenses/by/4.0/",
  "publisher": {
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  },
  "dateModified": "2026-05-21T02:22:50",
  "inLanguage": "en"
}

Referenced across the research world

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