What evidence should be kept?

Map every automated employment decision point and identify whether the tool substantially assists a human decision. Request vendor model documentation, audit summaries, data retention details, and accessibility information. Run employer-specific outcome testing for protected groups and keep sample-size limitations visible.

AI recruitment compliance

AI Recruitment Compliance Checklist

AI recruitment compliance is the operational discipline of knowing which automated tools affect employment decisions, testing their impact, publishing or retaining the required evidence, and monitoring changes over time.

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Where this fits

The recruiting stack includes sourcing intelligence, resume ranking, chatbot screening, video interview scoring, or assessments.

The company hires in multiple US states or in the EU and needs one internal control map.

Procurement wants to compare vendor risk before approving a new AI recruiting tool.

Operating steps

Map every automated employment decision point and identify whether the tool substantially assists a human decision.
Request vendor model documentation, audit summaries, data retention details, and accessibility information.
Run employer-specific outcome testing for protected groups and keep sample-size limitations visible.
Create notices, public summaries, or internal evidence packets where the applicable rules require them.
Schedule quarterly or release-based monitoring when thresholds, models, roles, or geographies change.

Common risks

Treating AI recruitment compliance as a vendor questionnaire instead of an employer control process.
Missing state or city obligations when remote candidates apply into regulated locations.
Not retesting after a vendor changes ranking logic, model weights, or assessment scoring.

How HireBias Audit connects

HireBias Audit gives recruiting, compliance, and legal teams one place to inventory tools, test impact, score risk, and export reports for review.

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