Audit Engagement
Bias, Fairness and Equity Evaluation
Identify whether your healthcare AI performs differently across patient populations. Available separately or as part of the Comprehensive Audit.
Overview
What this engagement does
A structured evaluation of whether a healthcare AI system produces statistically meaningful performance differences across protected and clinically meaningful subgroups. We use recognized fairness methodologies, document subgroup performance, and provide actionable recommendations for mitigating identified disparities. This is one of five components bundled into the Comprehensive Healthcare AI Audit — purchase it separately for a focused fairness review, or choose the full Comprehensive Audit to earn the AI Health Audit Verified distinction.
Typical timeline
5–8 weeks
Timelines are configurable estimates and depend on scope, system complexity, and evidence availability.
Who It's For
✓ Health plans concerned about equitable access to care
✓ Hospitals serving diverse patient populations
✓ AI developers validating models for fairness
✓ Compliance teams evaluating civil-rights and anti-discrimination exposure
✓ Quality leaders addressing health-equity priorities
Systems Covered
· Predictive risk models
· Clinical decision-support tools
· Prior-authorization and utilization-management systems
· Triage and routing algorithms
· Resource-allocation models
· Generative AI applications affecting patient communication
What Is Evaluated
✓ Subgroup performance across protected classes
✓ Subgroup performance across clinically meaningful strata
✓ Calibration and threshold effects
✓ Data representativeness and feature contribution
✓ Proxy-variable risk
✓ Cumulative impact on access to care
✓ Documentation of fairness testing performed by the developer
Deliverables
Subgroup performance analysis
Fairness metrics dashboard
Data-representativeness review
Identified disparities and severity ratings
Mitigation recommendations
Ongoing-monitoring framework
Frequently Asked Questions
Common questions
Can an audit prove a model is unbiased?
No. Bias evaluation can identify measured disparities across defined subgroups, but cannot prove the absence of all bias. Findings describe what was measured, what was found, and the methodological limitations of the analysis.
Which subgroups do you evaluate?
We evaluate subgroups relevant to the use case, available data, and applicable legal framework. This typically includes protected classes under civil-rights law and clinically meaningful strata such as age, comorbidity burden, and payer mix.
Do you provide de-biased models?
No. We are independent of model development. We identify disparities and recommend mitigation strategies, but we do not modify production models. Remediation is the responsibility of the model owner.
Related Services
Flagship — Earns AI Health Audit Verified
Comprehensive Healthcare AI Audit
The complete package — bundles clinical, prior auth, bias, governance, and vendor evaluation into one integrated audit. The only way to earn the AI Health Audit Verified distinction.
Typical timeline: 8–14 weeks
Standalone or bundled
Clinical and Utilization Management Validation
Independent validation that AI recommendations align with clinical evidence and coverage criteria. Available separately or as part of the Comprehensive Audit.
Typical timeline: 6–10 weeks
Requires prior Comprehensive Audit
Continuous Monitoring and Reassessment
Ongoing oversight after your Comprehensive Audit is complete. Requires a prior Comprehensive Audit engagement.
Typical timeline: Ongoing; quarterly and annual cadence
Ready to scope a Bias & Equity engagement?
Start with a consultation. We will confirm scope, required evidence, timeline, and pricing before any commitment.
Independent healthcare AI auditing, governance, compliance education, and enterprise training.
● Founded in 2023
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© 2026 AI Health Audit. All rights reserved. · AI Health Audit provides independent audit, advisory, and educational services. We do not provide legal advice, compliance determinations, or guarantees of regulatory outcomes. Findings are educational and informational.
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