Health Equity Is Your Newest Performance Metric. Are You Auditing for Bias?
Algorithmic Bias is Justice Scaled by Technology
As a patient, I've witnessed how existing healthcare systems already struggle with equity. As an AI Governance expert, I know that AI is not a neutral tool; it is a powerful amplifier that can encode systemic inequities into logic. When an AI system is trained on non-representative data—such as algorithms that underperform on darker skin tones or that undervalued the needs of Black patients by using cost as a proxy for sickness—it is perpetuating injustice at scale.
The Performance Mandate for Bias Audits
This is why health equity is a non-negotiable performance metric—it is a matter of both moral obligation and clinical accuracy. An algorithm that performs well for one demographic but fails for another is, by definition, an unreliable and unsafe medical device.
To honor the principle of Justice, hospitals and developers must integrate mandatory bias audits and fairness assessments into their governance frameworks. This requires:
- Intentionally Inclusive Data Collection: Ensuring training data reflects the diversity of the population the AI will serve.
- Explainability: Demanding transparency in how algorithms arrive at conclusions that affect vulnerable populations.
- Multi-Stakeholder Review: Including patients and community representatives in the design and evaluation process.
The era of ignoring who is "falling outside algorithmic norms" is over. Your organization must prove, through auditable metrics and transparent governance, that its AI is designed to close health equity gaps, not widen them.
About Dan Noyes
Dan Noyes operates at the intersection of healthcare AI strategy and governance. After 25 years leading digital marketing strategy, he is transitioning his expertise to healthcare AI, driven by his experience as a chronic care patient and his commitment to ensuring AI serves all patients equitably. Dan holds AI certifications from Stanford, Wharton, and Google Cloud, grounding his strategic insights in comprehensive knowledge of AI governance frameworks, bias detection methodologies, and responsible AI principles. His work focuses on helping healthcare organizations implement AI systems that meet both regulatory requirements and ethical obligations—building governance structures that enable innovation while protecting patient safety and advancing health equity