Managing Unintended Bias in Medical AI: A Practical Framework for Health Care Leaders

Below is an overview of a recently published article in Harvard Data Science Review authored by Sam Tyner-Monroe and colleagues. Click to read the full text.

Health care organizations are adopting artificial intelligence (AI) at historic speed—from clinical decision support and risk prediction tools to generative AI systems that streamline documentation and patient communication. While these technologies promise greater efficiency and improved patient outcomes, they also introduce a growing and increasingly visible risk: unintended bias.

Below, we summarize risks, considerations and practical guidance through the lens of the new Unintended Bias Risk Matrix (UBRM), which helps health care organizations systematically assess potential bias risks across the AI lifecycle and determine when additional monitoring, validation or testing is warranted.

Why Unintended Bias in Medical AI Matters Now

Bias in health care has long been studied in the context of human decision‑making. What is newer and increasingly consequential is how AI systems can inherit, amplify or obscure those biases at scale.

AI systems are deeply sociotechnical: their behavior reflects choices made during design, development and deployment, as well as the social and institutional contexts in which they operate. Even when protected characteristics such as race or gender are excluded, AI systems may rely on proxies—such as ZIP code, language patterns or clinical documentation practices—that produce systematically different outcomes across patient groups.

At the same time, the regulatory landscape is tightening. U.S. federal and state authorities have emphasized accountability for discriminatory impacts arising from AI systems, yet most regulations stop short of offering detailed guidance on how organizations should assess bias risks in deployed AI use cases.

This leaves many health care leaders facing a practical question: Which AI systems pose meaningful bias risks—and what should we do about them?

Introducing the UBRM

The UBRM is a structured, qualitative framework designed to help organizations identify the potential for bias before harm occurs.

Determining if bias exists often requires costly data collection and testing resources: the UBRM saves on these costs with pre-deployment risk identification and prioritization. It helps organizations determine where unintended bias is most likely to arise, and which AI systems require closer scrutiny based on their use case, context, and impact.

The Matrix evaluates AI systems across five dimensions, spanning the full AI lifecycle:

1. Representative Data and Population Alignment. Do the system’s training data reflect the clinical and demographic characteristics of the population in which it will be used?
2. Potential for Proxy Discrimination. Have developers or deployers considered whether system inputs may act as proxies for protected characteristics?
3. Measurable Outcomes. Is the system optimizing for outcomes that are directly measurable—or is it relying on imperfect proxies that may distort equity?
4. AI System Evaluation. Was the system evaluated using data, benchmarks, and assumptions that align with the real‑world deployment context?
5. AI System Transparency. Do clinicians, administrators, and patients have sufficient information to understand what the system does, how outputs should be used, and where limitations or failure modes may exist?

Each dimension is scored as low, medium or high risk, allowing organizations to develop a clear, shared understanding of an AI system’s unintended bias risk profile for their unique context.

Importantly, a high score does not mean a system is biased. Instead, it signals that additional safeguards, such as local validation, monitoring or bias testing, should be considered.

Applying the UBRM: From Theory to Practice

To demonstrate real‑world applicability, the authors applied the UBRM to two common medical AI use cases:

• A machine‑learning–based sepsis prediction tool, used for clinical decision support
• A generative‑AI “AI scribe” system, designed to automate clinical documentation

These case studies illustrate a critical insight for health care leaders: bias risk is highly context‑dependent.

A tool that appears low risk in one environment may pose higher risks in another due to differences in patient populations, workflows, documentation practices or clinician reliance. Generative AI systems, while often considered “administrative,” may present particularly elevated risks due to opaque training data, emerging proxy variables and hard‑to‑measure downstream effects.

The UBRM enables organizations to make these distinctions explicitly—and to align governance, testing and monitoring actions with their risk appetite and regulatory exposure.

Strategic Takeaways for Health Care Decision‑Makers

For executives and boards overseeing AI strategy, the paper offers several practical takeaways:

• Bias risk assessment is not one‑size‑fits‑all. Governance decisions should reflect the system’s clinical impact, degree of automation and deployment context.
• Not every AI system needs the same level of testing. The UBRM helps prioritize limited resources by identifying which tools warrant deeper validation or continuous monitoring.
• “Fairness through unawareness” is not enough. Excluding protected characteristics does not eliminate bias if other inputs function as proxies.
• Transparency and training reduce downstream risk. Clear documentation, clinician education and thoughtful human‑in‑the‑loop design remain essential safeguards.
• Regulatory scrutiny rewards proactive governance. Demonstrating that an organization has a structured process for identifying and mitigating AI bias risks can be as important as technical performance metrics.

A Governance Tool—Not a Standalone Solution

The authors emphasize that the UBRM is not a diagnostic test and does not replace statistical bias audits, regulatory compliance efforts or broader responsible‑AI programs. Instead, it functions as a front‑end governance tool, helping organizations decide when and where those deeper interventions are necessary.

Used alongside existing frameworks—such as dataset documentation, model facts labels and ongoing performance monitoring—the UBRM provides health care leaders with a practical way to move from abstract AI ethics principles to operational decision‑making.

Looking Ahead

As AI becomes embedded across clinical, operational and administrative functions, managing unintended bias is no longer a theoretical concern—it is a core element of patient safety, equity and organizational risk management.

The UBRM offers health care organizations a structured, defensible approach to understanding these risks and acting proportionately. For leaders seeking to deploy AI responsibly while continuing to innovate, this framework provides a timely and actionable place to start.

The is available in the latest issue of the Harvard Data Science Review. For more information on how your organization can implement AI, please reach out to and Manatt’s team.