In today’s age of artificial intelligence, algorithms are embedded in nearly every part of our lives. They shape the media we consume, the jobs shown, and even the decisions our doctors make. It’s crucial to understand that AI bias isn’t just a matter of an algorithm getting something slightly wrong, like a navigation app suggesting a longer route. Instead, it can have real-world consequences, impacting opportunities and outcomes for individuals and groups profoundly.
Healthcare algorithms are increasingly being used to guide treatment plans, allocate resources, and prioritize patients. On the surface, this sounds like progress: letting data-driven tools take the lead to remove human error and subjectivity. However, it is imperative to recognize that data are not neutral artifacts; rather, they reflect the societies from which they are derived. Consequently, when historical and persistent racial inequities are embedded within these societal structures, analytical tools constructed from such data invariably risk perpetuating and amplifying these existing biases.
A widely cited study by Obermeyer et al. (2019) found that a risk-prediction algorithm used to guide healthcare decisions significantly underidentified Black patients. Only 18% of those flagged as needing high-risk care were Black, even though the actual proportion should have been closer to 46%. This wasn’t a minor glitch. It was a life-threatening distortion. These kinds of errors don’t just skew results; they determine who gets access to care, who is prioritized, and ultimately, who survives.
Healthcare Algorithms Are At Risk to Fail Black Patients
Despite their promise, many healthcare algorithms are failing patients of color because they were never designed with them in mind. As Fierce Healthcare reports, algorithms trained mostly on white populations often perform poorly when applied to other racial groups. Facial recognition systems, for example, misclassified darker-skinned women up to 35% of the time, while error rates for lighter-skinned men were less than 1% (Zou & Schiebinger, 2018).
These aren’t harmless errors. In a medical context, they mean delayed diagnoses, misdiagnoses, or being deprioritized entirely. Even when Black patients present with more advanced disease symptoms, they’re often referred to care at lower rates than their white counterparts (Ledford, 2019). If algorithmic recommendations were truly equitable, nearly half—46.5%—of Black patients would receive appropriate specialty care. Today, that number sits at just 17.7%.
Human Bias Becomes Machine Bias
The most dangerous myth surrounding AI is the idea that algorithms are neutral. In reality, they are anything but. Algorithms learn from the data they’re given—and that data is shaped by the people who collect, code, and interpret it. As Williams et al. (2020) wrote in Colorblind Algorithms: Racism in the Era of COVID-19, “when you put racist data in, you get racist outcomes.” Their research reinforced what Black communities have long known: structural racism doesn’t disappear with technology. It adapts.
Even the design of medical tools reflects this problem. Pulse oximeters, which estimate oxygen levels, have been found to perform less accurately on darker skin tones (Obermeyer et al., 2021). This isn’t just a design oversight. It’s a failure to prioritize inclusivity at the most basic levels of product development, and it’s one that can result in inadequate treatment during critical moments, such as respiratory illness or surgery.
This Is a Management Problem.
Bias in healthcare algorithms isn’t just a tech problem. It’s a leadership problem. At every stage – data collection, algorithm design, model training, testing, and deployment – bias can be introduced, often without detection. That’s why leaders must treat algorithm equity not as an optional feature, but as a strategic imperative.
As Panch et al. (2019) explain, simply removing protected characteristics like race from datasets doesn’t eliminate bias. In fact, it often obscures it. Without a commitment to diversity, ethics, and equity baked into every phase, flawed outcomes are inevitable.
What are some steps that leaders can take? Start by building more diverse teams, not just data scientists, but ethicists, clinicians, and cultural experts who can flag blind spots before they become dangerous errors. Ensure that algorithms are tested on representative populations. Regularly audit performance along demographic lines. And most importantly, treat bias correction as an ongoing process, not a one-time fix.
Embracing Equity as a Design Principle
Fixing algorithmic bias in healthcare won’t happen overnight, but there are immediate steps organizations can take:
- Diversify data and design – Use training data that reflects racial, ethnic, and gender diversity. Test algorithms thoroughly across those groups before rolling them out.
- Include underrepresented voices at every stage – From ideation to implementation, involve people from diverse backgrounds who can identify harmful assumptions early.
- Audit and act – Bias detection should be built into regular performance checks. Equity should be treated as a core metric of success, not a PR checkbox.
As Norori et al. (2021) emphasized in Patterns, algorithmic fairness isn’t just a technical issue. It’s a moral obligation. That means leaders must step up, not just as executives, but as stewards of equity.
Looking Ahead
Bridging the racial healthcare gap demands more than intention, it demands infrastructure change. We need deeper research, interdisciplinary collaboration, and institutional accountability. AI developers shouldn’t be building these tools in isolation. Doctors, nurses, patient advocates, community leaders, and data scientists must be at the table, together.
For too long, healthcare systems have failed Black and brown communities. Biased algorithms are simply the latest iteration of a system built without them in mind. But here’s the difference: unlike human history, algorithms can be rewritten. And it’s time we start.
