Dark Skin, Data Lies: Mississippi’s Hidden Health Bias.

By Franklin Everett Shaw

May 23, 2026

The numbers don’t lie, or so we’re told. But what happens when the numbers themselves are skewed, reflecting biases that perpetuate inequality? In Mississippi, a state already grappling with significant health disparities, the rise of algorithmic decision-making in healthcare presents a particularly thorny challenge. These algorithms, designed to predict risk and guide treatment, often rely on data that reflects historical and systemic biases, potentially exacerbating existing health inequities for Black Mississippians.

Algorithmic bias in healthcare isn’t some futuristic dystopia; it’s happening now, in hospitals and clinics across the country, including right here in Mississippi. These biases can creep into algorithms in several ways. The data used to train these algorithms might over-represent certain populations or under-represent others, leading to skewed predictions. Historical biases in medical practice, such as the under-diagnosis of pain in Black patients, can also be encoded into the algorithms.

Let’s consider diabetes risk assessment, a critical area in Mississippi, where diabetes rates are alarmingly high, particularly among the Black population. If an algorithm is trained on data that primarily includes white patients, it might underestimate the risk of diabetes in Black patients, leading to delayed diagnosis and treatment. This is because factors like socioeconomic status, access to healthy food, and environmental stressors, which disproportionately affect Black communities in Mississippi, might not be adequately accounted for in the algorithm.

Cardiac care is another area of concern. Algorithms used to predict the risk of heart disease or to determine eligibility for certain treatments might be biased against Black patients. For example, some algorithms rely on creatinine levels to estimate kidney function, which in turn affects dosage recommendations for certain heart medications. However, creatinine levels can vary based on race, and using a single equation for all patients can lead to inaccurate estimations and potentially harmful treatment decisions for Black Mississippians.

So, what can you do if you’re a Black Mississippian concerned about algorithmic bias in your healthcare? First, understand that you have the right to ask questions. Don’t be afraid to ask your doctor about the tools they’re using to assess your risk and guide your treatment. Specifically, ask if any algorithms are being used and, if so, how they account for racial and ethnic differences.

Second, request a detailed explanation of your individual risk score. Ask what factors contributed to the score and whether any of those factors are potentially biased. For example, if your risk score is based on factors like neighborhood income or access to transportation, consider whether those factors accurately reflect your individual circumstances.

Third, if you believe that an algorithm has unfairly assessed your risk, don’t hesitate to challenge the assessment. Ask for a second opinion from another healthcare provider. Document your concerns and consider filing a complaint with the hospital or clinic.

Here’s a step-by-step guide to advocating for yourself:

1. Gather Information: Before your appointment, research the conditions you’re being assessed for. Understand the risk factors and common diagnostic procedures.
2. Prepare Questions: Write down a list of questions to ask your doctor about the algorithms they use. Be specific and ask about potential biases.
3. Document Everything: Keep a record of your appointments, test results, and any concerns you have about algorithmic bias.
4. Seek a Second Opinion: If you’re not satisfied with the explanation you receive, seek a second opinion from another healthcare provider.
5. File a Complaint: If you believe you’ve been unfairly assessed, file a complaint with the hospital or clinic. You can also contact the Mississippi State Department of Health.

Finding culturally competent healthcare providers is also crucial. These providers are trained to understand and respect the cultural beliefs and practices of their patients. They are more likely to be aware of the potential for algorithmic bias and to take steps to mitigate its effects. Resources like the National Medical Association and the Mississippi Medical Association can help you find Black physicians in your area.

One of the biggest challenges in addressing algorithmic bias is the lack of transparency. Many algorithms are proprietary, meaning that their inner workings are hidden from the public. This makes it difficult to identify and correct biases. We need greater transparency in the development and deployment of healthcare algorithms.

Another challenge is the lack of diversity in the data science field. The people who design and build these algorithms often don’t reflect the diversity of the populations they’re intended to serve. This can lead to unintentional biases being baked into the algorithms. We need to encourage more people from underrepresented groups to enter the data science field.

Ultimately, addressing algorithmic bias in healthcare requires a multi-faceted approach. It requires greater transparency, more diverse data, and a commitment to cultural competence. It also requires patients to be informed and empowered to advocate for themselves.

Consider the case of a 55-year-old Black woman in Jackson, Mississippi, who was denied a potentially life-saving heart procedure because an algorithm deemed her too high-risk. After questioning the assessment and seeking a second opinion, she discovered that the algorithm was heavily weighted towards factors that disproportionately affect Black communities, such as poverty and lack of access to healthcare. By advocating for herself, she was able to overturn the decision and receive the treatment she needed.

This is just one example of how algorithmic bias can have a real and devastating impact on people’s lives. It’s a problem that we need to address head-on, not just in Mississippi, but across the country. We must demand more equitable AI in our healthcare systems, ensuring that these tools are used to improve health outcomes for all, not to perpetuate existing inequalities.

Remember, your health is your right. Don’t let biased algorithms stand in the way of getting the care you deserve.