Bias in Healthcare ML: Why Accuracy Isn’t Enough

In healthcare machine learning, high accuracy does not guarantee fairness.

The Core Issue

Models learn from historical data.
If the data contains bias, the model inherits it.

Types of Bias
Representation bias (underrepresented groups)
Measurement bias (incomplete or inconsistent data)
Outcome bias (historical treatment disparities)
Why It Matters

A model may:

perform well overall
fail for specific populations

This creates unequal outcomes.

What to Do
Evaluate performance across subgroups
Use fairness metrics (e.g., demographic parity, equal opportunity)
Audit training data
Incorporate domain knowledge
Key Insight

In healthcare ML, fairness is not optional—it is part of model quality.

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