Comparing Bias Reduction Strategies to Improve Healthcare AI Fairness
Author: Neeley Jo Minor | Founder, EnBloc Co™
🧠 Introduction
Healthcare AI has immense potential to advance diagnosis, treatment, and access. However, algorithmic bias can lead to harmful disparities—especially for women, racial minorities, and individuals with chronic illness. EnBloc AI™ is committed to identifying and integrating effective bias reduction strategies to ensure fair, accountable AI.
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📉 Strategy Comparison Table
| Strategy Type | Description & Methods | Strengths | Weaknesses | Impact on Fairness & Performance |
|---|---|---|---|---|
| Pre-processing | Modifies training data (e.g., resampling, reweighting) | Easy to apply before training; addresses data imbalance | May slightly lower accuracy | Improves fairness; useful when retraining model is difficult |
| In-processing | Adds fairness constraints or training methods (e.g., adversarial learning) | Directly optimizes fairness; can tailor per subgroup | Requires more resources and group data | Strongest fairness gains; small performance tradeoffs |
| Post-processing | Adjusts predictions or thresholds after model training | Works on deployed models; minimal disruption | Limited by pre-existing model bias | Helpful for tuning; less effective on deep-rooted bias |
| Human-in-the-loop | Human reviews and feedback at key AI development points | Adds nuance, context, and ethical oversight | Time and resource intensive | Enhances trust and captures subtle bias |
| Transparency | Model explainability and decision traceability | Builds trust; allows for bias identification | Doesn’t fix bias alone; explanations may be unreliable | Supports fairness through auditability and accountability |
| Stakeholder Input | Involves patients, advocates, and clinicians throughout design and deployment | Ensures real-world relevance and equitable goals | Slower development; resource heavy | Critical for ethical, inclusive AI |