Creating AI Governance Dashboards for Healthcare Compliance Teams
Healthcare regulators care about *process fairness*—not just fair outcomes, but fair treatment at every step: data collection, training, deployment, and patient-facing decisions.
I measure disparity *within the target distribution* (e.g., if 30% of sepsis cases are female, does the training data reflect that?), not just raw demographics.
Most healthcare AI audits focus on recall parity, not equalized odds, because false-negatives have direct clinical consequences (missed sepsis = death).
If `avg_corr < 0.7`, explanations are essentially noise. In healthcare, SHAP instability > 0.3 triggers mandatory clinician review.
Harmonic mean guarantees that one failing lens drags the entire score down. This matches regulatory thinking: a safe but biased model is still non-compliant.
If you’re running >5 models, containerize each scoring pipeline with memory limits; SHAP explainer can silently OOM-kill your VM.