Audit Failure, Bias & Recovery: How to Restore Trust in ML Validation

This final session brings together the most critical—and most overlooked—dimension of ML validation:

👉 Trust

Even when systems are validated, audits can fail due to:

Bias in model behavior
Poor handling of failure scenarios
Inability to recover under pressure

This video focuses on:

Real audit failure scenarios
Bias and fairness risks (FDA-sensitive area)
Live recovery strategies during audit
🧠 Shankar’s View

Validation proves a system works.

But trust comes from proving it works for everyone, over time, under pressure.

🎯 What You’ll Learn
How bias becomes a regulatory risk
Why accuracy alone is not enough
How to evaluate fairness in ML systems
What to do when you make a mistake during audit
How to recover without losing credibility
How to re-anchor answers under pressure
📌 What Is Covered

✔ Bias and fairness in ML systems (regulatory perspective)
✔ Dataset representativeness (critical for clinical systems)
✔ Subgroup performance analysis
✔ Real case study: bias failure
✔ Why “overall accuracy” is not sufficient
✔ Audit failure scenario (contradiction exposure)
✔ Live recovery strategy (how to respond under pressure)
✔ Re-anchoring technique (point-in-time vs continuous validation)
✔ Trust-based validation thinking
✔ Final synthesis: validation → control → trust

Видео Audit Failure, Bias & Recovery: How to Restore Trust in ML Validation канала A Shankar rao