Question 1

What is AI model drift and why does it matter?

Accepted Answer

AI model drift occurs when a model's performance degrades over time due to changes in input data distribution (data drift) or the relationship between inputs and outputs (concept drift). Drift matters because it can cause your AI to make increasingly biased or inaccurate decisions, leading to compliance violations and business losses. HAIEC's Compliance Twin detects drift using PSI (Population Stability Index) and KS tests, alerting you before performance degrades significantly.

Question 2

How does anomaly detection work for AI systems?

Accepted Answer

AI anomaly detection identifies unusual patterns in model behavior that may indicate problems. HAIEC uses Z-score analysis (detecting values more than 3 standard deviations from the mean) and IQR (Interquartile Range) methods to catch outliers. This includes detecting unusual prediction distributions, latency spikes, error rate increases, and bias metric changes.

Question 3

What compliance rules does Compliance Twin monitor?

Accepted Answer

Compliance Twin monitors 10+ rules across 6 categories: Bias (demographic parity, equalized odds), Performance (accuracy thresholds, latency limits), Data Quality (missing data, schema validation), Documentation (model cards, audit trails), Security (access controls, encryption), and Regulatory (GDPR consent, NYC LL144 bias audits, Colorado AI Act requirements).

Root Cause Analysis

HAIEC: DIY AI Compliance