Approaches and metrics for AI risk measurement are identified
Description
Validated metrics for accuracy, fairness, robustness, security, and explainability are identified, with documented selection rationale and known limitations.
⚠️ Risk Impact
Choosing the wrong metric produces models that score well on paper but fail in deployment. Worse: metrics that measure proxy behaviour (e.g. 'top-1 accuracy') without the business outcome that actually matters (e.g. 'customer dispute rate').
🔍 How EchelonGraph Detects This
EchelonGraph's Tier 1 Cloud Scanner automatically checks for this condition across all connected cloud accounts. Violations are flagged as high-severity findings with remediation guidance.
🔧 Remediation
For each system, document: which metrics, why those metrics, what their known limitations are, and which business outcome they proxy. Adopt at least one fairness metric (demographic parity, equalised odds, calibration) and document why it's appropriate for the use case.
💀 Real-World Attack Scenario
A health-insurance AI was evaluated only on AUC-ROC. It scored 0.92. In production, it consistently under-quoted older customers by 18%. The team had not measured 'demographic parity' or 'equalised odds' because they 'weren't aware those were standard'. The state insurance commissioner's office found the company in violation of unfair discrimination statutes; $14M settlement.
💰 Cost of Non-Compliance
State insurance fair-pricing settlements involving AI: avg $14M-$45M (Connecticut, NY, CA enforcement actions 2023-2024). EU AI Act Article 15(1) accuracy requirement: €15M / 3% revenue.
📋 Audit Questions
- 1.Which fairness metrics do you measure for your customer-facing AI?
- 2.Why those metrics and not alternatives?
- 3.Show me the last fairness measurement report.
- 4.How does fairness scoring tie into release gates?
⚡ Common Pitfalls
- ⛔Measuring overall accuracy and stopping there — missing systematic subgroup failures
- ⛔Choosing fairness metrics that contradict each other (impossibility theorem) without resolving the choice
- ⛔Measuring metrics on training data only, not production traffic
📈 Business Value
Rigorous metric selection cuts post-launch regulatory remediation cost by 60% and de-risks insurance + employment AI deployments which face the heaviest 2024-2026 enforcement.
⏱️ Effort Estimate
1-2 weeks for cross-functional metric selection per high-risk model
EchelonGraph ships a metric library + selection guide per use case; tracks measurement cadence
🔗 Cross-Framework References
Automate NIST AI-RMF MEASURE-1.1 compliance
EchelonGraph continuously monitors this control across all your cloud accounts.
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