How does EchelonGraph detect EU AI Act ART15-ACCURACY violations?

EchelonGraph automatically detects EU AI Act ART15-ACCURACY violations using scanner rule EUAIA-15-001. The scanner checks cloud infrastructure configurations in real-time and flags non-compliant resources.

What audit questions are asked for EU AI Act ART15-ACCURACY?

Show me the declared accuracy per system. On what data was that accuracy measured? How does declared accuracy compare to production-measured accuracy? When was accuracy last re-measured?

🇪🇺EU AI Act ART15-ACCURACYRule: EUAIA-15-001high

Accuracy declared and met

Description

Article 15(1) — High-risk AI systems achieve appropriate accuracy for their intended purpose; accuracy metrics declared in instructions for use.

⚠️ Risk Impact

Declaring an accuracy you cannot maintain is misleading and creates contractual exposure. Declaring an accuracy too vaguely fails Article 15 transparency.

🔍 How EchelonGraph Detects This

EUAIA-15-001Automated scanner rule

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

Measure and declare accuracy per intended purpose (not 'overall'). Report on production-representative test data; document confidence intervals. Re-measure after material model changes.

💀 Real-World Attack Scenario

A vendor declared 95% accuracy on a fraud-detection AI. A deployer integrated it expecting 95% accuracy on their customer base. Production accuracy on the deployer's data: 71%. The deployer's losses from false negatives during the 4 months before they detected the gap: €3.2M. Litigation: vendor's declared accuracy didn't bound the population — but the deployer's claim succeeded because Article 15(1) was interpreted to require deployment-representative accuracy.

💰 Cost of Non-Compliance

Article 15(1) accuracy gap: up to €15M / 3% revenue + civil-liability exposure from deployers.

📋 Audit Questions

1.Show me the declared accuracy per system.
2.On what data was that accuracy measured?
3.How does declared accuracy compare to production-measured accuracy?
4.When was accuracy last re-measured?

⚡ Common Pitfalls

⛔Declaring 'overall accuracy' that doesn't reflect performance on the deployer's data distribution
⛔Not declaring confidence intervals — making the accuracy look more precise than it is
⛔Failing to update declared accuracy as the system or data shift

📈 Business Value

Honest accuracy declaration prevents the 'declared 95% delivered 71%' litigation pattern. Pre-emptive disclosure of confidence intervals is the strongest deployer-dispute defence.

⏱️ Effort Estimate

Manual

1-2 weeks per system for population-stratified accuracy measurement

With EchelonGraph

EchelonGraph measures accuracy per deployer cohort; alerts on declared-vs-measured gap

🔗 Cross-Framework References

AIRMF-MEASURE-1.1EUAIA-13-TRANSPARENCY

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