EU AI Act Article 15: Accuracy and Robustness
What EU AI Act Article 15 requires for high-risk AI accuracy, robustness against adversarial attacks, fail-safe mechanisms, and cybersecurity.
Article 15: Accuracy, Robustness and Cybersecurity
High-risk AI systems must achieve appropriate levels of accuracy, robustness, and cybersecurity throughout their lifecycle.
Providers must declare accuracy metrics, build resilience against errors and adversarial attacks, and implement technical safeguards to prevent unauthorized manipulation of system behavior.
Core Requirements
Declared Accuracy Metrics
Define and document appropriate accuracy levels and metrics in the instructions for use. Accuracy must be measurable and verifiable throughout the system lifecycle.
Resilience to Errors and Faults
Systems must handle errors, faults, and inconsistencies gracefully. Implement redundancy mechanisms such as backup systems and fail-safe plans to maintain consistent performance.
Resilience to Adversarial Attacks
Protect against data poisoning and model poisoning attacks that corrupt training data or model parameters. Systems must detect and mitigate attempts to manipulate inputs or weights.
Protection Against Evasion and Confidentiality Attacks
Guard against model evasion attacks that exploit decision boundaries and confidentiality attacks that extract sensitive model information or training data.
Technical Cybersecurity Solutions
Deploy appropriate security measures to prevent and control adversarial attacks and model vulnerabilities. Solutions must address the specific risks of AI system exploitation.
Implementation Steps
Adversarial Testing Pipelines
Integrate adversarial robustness testing into CI/CD. Generate adversarial examples, test model boundaries, and validate defenses against known attack patterns.
Accuracy Benchmarking
Establish baseline accuracy metrics aligned with your declared levels. Track accuracy across model versions, datasets, and deployment environments to detect degradation.
Redundancy and Fail-Safe Design
Build fallback mechanisms for critical system paths. Implement backup models, safety monitors, and human-in-the-loop triggers when confidence drops below thresholds.
Model Supply Chain Security
Scan dependencies, base models, and training datasets for vulnerabilities. Verify the provenance of external models and validate integrity of training pipelines.
The Accuracy-Robustness Tradeoff
Optimizing for benchmark accuracy can reduce adversarial robustness. Providers must balance performance goals with security requirements and document design decisions that navigate this tradeoff.
Frequently Asked Questions
Related Articles
Article 9: Risk Management
Accuracy and robustness requirements feed into the continuous risk management system required by Article 9.
Article 10: Data Governance
Data quality and provenance under Article 10 directly impact accuracy, robustness, and resilience to poisoning attacks.
Article 11: Technical Documentation
All accuracy metrics, robustness testing, and cybersecurity measures must be documented in the technical documentation package.
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