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AI Risk

Risks from models and algorithms (Risks of robustness)

"As deep neural networks are normally non-linear and large in size, AI systems are susceptible to complex and changing operational environments or malicious interference and inductions, possibly leading to various problems like reduced performance and decision-making errors."

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AI Risk7. AI System Safety, Failures, & Limitations7.3 > Lack of capability or robustness2 - Post-deployment

Record summary

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Techniques0Attack methods connected to this risk.
Mitigations0Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.

Risk profile

How this risk is described and categorized.

Domain7. AI System Safety, Failures, & Limitations
Subdomain7.3 > Lack of capability or robustness
Entity2 - AI
Intent3 - Other
Timing2 - Post-deployment
CategoryAI's inherent safety risks
SubcategoryRisks from models and algorithms (Risks of robustness)

Related techniques

Attack methods connected to this risk.

No linked attack methods. No AI attack method is connected to this risk in the current data.

Suggested mitigations

Defenses that may help with related attacks.

No propagated mitigations. No defense is available through the connected attack methods.

Source

Research source for this risk, when available.