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Lack of robustness
Operational data issues

Uncertainty concerns

Category

"AI systems should be able not only to return output for a given instance but also to provide a corresponding level of confidence. If such a method is not implemented or not working correctly, this can have a negative impact on performance and safety."(p. 10)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Other risks from Schnitzer2024 (24)

Inadequate specification of ODD

7.3 Lack of capability or robustness
HumanUnintentionalPre-deployment

Inappropriate degree of automation

7.2 AI possessing dangerous capabilities
AI systemUnintentionalPost-deployment

Inadequate planning of performance requirements

7.3 Lack of capability or robustness
HumanUnintentionalPre-deployment

Insufficient AI development documentation

7.4 Lack of transparency or interpretability
HumanOtherPre-deployment

Inappropriate degree of transparency to end users

7.4 Lack of transparency or interpretability
HumanOtherPre-deployment

Choice of untrustworthy data source

7.0 AI System Safety, Failures & Limitations
HumanUnintentionalPre-deployment
View all 24 risks from this paper →