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MIT AI Risk Initiative
BackUncertainty concerns
Lack of robustness
Operational data issues

Uncertainty concerns

AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks

Schnitzer et al. (2024)

SourceDOI
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 Schnitzer et al. (2024) (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 →