BackData drift
Data drift
Risk Domain
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
"Data drift is a phenomenon in that distribution of operational input data departs from those used during training. This can cause a degradation in performance."(p. 10)
Entity: Who or what caused the harm
Intent: Whether the harm was intentional or accidental
Timing: Whether the risk is pre- or post-deployment
Other risks from Schnitzer et al. (2024) (24)
Inadequate specification of ODD
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Inappropriate degree of automation
7.2 AI possessing dangerous capabilitiesAI systemUnintentionalPost-deployment
Inadequate planning of performance requirements
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Insufficient AI development documentation
7.4 Lack of transparency or interpretabilityHumanOtherPre-deployment
Inappropriate degree of transparency to end users
7.4 Lack of transparency or interpretabilityHumanOtherPre-deployment
Choice of untrustworthy data source
7.0 AI System Safety, Failures & LimitationsHumanUnintentionalPre-deployment