BackIncorrect data labels
Incorrect data labels
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 labels are essential for any supervised learning algorithm since they preset the result of the learning process. If the correctness of the data labels is not given, the AI system is prevented from learning the ground truth and therefore the intended functionality."(p. 9)
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