BackUnrepresentative risk testing
Unrepresentative risk testing
Risk Domain
Inadequate regulatory frameworks and oversight mechanisms that fail to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.
"Testing is unrepresentative when the test inputs are mismatched with the inputs that are expected during deployment."
Entity: Who or what caused the harm
Intent: Whether the harm was intentional or accidental
Timing: Whether the risk is pre- or post-deployment
Supporting Evidence (1)
1.
"If the model is evaluated in a use, context, or setting that is not the same as the one expected for deployment, the evaluations might not accurately reflect the risks of the model."
Other risks from IBM (2025) (63)
Lack of training data transparency
6.5 Governance failureHumanUnintentionalPre-deployment
Uncertain data provenance
6.5 Governance failureHumanOtherPre-deployment
Data usage restrictions
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Data acquisition restrictions
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Data transfer restrictions
7.3 Lack of capability or robustnessHumanUnintentionalPre-deployment
Personal information in data
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationAI systemUnintentionalPost-deployment