Problems of synthetic data
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.
"In the case of sparse data quantity, the simulation or generation of data is a valid alternative. However, it is essential to make sure that the simulated data is sufficiently similar to real data, especially in the way the AI system perceives them. Otherwise, generalization to operational data and reliable operational behavior can not be guaranteed."(p. 10)
Other risks from Schnitzer et al. (2024) (24)
Inadequate specification of ODD
7.3 Lack of capability or robustnessInappropriate degree of automation
7.2 AI possessing dangerous capabilitiesInadequate planning of performance requirements
7.3 Lack of capability or robustnessInsufficient AI development documentation
7.4 Lack of transparency or interpretabilityInappropriate degree of transparency to end users
7.4 Lack of transparency or interpretabilityChoice of untrustworthy data source
7.0 AI System Safety, Failures & Limitations