  The use of AI technologies is percolating into the secure development of
software-based systems, with an increasing trend of composing AI-based
subsystems (with uncertain levels of performance) into automated pipelines.
This presents a fundamental research challenge and poses a serious threat to
safety-critical domains (e.g., aviation). Despite the existing knowledge about
uncertainty in risk analysis, no previous work has estimated the uncertainty of
AI-augmented systems given the propagation of errors in the pipeline. We
provide the formal underpinnings for capturing uncertainty propagation, develop
a simulator to quantify uncertainty, and evaluate the simulation of propagating
errors with two case studies. We discuss the generalizability of our approach
and present policy implications and recommendations for aviation. Future work
includes extending the approach and investigating the required metrics for
validation in the aviation domain.
