  Recent advancements in machine learning have accelerated its widespread
adoption across various real-world applications. However, in safety-critical
domains, the deployment of machine learning models is riddled with challenges
due to their complexity, lack of interpretability, and absence of formal
guarantees regarding their behavior. In this paper, we introduce a verification
framework tailored for Bayesian networks, designed to address these drawbacks.
Our framework comprises two key components: (1) a two-step compilation and
encoding scheme that translates Bayesian networks into Boolean logic literals,
and (2) formal verification queries that leverage these literals to verify
various properties encoded as constraints. Specifically, we introduce two
verification queries: if-then rules (ITR) and feature monotonicity (FMO). We
benchmark the efficiency of our verification scheme and demonstrate its
practical utility in real-world scenarios.
