BayesL: a Logical Framework for the Verification of Bayesian Networks

Bayesian networks lack systematic verification tools: Despite being used widely in AI systems, Bayesian networks currently have no unified formal method to verify what they actually imply—analysts instead rely on informal, ad hoc approaches that make it difficult to validate model behavior and uncover hidden assumptions.

BayesL enables formal specification of model properties: The framework allows users to formally specify and automatically check desired properties of Bayesian networks (such as causal relationships and hypothetical scenarios), moving beyond manual exploration toward systematic, provable verification of model behavior.

Counterfactual reasoning becomes more transparent: BayesL supports "what-if" scenario analysis through formal specification rather than manual model modifications, making it easier to trace how assumptions affect predictions and to document the reasoning process for stakeholders.

White-box verification improves accountability: By encoding model structure, assumptions, and reasoning processes explicitly in a single formal language, BayesL creates transparency that supports auditing and accountability—critical requirements for responsible AI deployment in high-stakes domains.

Bridges the explainability gap in probabilistic AI: The framework addresses a fundamental problem in explainable AI: while Bayesian networks are theoretically transparent, practitioners have lacked practical tools to systematically query and verify them, limiting their trustworthiness in real-world applications.