A Unified Framework for Probabilistic Verification of AI Systems via
  Weighted Model Integration
Paolo Morettin Andrea Passerini Roberto Sebastiani
The probabilistic formal verification (PFV) of AI systems is in its infancy.
So far, approaches have been limited to ad-hoc algorithms for specific classes
of models and/or properties.
  We propose a unifying framework for the PFV of AI systems based onWeighted
Model Integration (WMI), which allows to frame the problem in very general
terms.
  Crucially, this reduction enables the verification of many properties of
interest, like fairness, robustness or monotonicity, over a wide range of
machine learning models, without making strong distributional assumptions.
  We support the generality of the approach by solving multiple verification
tasks with a single, off-the-shelf WMI solver, then discuss the scalability
challenges and research directions related to this promising framework.