Formal verification under uncertainty

Being able to ensure that AI systems behave as intended is essential for their adoption in many areas. This series covers formal methods for the verification of safety, fairness, and other relevant properties of (probabilistic) ML systems. We document the progress in research domains such as neuro-symbolic verification, probabilistic model-checking, and weighted model integration.
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