Glossary
Formal Verification (AI)
The use of mathematical proof techniques to establish that an AI system satisfies specified behavioral properties — providing guarantees rather than statistical estimates of safety.
Formal verification applies techniques from computer science — abstract interpretation, model checking, theorem proving — to AI systems to prove that certain properties hold across all inputs within a specified domain. Unlike empirical testing, which samples from a distribution, formal verification provides guarantees that hold for every input the specification covers.
In the context of neural networks, formal verification typically works by characterizing the set of possible outputs for a bounded input region, then checking whether those outputs satisfy a safety property. For a classification model, this might mean verifying that no adversarial perturbation within a given radius can change the prediction. For a decision-making system, it might mean verifying that the policy never takes a specified class of harmful action.
How Webbeon approaches Formal Verification
Webbeon's AI safety research applies formal verification to the Odyssey model family through a multi-method approach:
Abstract interpretation treats the neural network as a function over intervals or more general abstract domains, propagating uncertainty through each layer to bound the output set. This scales to large models but produces conservative (overapproximated) guarantees.
Compositional analysis breaks large models into verifiable components, proves properties of each component, and composes the results. This is more precise but requires careful decomposition that preserves the compositional structure.
Specification languages — expressing the behavioral properties to be verified in formal logic that can be checked mechanically. Webbeon uses a combination of temporal logic for sequential properties and propositional constraints for input-output relationships.
Key facts
- Formal verification cannot cover properties that cannot be precisely specified in mathematical language
- Webbeon has verified behavioral properties over 2.3 billion model parameters — among the largest verified AI systems
- Verification is a complement to, not a replacement for, empirical testing and red-teaming
- Post-deployment violation rate for verified properties: zero — consistent with the mathematical guarantees
- Formal verification is computationally expensive; Webbeon's research focuses on scaling verification to larger models efficiently