A common misconception about verifiable AI: that verification means the AI is telling the truth.
It doesn't.
What Verification Actually Means
When we say an inference run is verifiable, we mean:
- We can prove which model processed the input
- We can prove which adapter was active
- We can prove what configuration was used
- We can prove the input-output relationship
None of this proves the output is factually correct, logically sound, or appropriate for the use case.
Why This Matters
Consider an auditor reviewing an AI-assisted document analysis:
Wrong question: "Did the AI correctly identify all compliance issues?"
Right question: "Can we prove this specific model with this specific configuration analyzed this specific document?"
The first question requires domain expertise and ground truth. The second question is answerable with cryptographic evidence.
The Compliance Use Case
Regulatory frameworks like CMMC and AS9100 don't require AI to be correct—they require processes to be documented and auditable.
Verifiable inference supports:
- Evidence that approved models were used
- Proof that configurations weren't modified
- Audit trails for decision-making processes
- Tamper-evident logging
It does not replace:
- Domain expert review
- Ground truth validation
- Human judgment
- Quality assurance processes
Practical Implications
When deploying verifiable AI:
- Don't claim truth. Claim auditability.
- Document limitations. What can the verification prove? What can't it prove?
- Maintain human oversight. Verification supports humans, it doesn't replace them.
- Define tolerances. What variance is acceptable? Document it.
Conclusion
Verifiability is about provenance and process, not about correctness. This is a feature, not a limitation. It means verification can be technically rigorous without making claims that require philosophical judgments about AI capability.
We verify what ran. We don't verify what it means.