Verifiable AI vs explainable AI: what is the difference?
Explainable AI and verifiable AI are often confused, but they answer different questions. Explainability asks "why did the model produce this output?" Verifiability asks "can we prove what the model actually produced, when, and that nobody altered the record afterwards?" Both matter for trustworthy AI, and increasingly regulators expect both — but they are not substitutes.
Explainable AI: understanding the decision
Explainable AI aims to make a model’s reasoning intelligible to humans — feature attributions, saliency maps, surrogate models and natural-language rationales. Its goal is comprehension: helping an operator, auditor or affected person understand what drove an output. Its limit is that an explanation is an interpretation, not a proof. It tells a plausible story about the decision; it does not, by itself, establish that the recorded decision is authentic and unaltered.
Verifiable AI: proving the record
Verifiable AI produces cryptographic evidence about the decision itself: sign each record (input hash, output, model identifier, timestamp) and chain entries so any later edit breaks verification. This yields a non-repudiable, tamper-evident history that a regulator or court can check independently, without trusting the operator. It does not explain the reasoning — it proves what happened and that the record was not changed after the fact.
Why you need both, and the honest limit
Explainability supports understanding and contestability; verifiability supports accountability and evidence. A complete audit posture combines them: explain the decision to the affected party, and prove the record to the auditor. Sign records with post-quantum ML-DSA (FIPS 204) so they stay verifiable for years. Honest scope: this proves authenticity, ordering and integrity of the logged decision — it does not prove the model reasoned correctly, and the crypto is resistant to known attacks per NIST, not unbreakable.
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