Can explanations be formally validated?

A skeptical reading argues that formal validation is narrower than the word “explanation” suggests. A prover may certify consistency or derivability within an encoding while leaving the encoding, causal interpretation, human usefulness, or faithfulness to the model’s actual decision unverified. The reliability of the whole pipeline therefore remains an empirical concern, especially because published assessments have found bugs in formal-explainer implementations.

• A formal proof may validate the translated rationale under encoded premises rather than the logical validity of the original natural-language argument.
• Many XAI techniques lack a clearly specified purpose or rely on questionable assumptions, making formal validation of the wrong target possible.
• An assessment of PyXAI reported incorrect explanations on most analyzed datasets, showing that formal status does not eliminate implementation errors.
• Validation frameworks that independently check witnesses, proofs, and model behavior reflect concern that a single explainer should not be trusted automatically.

Featured sourceForEx: A Formal Verification Framework forarxiv.org5 sourcesView sources