AI gave the right answer. Did its explanation describe the actual reason?

Check the conclusion and the explanation separately. An understandable story is not automatically evidence of the system’s internal process.

Our view: ask what can be independently checked in an AI explanation, rather than treating a detailed narrative as a record of how the system worked. A useful explanation and a faithful account of a process are different claims.

A small rule makes the difference visible

Our invented program labels an order Bulk when quantity is at least five, otherwise Standard. It never uses colour. Quantity six gives Bulk for both red and blue; quantity four gives Standard. Saying “Bulk because it is red” does not describe this rule even when the label is correct.

What NIST distinguishes

NIST’s 2021 report separates meaningful explanations, explanation accuracy and decision accuracy. Explanation accuracy concerns whether an explanation reflects the process or reason for the output. The report is a framework; it is not a test of today’s particular chatbot.

Check what is available

For a calculation, recompute the steps. For a document claim, compare the cited passage. For software, inspect the rule or run appropriate cases when you can. These checks can support a conclusion without exposing every internal operation of a model.

Do not overstate a toy example

Our simple rule is visible in source code. A few changed inputs to a closed model do not provide the same access or prove its true cause. Label a plausible explanation as an explanation to assess, rather than an independently verified trace.

Published October 14, 2026. Editorial analysis using a dated NIST framework and an original three-case program. We executed the toy rule, not an AI explainability benchmark or model-internals audit.

Sources: NIST · Explainable AI principles.