When No One Can Inspect the Reasoning | AI Learning Guide

The three audiences are not the same complaint repeated three times. The chemist's problem is practical: a score with no attribution gives nothing to act on. The regulator's problem is procedural: a claim that cannot be inspected cannot be admitted into a decision that carries consequences. The clinician's problem is ethical: the output affects a person, so the basis has to be defensible. And the tension is real — the models that predict best are often the ones that explain least. That is not a problem waiting for a clever fix; it is a trade-off that gets managed case by case, depending on what the prediction is for.

0:00 / 0:00

Auto-play

Who needs the reasoning, and why

Chemist

Needs to know which feature to change next
A bare score ranks but does not guide design

Reviewer or regulator

Needs a basis that can be checked and challenged
An unexplainable input is hard to admit into a consequential decision

Clinician or patient

Needs to know why this compound, for this person
Opacity that is inconvenient in research becomes an obstacle to care

Interpretability and accuracy often pull against each other. A simpler model that can be explained may predict less well; a more accurate model may be opaque. There is no universal right answer — the acceptable balance depends on what the prediction will be used for.