An explanation answers a narrower question
Feature-attribution methods can help inspect which inputs contributed to a model output under a defined explanation method. That is valuable for debugging, model review, and communicating behaviour. It does not automatically explain the real-world process that generated the outcome.
A feature can be influential because it is correlated with a meaningful variable, because it proxies for a sensitive attribute, or because the training data contains a spurious pattern. Attribution alone cannot tell you which story is true.
Check the context around an attribution
Before interpreting an explanation, record the model version, background/reference data, target, preprocessing path, and whether the explanation is local or global. Different choices can change what a chart means.
Correlated features complicate interpretation because information can be shared across inputs. A ranked bar chart can look precise while concealing uncertainty or instability.
Treat explanation as one piece of evidence
Combine attribution with error analysis, subgroup checks, domain review, and tests for data leakage. If an explanation is used in a high-impact setting, validate whether intended users understand it and whether it improves the decision process.
For a healthcare-oriented prototype such as MediSight AI, SHAP helps inspect model behaviour. It does not establish clinical validity, prove causality, or replace a clinician. Those claims require evidence beyond an attribution plot.
References and further reading
These sources informed the technical framing of this note. Consult the original documentation for full definitions and context.