Vista Academy · Interactive lesson 23
Explainable AI and Model Interpretation
Learn to inspect model predictions, compare explanation methods, and turn explanations into responsible evidence-based decisions.
Why explainability matters
Connect a model prediction to a decision a person can inspect.
A model can predict accurately and still be difficult to trust. Explainability helps people investigate why a prediction was made, spot possible errors, and decide whether the model is suitable for a real decision. An explanation is evidence about model behaviour—not proof that the model is fair or correct.
Business example
A loan model declines an application. The applicant and the risk team need to understand which information influenced that prediction.
Interactive teaching lab
Prediction → explanation → review
Select a case and inspect the reasons a review team might investigate.
Model score
0.82
Prediction output
Explanation
3 signals
Potential influences
Human review
Required
Check policy and evidence
Your investigation
Check 1 of 2A hiring model rejects a qualified candidate. What is the best first use of an explanation?
Knowledge check
Check 2 of 2What is the most useful purpose of model explainability?