Turn complex model logic into insights executives can act on. Learn which explainability tools are reliable and how to present them clearly.
SHAP values most directly represent ______ for an individual prediction
each feature’s contribution to the prediction relative to a baseline
overall correlation between feature and target
hyperparameter sensitivity ranking
the feature’s data lineage in the pipeline
A partial dependence plot (PDP) shows the ______ effect of a feature on the prediction
purely causal
average marginal
individual case-specific
time-lagged
Counterfactual explanations answer which executive-friendly question
What hyperparameters should we tune next
What is the model’s maximum theoretical accuracy
What is the smallest change that would flip this decision
What is the global variable importance
A surrogate model is used to ______ a black-box model with an interpretable approximation
encrypt
calibrate labels by
replace training data with
mimic
One pitfall when showing feature importance to executives is
correlated features can split or dilute importance and mislead
importance values always prove causality
importance eliminates the need for validation data
importance values are identical across all models
Local explanations are best when leaders ask about
how to design new data pipelines
why this one case received its specific score
how the model behaves on average across all data
how to set cloud quotas
Confidence bands around explanation charts primarily communicate
whether training converged
server response latency
uncertainty in the estimated effect
data freshness SLAs
Which pairing is most appropriate for an executive dashboard
A list of hyperparameters and seed values
Only PDPs for every feature, no summaries
Only raw predictions without any context
Global importance for orientation, plus local SHAP on selected cases
SHAP’s additivity property means individual attributions ______
sum to the prediction minus a baseline
match feature correlations exactly
sum to zero for every instance
equal the training loss
For governance, an executive-facing explanation should always include
the full training dataset for download
model version, data window, and assumptions noted near the chart
screenshots of developer IDE settings
private customer PII fields
Starter
New to explainability? Start with global patterns, then drill into one case at a time.
Solid
Good grasp of XAI basics—add uncertainty bands and governance notes to your dashboards.
Expert!
You’re ready to brief the board with clear, defensible explanations tied to decisions.
