Explain a Model's Predictions to Stakeholders
Communicate model behavior honestly.
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Help me explain a machine learning model to non-technical stakeholders. Model purpose and type: [MODEL_PURPOSE_AND_TYPE]. Performance results: [PERFORMANCE]. Feature importance or explanation output (coefficients, permutation importance, SHAP values): [EXPLANATION_OUTPUT]. Decisions affected: [DECISIONS]. Produce: 1. A one-page explanation: what it predicts, what data it uses, how well it works in terms people understand (for example "of 100 customers flagged, about 62 actually leave"), and when it is wrong. 2. A table of the top drivers: Feature | Direction of effect | Plain-language meaning | Caution (association, not cause). 3. Three example predictions explained in a sentence each, with what would change the prediction. 4. A section of limits: groups where it performs worse, data it has not seen, and what it should not be used for. 5. A monitoring plan: metrics, drift checks, and who reviews them. 6. Questions stakeholders will ask with honest answers, including "Can we trust it?" and "Is it fair?". 7. A statement on human oversight and appeal for affected people, with legal requirements on automated decisions to be checked [VERIFY in [JURISDICTION]]. Do not overstate causal meaning of feature importance.
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