Teams should use SHAP to explain both overall model behaviour and individual predictions, but they should treat the results as an approximation rather than a direct proof of causation. SHAP works best when practitioners want a consistent way to compare feature contributions across models and cases. The strongest use is to support review, debugging, and governance, not to replace model validation.
How to Use SHAP for Both Global and Local Explanations
SHAP is most useful when teams need one explanation method that works at two levels at once: the aggregate view of what drives the model overall, and the case-by-case view of why a specific prediction moved up or down. That makes it a strong fit for model review, debugging, and governance, especially when the goal is to compare feature impact consistently across different models or deployments.
For global explanations, practitioners usually look at the distribution and magnitude of SHAP values across many records to identify the features that most often influence outcomes. That helps answer questions such as which inputs the model depends on most, whether the model is leaning on a small number of dominant signals, and whether feature effects look stable across populations or segments. This is especially useful when reviewing whether the model is behaving in a way that aligns with business intent and expected data relationships.
For local explanations, SHAP shows how individual feature contributions combine to move a single prediction away from the baseline. This is valuable when a specific decision needs to be reviewed, disputed, or debugged. A local explanation should be read as a decomposition of the model output, not as proof that any feature caused the real-world outcome. That distinction matters because an explanation can be directionally helpful even when the model is imperfect or the underlying process is more complex than the model can capture.
Teams often get the most value when they use global and local views together. Global SHAP results help set expectations about what the model generally relies on, while local SHAP values help identify exceptions, odd edge cases, or records where a prediction is driven by an unusual combination of features. If the two views disagree sharply, that is often a sign to inspect data quality, feature engineering, thresholding, or whether the model is overfitting to patterns that do not hold broadly.
NHI Mgmt Group’s Ultimate Guide to NHIs is useful here as a governance analogue, because SHAP outputs are strongest when they support review and control rather than replace validation or accountability. The same discipline applies to model explanations: they help teams inspect behaviour, but they do not certify correctness on their own.
Where SHAP Helps and Where It Can Mislead
The main strength of SHAP is consistency. Because it assigns contributions relative to a reference baseline, teams can compare feature influence across instances and, with care, across models built on the same problem. That makes it useful for model comparison, feature debugging, and stakeholder communication. It also supports more disciplined review than ad hoc explanation methods, because the same framework can be used for both a summary view and a single prediction.
The main limitation is that SHAP is still an approximation of model behaviour, not a direct window into truth. The values describe how the model output is attributed, not whether the model is logically or scientifically correct. If the input data is biased, the feature set is incomplete, or the model has learned a spurious pattern, SHAP may explain that flaw very clearly. Practitioners should therefore treat SHAP as evidence for review, not as a substitute for testing, validation, or domain analysis.
A second limitation is interpretive drift. Global SHAP summaries can hide important subgroup differences, while local explanations can overemphasise a few visible features and make a prediction feel more certain than it really is. Teams should be careful not to confuse explanation stability with model reliability. If explanation patterns change materially after retraining, new data ingestion, or feature updates, that is a change to investigate, not just a cosmetic difference in charts.
NIST AI Risk Management Framework is a useful companion for deciding how much confidence to place in explanations, because it frames interpretability as part of broader governance and risk review rather than a standalone proof mechanism. When teams ask whether a SHAP view is “good enough,” the better question is whether it is good enough for the decision being made.
Practical Review Questions for Teams Using SHAP
What to verify: Check that the baseline is sensible, the feature set is well understood, and the explanation behaves consistently across representative samples. If the top drivers change dramatically between similar cases, or if the model relies on features that are proxies for protected or unstable signals, the explanation should trigger further investigation.
Decision rule: Use global SHAP when you need model-level insight, use local SHAP when you need case-level review, and use both together when you need to reconcile aggregate behaviour with a specific prediction. If the explanation is being used to justify a high-impact decision, require a separate validation step before treating the output as operationally trustworthy.
Common mistake: Teams sometimes present SHAP as if it proves causation or fairness by itself. A more defensible approach is to treat it as a structured explanation layer that supports debugging, governance, and human review, then combine it with performance testing, bias checks, and data inspection before making release or escalation decisions.
Practitioner takeaway: The best SHAP practice is to let global explanations show what the model usually depends on, let local explanations show why a single prediction moved, and then use the gap between those two views as a signal for review rather than as proof of correctness.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF, NIST CSF 2.0 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | GOVERN — AI governance | SHAP explanations support governance and oversight of model behaviour. |
| MAP — Map | SHAP helps map model behaviour, impacts and stakeholders for risk review. | |
| MEASURE — Measure | SHAP provides measurable signals for feature influence and model behaviour. | |
| Recommendation — Use governance processes to review explanation outputs before relying on them operationally. Map explanation use to the decisions and risks the model actually influences. Measure feature contribution patterns to detect drift, instability and unexpected dependence. | ||
| NIST CSF 2.0 | GV.RM — Risk Management Strategy | SHAP supports risk-informed model review and control decisions. |
| PR.DS — Data Security | SHAP findings can reveal data-quality or proxy-feature issues affecting model trust. | |
| Recommendation — Incorporate explanation outputs into the model risk management strategy. Protect and validate the data used to generate and interpret model explanations. | ||
| CIS Controls v8 | 16 — Application Software Security | SHAP is a development and review aid for model behaviour and debugging. |
| 8 — Audit Log Management | Explanation outputs can form part of review evidence for model decisions. | |
| Recommendation — Review model logic and dependencies before approving production use. Retain explanation artefacts that support investigation and decision review. | ||
Related resources from NHI Mgmt Group
- What do teams get wrong when they try to use one global role model across all tenants?
- How should teams use local explainability when machine learning models affect high-stakes decisions?
- How should data science teams use permutation feature importance to decide which variables matter most in a machine learning model?
- How should security teams use machine learning without creating too many false declines?
