Episode 88 — Explainability: Global vs Local and Interpretable vs Post-Hoc
In Episode eighty eight, titled “Explainability: Global vs Local and Interpretable vs Post Hoc,” we focus on why models need explanations in the first place, especially when their outputs influence security decisions, customer outcomes, or operational workflows. An accurate model that cannot be explained to the right stakeholders is difficult to govern, difficult to audit, and difficult to trust when something goes wrong. Explainability is not a decorative extra, because in many environments it is the bridge between analytics and accountability. It also protects teams from fooling themselves, since explanations can reveal when a model is relying on shortcuts, proxies, or artifacts that will not hold up in the real world. At the same time, explainability has limits, and treating explanations as absolute truth can create new risks rather than reducing them. The goal here is to understand the main categories of explanations and how to choose an approach that is appropriate for both the model and the decision context.
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Global explanations describe overall model behavior patterns, meaning they summarize what the model tends to do across the population rather than why it made one specific prediction. A global view might characterize which features the model relies on most, how predicted risk changes as a feature increases or decreases, or what combinations of features tend to drive outcomes. Global explanations are useful because they support policy level governance, such as deciding whether a model aligns with acceptable decision criteria and whether it appears to be using sensible signals. They can also help you understand the model’s general decision surface, which is valuable when you are comparing models or tuning performance tradeoffs. Importantly, global explanations are still summaries, not proofs, because they compress complex behavior into a form humans can digest. Their strength is breadth, because they help you reason about the model as a system rather than as a set of individual decisions.
Local explanations focus on why one prediction happened, meaning they aim to describe which features most influenced the model’s output for a specific case. This is the type of explanation you often need when an analyst, auditor, or customer asks why an alert was raised or why an action was taken. Local explanations can highlight the particular feature values that pushed the model toward a positive or negative classification in that instance, offering a narrative that helps humans validate or contest the decision. They are especially important in operational settings where decisions are reviewed case by case, such as fraud investigations or security incident triage. The key is that a local explanation is inherently conditional on the case, so it may not generalize to other cases even if they look similar. Local explanations are therefore about case understanding and accountability, not about describing overall system policy.
When transparency is required, interpretable models are often the best choice because their structure is understandable by design rather than explained after the fact. Interpretable models are those whose decision logic can be communicated directly, such as linear models with a manageable number of features or decision trees that are shallow enough to be read and reasoned about. The advantage is that interpretation is not an approximation layered on top of a black box, but a direct view of the mechanism that produced the prediction. This can be critical under regulatory or contractual requirements, because you can provide explanations that are stable and auditable. Interpretable models also make it easier to detect problematic proxies, because you can see how features enter the decision directly. The tradeoff is that interpretable models may not capture complex nonlinear patterns as well as more flexible models, so you must weigh transparency needs against performance requirements.
Post hoc methods are used when complex models must be explained, meaning you keep the complex learner but add an explanation layer that approximates or summarizes its behavior. This is common when the highest performing model is not inherently interpretable, such as ensembles or deep learning models, and the organization still needs a way to provide insight into decisions. Post hoc explanations can operate globally or locally, offering feature attributions, sensitivity analyses, or simplified surrogate representations. The benefit is that you can retain strong predictive performance while still providing a form of transparency for governance and communication. The risk is that post hoc explanations can be misleading if they are treated as exact representations of the model rather than as approximations. In other words, post hoc explainability can satisfy the need for insight, but it requires careful communication about what is being explained and how reliable that explanation is.
A crucial conceptual boundary is that explanation is not the same as causation, because model explanations are typically descriptive, not causal claims about the world. When an explanation says a feature contributed to a prediction, it is describing how the model used that feature within its learned function, not proving that the feature caused the outcome. This distinction matters because people naturally interpret explanations as stories about why something happened, and stories often imply causality. In reality, a model can rely on correlated features that are not causal, and it can produce accurate predictions while still being driven by proxy signals. This is especially relevant in cybersecurity, where many signals are behavioral correlates rather than root causes, and adversaries can manipulate correlates without changing underlying intent. Treating descriptive explanations as causal truth can therefore lead to policy mistakes and misguided interventions.
Choosing an explanation approach should be driven by audience and regulation, because different stakeholders need different forms of understanding and different levels of rigor. An executive audience may need a global explanation that supports policy decisions, risk tolerance, and oversight, while an analyst audience may need local explanations that support case triage and investigative workflows. Regulators or auditors may require documented methods, stability expectations, and evidence that explanations are not arbitrary, which can push you toward interpretable models or toward carefully validated post hoc methods. The important skill is matching the explanation to the question being asked, because the wrong explanation style can confuse rather than clarify. Some environments also require consistent explanations across time, which implies you must version and govern the explanation method just as you govern the model. When you align explainability with stakeholder needs, it becomes a tool for trust rather than a performative exercise.
Explanations that change wildly across similar cases are a red flag, because instability undermines trust and suggests the explanation method may be sensitive to small perturbations. If two cases are nearly identical and the explanation assigns radically different feature influences, users will reasonably question whether the explanation reflects anything meaningful. Instability can arise from the model itself, from the post hoc method, or from data issues like noise and correlated features that make attributions fragile. This matters operationally because inconsistent explanations can cause analysts to chase the wrong clues or to dismiss the model’s outputs entirely. For governance, unstable explanations are hard to audit, because you cannot establish a consistent rationale for similar decisions. A mature approach treats explanation stability as a quality attribute that must be monitored and improved, not as an optional comfort feature.
Communicating feature influence requires care because it is easy to overstate certainty, especially when explanations are presented as tidy rankings or clean narratives. Feature attribution scores can look precise, but they often reflect complex interactions and correlation structures that are not obvious in the final explanation. You also need to be careful about implying that a feature alone “caused” the decision, because model outputs usually reflect multiple features and their combinations. Good communication uses measured language, describing features as contributing to the model’s score rather than as definitive causes, and acknowledging that explanations are conditional on the model and the data distribution. This caution is not weakness, because it preserves credibility when stakeholders test the explanation against edge cases. Overconfident explanation language, on the other hand, invites backlash when someone finds a counterexample or notices inconsistency.
Documentation is the backbone of explainability governance because it records not only what explanation method is used, but also its limitations and the conditions under which it is valid. Documenting methods includes specifying whether explanations are global or local, whether they are derived from an interpretable model or from post hoc approximation, and how they are generated and versioned. Limitations include known instability under correlation, reduced reliability under drift, and the fact that explanations are descriptive rather than causal. This documentation belongs in model records because explanations influence how decisions are defended, how incidents are investigated, and how compliance requirements are met. Without documentation, teams can accidentally change explanation methods and create inconsistent narratives over time. With documentation, explainability becomes a controlled part of the model lifecycle rather than a last minute justification step.
Explainability is also a diagnostic tool because it can reveal bias, leakage, and spurious correlations that pure performance metrics might hide. A global explanation that shows heavy reliance on a suspicious feature can indicate leakage, such as a post outcome field or an identifier proxy that should not be available at prediction time. A local explanation that repeatedly highlights a demographic proxy or an irrelevant operational field can indicate potential bias or a misaligned objective. Explanations can also expose spurious correlations, such as a model relying on a seasonal artifact or a logging quirk that happens to correlate with the target in the training window. In cybersecurity, this is particularly valuable because attackers can exploit brittle proxies, and explanation analysis can reveal where the model is vulnerable. Using explainability as a lens for model sanity checks is one of the most practical ways to improve trustworthiness.
Balancing explainability with performance and operational constraints is where the topic becomes real, because the best explanation approach on paper might be impossible to run at the required speed or scale. Interpretable models can be faster and easier to govern, but they may underperform in complex pattern recognition tasks where nonlinear interactions matter. Post hoc methods can provide insight for complex models, but they can add compute cost, latency, and engineering complexity, especially if local explanations are generated for every prediction. Operational constraints may force you to generate explanations only for flagged cases, to precompute global summaries, or to use simpler surrogate explanations for real time triage. The balance is not about choosing explainability or performance, but about designing a system that achieves acceptable performance while providing explanations that are useful, stable, and auditable. The exam perspective is that the correct choice depends on context, not ideology.
The anchor memory for Episode eighty eight is that global explanations are for policy, local explanations are for cases, and both need limits. Global explanations help you set governance expectations and verify that the model’s overall behavior aligns with acceptable signals. Local explanations help you justify and investigate individual decisions, supporting operational workflows and accountability. Both types can mislead if treated as causal truth or if their instability and approximation limits are ignored. This anchor also reminds you that explainability is not a single technique, but a family of approaches that must be selected and governed intentionally. When you keep this framework in mind, you can answer explainability questions with clarity and restraint.
To conclude Episode eighty eight, titled “Explainability: Global vs Local and Interpretable vs Post Hoc,” consider a scenario and state your explanation plan in a way that matches the stakeholder needs and constraints. Imagine a fraud detection model used to generate alerts that investigators review, in an environment where compliance requires a defensible rationale for high impact decisions. A sound plan would include a global explanation summary to support policy oversight, showing which features generally drive risk and confirming that no leaky or prohibited proxies dominate behavior. For investigator workflows, you would provide local explanations for each flagged case, using a validated post hoc method if the underlying model is complex, while also monitoring explanation stability so similar cases yield consistent rationales. You would document the explanation method, its descriptive nature, and its limitations in model records, and you would use explanation reviews as part of bias and leakage detection routines. This plan balances operational needs with governance requirements, and it treats explainability as an engineered capability with boundaries rather than as a story generator.
