Editing Explainable AI (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
There is a fundamental tension in AI between **model complexity and interpretability**: the most accurate models (deep neural networks, gradient boosting ensembles) are the least interpretable, while the most interpretable models (linear regression, shallow decision trees) are often less accurate. XAI attempts to navigate this tension.

**Two strategies**:

**Intrinsically interpretable models**: Choose model architectures that are interpretable by design. Linear models explain predictions as weighted feature sums. Generalized Additive Models (GAMs) extend this to non-linear feature contributions. Decision trees can be visualized. Rule lists produce human-readable decision logic. For high-stakes decisions, these are often preferable even at some accuracy cost.

**Post-hoc explanation**: Train any model, then explain its predictions afterward. SHAP computes each feature's Shapley value — its average marginal contribution across all possible feature orderings — providing a theoretically principled attribution. LIME fits a local linear model around the prediction to approximate the complex model's behavior in that region.

**The faithfulness problem**: Post-hoc explanations don't explain the model — they explain a simpler approximation of the model. An explanation that looks plausible may not accurately reflect the model's actual reasoning. This is known as the faithfulness problem and is a fundamental limitation of post-hoc XAI.

**Explanation types by audience**: Data scientists need feature attributions and global model behavior. Domain experts need contrastive explanations ("why X not Y?"). End users affected by decisions need natural language explanations. Regulators need documentation and audit trails.
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