# Model Interpretability Deep

> Use when implementing deep model interpretability methods.

- Skill: `loopyluci/model-interpretability-deep` (Agent Skill)
- Install (CLI): `npx skillmds@latest add loopyluci/model-interpretability-deep`
- Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/model-interpretability-deep/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: LoopyLuci (https://skillmd.com/u/loopyluci)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/loopyluci/model-interpretability-deep

---


# Deep Model Interpretability

Implementing deep model interpretability — from feature attribution (SHAP, Integrated Gradients) through mechanistic interpretability, activation patching, and sparse autoencoders.

## When to Use

- Understanding why a model made a specific prediction
- Debugging model behavior in production
- Building trust with stakeholders through explanations
- Discovering what neural networks internally represent
- Detecting spurious correlations and shortcut learning

## Interpretability Methods

```python
INTERPRETABILITY_METHODS = {
    'shap': 'SHapley Additive exPlanations — game-theoretic feature attribution',
    'integrated_gradients': 'Path-based attribution for neural networks',
    'activation_patching': 'Intervene on specific neurons/layers, measure effect on output',
    'sparse_autoencoders': 'Learn interpretable features from hidden representations',
    'probing': 'Train simple classifiers on hidden states to detect encoded concepts',
}

def compute_shap_values(model, X, background_data):
    """Compute SHAP feature importance for a model."""
    import shap
    explainer = shap.KernelExplainer(model.predict, background_data)
    shap_values = explainer.shap_values(X, nsamples=100)
    return {
        'shap_values': shap_values,
        'feature_importance': np.abs(shap_values).mean(0),
    }
```

## Verification Checklist

- [ ] Feature attribution method chosen (SHAP, Integrated Gradients, LIME)
- [ ] Explanations are consistent across similar inputs
- [ ] Model behavior documented for key edge cases
- [ ] Mechanistic interpretability for understanding representations
- [ ] Spurious correlations identified and mitigated
- [ ] Explanations validated with domain experts
- [ ] Model card or documentation with interpretability findings