# Explainability

> AI model explainability

- Skill: `ffsshhttiikk/explainability` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ffsshhttiikk/explainability`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ffsshhttiikk/explainability/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: ffsshhttiikk (https://skillmd.com/u/ffsshhttiikk)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ffsshhttiikk/explainability

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## What I do

- Explain model predictions
- Implement interpretable models
- Create feature importance analysis
- Build visualization tools
- Design trust-building explanations
- Handle regulatory requirements

## When to use me

Use me when:
- Model debugging
- Stakeholder communication
- Regulatory compliance
- Bias detection
- Trust in AI decisions

## Key Concepts

### Explanation Methods
```python
import shap
import lime
from sklearn.inspection import permutation_importance

# SHAP values
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

# Visualize
shap.summary_plot(shap_values, X_test, feature_names=feature_names)

# LIME explanations
from lime.lime_tabular import LimeTabularExplainer

explainer = LimeTabularExplainer(X_train, feature_names=feature_names)
explanation = explainer.explain_instance(X_test[0], model.predict_proba)

# Permutation importance
result = permutation_importance(model, X_test, y_test, n_repeats=10)
```

### Interpretable Models
- Linear models: Coefficients
- Decision trees: Path visualization
- Rule-based: Explicit rules
- Attention: Attention weights

### Types of Explanations
- **Global**: Overall model behavior
- **Local**: Single prediction
- **Feature**: Feature importance
- **Counterfactual**: What if scenarios