Book
responsible_ai
Pocket Reference Title
Explainability & Interpretability
Proposed Content
Interpretability: Understanding how a model makes decisions
Explainability: Understanding why a model made a specific decision
The difference between transparency, interpretability, and explainability
When is it necessary.
Rationale
It bridges the gap between technical complexity and practical decision-making in AI development.
Content Types
Additional Resources
LIME – Ribeiro et al., “Why Should I Trust You?”
SHAP – Lundberg & Lee, “A Unified Approach to Interpreting Model Predictions”
"Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction"
Mersha et al., 2024
Book
responsible_ai
Pocket Reference Title
Explainability & Interpretability
Proposed Content
Interpretability: Understanding how a model makes decisions
Explainability: Understanding why a model made a specific decision
The difference between transparency, interpretability, and explainability
When is it necessary.
Rationale
It bridges the gap between technical complexity and practical decision-making in AI development.
Content Types
Additional Resources
LIME – Ribeiro et al., “Why Should I Trust You?”
SHAP – Lundberg & Lee, “A Unified Approach to Interpreting Model Predictions”
"Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction"
Mersha et al., 2024