Machine Learning Interpretability / Explainability

Key Notes / Ideas 

Key items from blog / Reposted 

• Create White-Box / Interpretable Models (Intrinsic): e.g., Linear Regression, Decision Trees.
• Explain Black-Box / Complex Models (Post-Hoc): e.g., LIME, SHAP.
• Enhance the Fairness of a Model: e.g., Fairness Indicators, Adversarial Debiasing.
• Test Sensitivity of Predictions: e.g., Perturbation Analysis.

Local vs Global Interpretations:

• Local: Dive into a single prediction to understand it. e.g., Individual SHAP values.
• Global: Grasp the overall model behavior. e.g., Feature Importance Rankings.

Data Types & Applicable Interpretability Methods:

• Tabular: e.g., Partial Dependence Plots.
• Text: e.g., Word Embedding Visualizations.
• Image: e.g., Grad-CAM for CNNs.
• Graph: e.g., Node Influence Metrics.

Model Specificity:

• Model Specific: Techniques that apply to a single model or a group of models. e.g., Feature Importances for Trees.
• Model Agnostic: General methods applicable to any model. e.g., LIME.

Ref - Link

From AI Ethics institute key points Link

• Transparency and explainability gains may be significant
• Explainable by justification - Examples could get a better understanding 
• Explainability through feature importance - understanding of the effect of features - SHAP (SHapley Additive exPlanations
• Abstracting key patterns identified in the deep learning models as actual features
• Implications of different types of errors have, as well as what the right way of evaluating these errors should be.

Keep Exploring!!!