Introduction to
Machine Learning Explainability
Part II
Kacper Sokol
Classification of Explanations
O1 Explanation Family
- associations between antecedent and consequent
- contrasts and differences
- causal mechanisms
Associations Between Antecedent and Consequent
- feature importance
- feature attribution / influence
- rules
- exemplars (prototypes & criticisms)
Contrasts and Differences
- (non-causal) counterfactuals
i.e., contrastive statements - prototypes & criticisms
Causal Mechanisms
- causal counterfactuals
- causal chains
- full causal model
Explanation Modalities
O2 Explanatory Medium
- (statistical / numerical) summarisation
- visualisation
- textualisation
- formal argumentation
O4 Explanation Domain
Original domain
Transformed domain
(O3 System Interaction & U4 Interactiveness]
Provided within a static or interactive protocol
interactive interface- interactive explanation
Examples of Explanations
Permutation Feature Importance
Individual Conditional Expectation &
Partial Dependence
FACE Counterfactuals
RuleFit
Data Explainability
- Data as an (implicit) model
- Data summarisation and description
Exemplars, prototypes and criticismsDimensionality reduction (e.g., t-SNE)
Transparent Modelling
- Rule lists and sets
- Linear models
- Decision trees
- \(k\)-nearest neighbours and \(k\)-means
Post-hoc Explainability
Understandable model of the relation between inputs and outputs
- SHAP
- LIME
Introduction to Machine Learning Explainability Part II Kacper Sokol