contrastive explanation

**Contrastive Explanations** explain a model's prediction by **contrasting it with an alternative outcome** — answering "why outcome A instead of outcome B?" by identifying features that are present for A (pertinent positives) and absent features that would lead to B (pertinent negatives). **Components of Contrastive Explanations** - **Foil**: The alternative outcome to contrast against (e.g., "why class A and not class B?"). - **Pertinent Positives (PP)**: Minimal features present in the input that justify the predicted class. - **Pertinent Negatives (PN)**: Minimal features absent from the input whose presence would change the prediction. - **CEM**: Contrastive Explanation Method finds both PPs and PNs using optimization. **Why It Matters** - **Human-Like**: Humans naturally explain by contrast — "I chose A over B because of X." - **Focused**: Contrastive explanations highlight only the discriminating features, not all features. - **Diagnostic**: For manufacturing, "why did this wafer fail instead of pass?" is a natural contrastive question. **Contrastive Explanations** are **"why this and not that?"** — focusing explanations on the differences that discriminate between the predicted and alternative outcomes.