case-based explanations

**Case-Based Explanations** are an **interpretability approach that explains model predictions by referencing similar past examples** — "the model predicts X because this input is similar to training examples A, B, C which had outcomes Y" — leveraging the human tendency to reason by analogy. **Case-Based Explanation Methods** - **k-Nearest Neighbors**: Find the $k$ most similar training examples in the model's feature space. - **Influence Functions**: Find training examples that most influenced the prediction (mathematically rigorous). - **Prototypes + Criticisms**: Show both typical examples (prototypes) and edge cases (criticisms). - **Contrastive Examples**: Show similar examples from different classes to explain decision boundaries. **Why It Matters** - **Human-Natural**: Humans naturally reason by analogy — case-based explanations match this cognitive style. - **No Model Assumptions**: Works with any model — just need access to representations and training data. - **Domain Expert**: Domain experts can validate predictions by examining whether cited cases are truly similar. **Case-Based Explanations** are **explaining by analogy** — justifying predictions by showing similar historical cases that the model draws upon.