Prototype and Criticism Models

Description

Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes represent the most typical instances that best summarise common patterns in the data, whilst criticisms are outliers or edge cases that are poorly represented by the prototypes. For example, in a dataset of customer transactions, prototypes might be the most representative buying patterns (frequent small purchases, occasional large purchases), whilst criticisms could be unusual behaviours (bulk buyers, one-time high-value customers). This dual approach reveals both what is normal and what is exceptional, helping understand data coverage and model blind spots.

Example Use Cases

Explainability

Analysing medical imaging datasets to identify prototype scans that represent typical healthy tissue patterns and criticism examples showing rare disease presentations, helping radiologists understand what the model considers 'normal' versus cases requiring special attention.

Evaluating credit scoring models by finding prototype borrowers who represent typical low-risk profiles and criticism cases showing unusual but legitimate financial patterns that the model might misclassify, ensuring fair treatment of edge cases.

Fairness

Evaluating representation bias in hiring datasets by examining whether prototypes systematically exclude certain demographic groups and criticisms disproportionately represent minorities, revealing data collection inequities.

Limitations

Selection of prototypes and criticisms is highly dependent on the choice of distance metric or similarity measure, which may not capture all meaningful relationships in the data.
Computational complexity can become prohibitive for very large datasets, as the method often requires pairwise comparisons or optimisation over the entire dataset.
The number of prototypes and criticisms to select is typically a hyperparameter that requires domain expertise to set appropriately.
Results may not generalise well if the training data distribution differs significantly from the deployment data distribution.

Resources

Research Papers

Examples are not Enough, Learn to Criticize! Criticism for Interpretability

Been Kim, Rajiv Khanna, and Oluwasanmi O. Koyejo•Dec 5, 2016

Prototype selection for interpretable classification

Jacob Bien and Robert Tibshirani•Feb 27, 2012

Prototype methods seek a minimal subset of samples that can serve as a distillation or condensed view of a data set. As the size of modern data sets grows, being able to present a domain specialist with a short list of "representative" samples chosen from the data set is of increasing interpretative value. While much recent statistical research has been focused on producing sparse-in-the-variables methods, this paper aims at achieving sparsity in the samples. We discuss a method for selecting prototypes in the classification setting (in which the samples fall into known discrete categories). Our method of focus is derived from three basic properties that we believe a good prototype set should satisfy. This intuition is translated into a set cover optimization problem, which we solve approximately using standard approaches. While prototype selection is usually viewed as purely a means toward building an efficient classifier, in this paper we emphasize the inherent value of having a set of prototypical elements. That said, by using the nearest-neighbor rule on the set of prototypes, we can of course discuss our method as a classifier as well.

Software Packages

alibi

Feb 26, 2019

Algorithms for explaining machine learning models

Documentations

Alibi Explain Documentation

Jan 1, 2019

Related Techniques

Name	Description	Assurance Goals
t-SNE	t-SNE (t-Distributed Stochastic Neighbour Embedding) is a non-linear dimensionality reduction technique that creates 2D or 3D visualisations of high-dimensional data by preserving local neighbourhood relationships. The algorithm converts similarities between data points into joint probabilities in the high-dimensional space, then tries to minimise the divergence between these probabilities and those in the low-dimensional embedding. This approach excels at revealing cluster structures and local patterns, making it particularly effective for exploratory data analysis and understanding complex data relationships that linear methods like PCA might miss.	Explainability
UMAP	UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction technique that creates 2D or 3D visualisations of high-dimensional data by constructing a mathematical model of the data's underlying manifold structure. Unlike t-SNE, UMAP preserves both local neighbourhood relationships and global topology more effectively, using techniques from topological data analysis and Riemannian geometry. This approach often produces more interpretable cluster layouts while maintaining meaningful distances between clusters, making it particularly valuable for exploratory data analysis and understanding complex dataset structures.	Explainability
Influence Functions	Influence functions quantify how much each training example influenced a model's predictions by computing the change in prediction that would occur if that training example were removed and the model retrained. Using calculus and the implicit function theorem, they approximate this 'leave-one-out' effect without actually retraining the model by computing gradients and Hessian information. This mathematical approach reveals which specific training examples were most responsible for pushing the model toward or away from particular predictions, enabling practitioners to trace problematic outputs back to their root causes in the training data.	Explainability Fairness Privacy
Contrastive Explanation Method	The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what makes a prediction distinctive. It identifies 'pertinent negatives' (minimal features that could be removed to change the prediction) and 'pertinent positives' (minimal features that must be present to maintain the prediction). This approach helps users understand not just what led to a decision, but what would need to change to achieve a different outcome, providing actionable insights for decision-making.	Explainability Transparency