Contrastive Explanation Method

Description

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.

Example Use Cases

Explainability

Explaining loan application rejections by showing that removing recent late payments (pertinent negative) or adding £5,000 more annual income (pertinent positive) would change the decision to approval, giving applicants clear actionable guidance.

Analysing medical diagnosis models by identifying that removing a specific symptom combination would change a high-risk classification to low-risk, helping clinicians understand the critical diagnostic factors.

Transparency

Providing transparent hiring decisions by showing job candidates exactly which qualifications (pertinent positives) they need to acquire or which application elements (pertinent negatives) might be hindering their success.

Limitations

Computationally expensive as it requires solving an optimisation problem for each individual instance to find minimal perturbations.
Results can be highly sensitive to hyperparameter settings, requiring careful tuning to produce meaningful explanations.
May generate unrealistic or impossible contrastive examples if constraints are not properly specified, leading to impractical recommendations.
Limited to scenarios where feature perturbations are meaningful and actionable, making it less suitable for immutable characteristics or highly constrained domains.

Resources

Research Papers

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives

Amit Dhurandhar et al.•Feb 21, 2018

In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be %necessarily and minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz. certain background pixels). We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology. What is minimally but critically \emph{absent} is an important part of an explanation, which to the best of our knowledge, has not been explicitly identified by current explanation methods that explain predictions of neural networks. We validate our approach on three real datasets obtained from diverse domains; namely, a handwritten digits dataset MNIST, a large procurement fraud dataset and a brain activity strength dataset. In all three cases, we witness the power of our approach in generating precise explanations that are also easy for human experts to understand and evaluate.

Benchmarking and survey of explanation methods for black box models

Bodria Francesco et al.•Jan 1, 2023

Documentations

Interpretable Machine Learning

Jan 1, 2019

Related Techniques

Name	Description	Assurance Goals
SHapley Additive exPlanations	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an importance score to every feature, indicating whether it pushes the prediction towards or away from the average. The method systematically evaluates how predictions change as features are included or excluded, drawing on game theory concepts to ensure a fair distribution of contributions.	Explainability Fairness Reliability
Integrated Gradients	Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of each input feature. It works by accumulating gradients along a straight path from a user-defined baseline input (e.g., a black image or an all-zero vector) to the actual input. This path integral ensures that the attributions satisfy fundamental axioms like completeness (attributions sum up to the difference between the prediction and the baseline prediction) and sensitivity (non-zero attributions for features that change the prediction). The output is a set of importance scores, often visualised as heatmaps, indicating which parts of the input were most influential for the model's decision.	Explainability
Local Interpretable Model-Agnostic Explanations	LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex model's behaviour in a small neighbourhood around a specific instance. It works by creating perturbed versions of the input (e.g., removing words from text, changing pixel values in images, or varying feature values), obtaining the model's predictions for these variations, and training a simple interpretable model (typically linear regression) weighted by proximity to the original instance. The coefficients of this local surrogate model reveal which features most influenced the specific prediction.	Explainability Transparency
ANCHOR	ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of feature conditions that guarantee a specific prediction with high confidence. It searches for 'anchor' conditions (e.g., 'age > 30 AND income < £50k') that ensure the model gives the same prediction at least 95% of the time when those conditions are met. This creates human-readable rules that users can trust as sufficient conditions for understanding why a particular decision was made.	Explainability Transparency