Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations
Concept-based explanations are a prevalent way to explain the decisions of complex black-box methods through semantically meaningful, human interpretable concepts. To attribute the contribution of such concepts to a model's decisions, feature attribution methods are used to quantify how strongly each concept contributes to a model output. These attributions are typically computed for a single output class and therefore answer a non-contrastive "why P?" question. In many situations, however, such as cases of misclassification, class confusion, and low- margin predictions, the more natural question to ask is "why P rather than Q?". We introduce contrastive concept importance, which attribut...
| Authors | Roel Visser, Isaac Roberts, Barbara Hammer |
|---|---|
| Primary category | cs.LG |
| Published | 30 July 2026 |
| Last updated at source | 18 September 2026 |
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