Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge
CounterFactual Examples (CFEs) are a cornerstone of eXplainable Artificial Intelligence (XAI), offering local, post hoc, and model-agnostic explanations by identifying minimal input modifications that alter a model's prediction. Yet, in order to be intelligible, these modifications must also be semantically meaningful to the explainee. This paper proposes to integrate knowledge expressed as a fuzzy linguistic vocabulary to represent the explainee's perception and interpretation of the data. The domain induced by this fuzzy vocabulary imposes structural constraints that make the features dependent, preventing the use of gradient-based optimisation methods for CFE generation, e.g., DiCE. Th...
| Authors | Akram Bensalem, Fahima Djelil, Marie-Jeanne Lesot et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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