Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge
Quick summary
arXiv:2609.15609v1 Announce Type: new Abstract: 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 stru
Key takeaways
- arXiv:2609.15609v1 Announce Type: new Abstract: 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.
Why it matters
“Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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