Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge

2026-09-14

AI Summary

本文通过引入模糊语言词汇来表达专家知识，改进了DiCE方法（命名为DiCEf），以生成语义上可感知且多样化的反事实例子，同时保持成本最小化、稀疏性和多样性。

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 structural constraints that make the features dependent, preventing the use of gradient-based optimisation methods for CFE generation, e.g., DiCE. The paper proposes a continuous data embedding in this linguistic domain and exploits it to define a variant of DiCE that allows personalisation for the explainee, named DiCEf. As illustrated by experimental results on a real-world dataset, this extension of the DiCE method enables the generation of CFEs that are linguistically perceptible while preserving cost minimality, sparsity, and diversity.

Problem

• CounterFactual Examples
• eXplainable Artificial Intelligence
• fuzzy linguistic vocabulary

Innovation

• Fuzzy Linguistic Vocabulary
• CounterFactual Examples
• DiCEf