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New XAI method generates perceptible counterfactual examples using expert knowledge

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces DiCEf, an extension of the DiCE method for generating counterfactual examples (CFEs) in explainable AI (XAI). This enhanced method integrates expert knowledge through a fuzzy linguistic vocabulary to ensure that the generated minimal input modifications are semantically meaningful and perceptible to the user. The approach allows for personalized CFEs that maintain cost minimality, sparsity, and diversity, as demonstrated by experimental results on a real-world dataset. AI

IMPACT Enhances the interpretability of AI models by generating more understandable explanations for users.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for explainable AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New XAI method generates perceptible counterfactual examples using expert knowledge

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The cluster contains a research paper published on arXiv detailing a new method for explainable AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Akram Bensalem (IMT Atlantique - INFO), Fahima Djelil (Lab-STICC\_MOTEL, IMT Atlantique - INFO), Marie-Jeanne Lesot (IMT Atlantique - INFO, Lab-STICC, Lab-STICC\_MOTEL), Gr{\'e}gory Smits (IMT Atlantique - INFO, Lab-STICC, Lab-STICC\_MOTEL) ·

    Diversified and Perceptible Counterfactual Examples Leveraging Expert Knowledge

    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…

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