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Research·arXiv cs.LG·

PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models

Illustration accompanying: PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models

Researchers propose PREF-XAI, a framework that tailors model explanations to individual user preferences rather than applying one-size-fits-all interpretability methods. The approach treats explanation generation as a preference-learning problem, addressing a gap in XAI where cognitive constraints and user goals vary widely.

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PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models · Modelwire