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Current XAI evaluation methods fall short, risking the effectiveness of bias detection and concept unlearning in evolving data environments.
Stop guessing what explanations users want: PREF-XAI learns personalized explanations by directly modeling user preferences over rule-based explanations.
Achieve human-readable interpretability in medical tabular data classification without sacrificing accuracy by learning and comparing against prototypical patient feature subsets.
