Titlebook: Explainable Artificial Intelligence; First World Conferen Luca Longo Conference proceedings 2023 The Editor(s) (if applicable) and The Auth

DEMN

潰爛

Insufficient

Power, Politics, and the Civil Sphereingful concepts achieving 4.8% higher concept completeness and 36.5% lower purity scores on average, (iii) provide high-quality concept-based logic explanations for their prediction, and (iv) support effective interventions at test time: these can increase human trust as well as improve model performance.

cancellous-bone

Hormones

TSH582

Hassan Namazi,Mohsen Mosadegh,Mozhgan Hayasi or not. In this paper, we study an out-of-distribution (OoD) detection approach based on a rule-based eXplainable Artificial Intelligence (XAI) model. Specifically, the method relies on an innovative metric, i.e., the weighted mutual information, able to capture the different way decision rules are used in case of in- and OoD data.

ECG769

賠償

Weighted Mutual Information for?Out-Of-Distribution Detection or not. In this paper, we study an out-of-distribution (OoD) detection approach based on a rule-based eXplainable Artificial Intelligence (XAI) model. Specifically, the method relies on an innovative metric, i.e., the weighted mutual information, able to capture the different way decision rules are used in case of in- and OoD data.

停止償付

Communications in Computer and Information Sciencehttp://image.papertrans.cn/e/image/319287.jpg

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