Dear Colleagues,

Symbolic methods, also called interpretable or white-box methods, were one of the first methods developed within the machine learning area. These methods are still being developed and they find practical applications particularly in knowledge-discovery tasks. In predictive analytics complex approaches (complex AI/ML models) such as boosting, bagging and deep learning usually achieve better results than white-box methods. However, the explanation of a decision-making process of complex AI/ML models is difficult and, without some additional assumptions, often impossible. For this reason, such models are called black-boxes. The dynamic growth of XAI (Explainable Artificial Intelligence) has been recently stimulated by the necessity to explain decisions made by complex AI/ML systems. In this domain the most progressive development has been observed in local, so- called instance-level explanation (i.e., explanation of reasons for making specific decision for a given example). The global or dataset-level XAI still requires intensive research. Generally, the method of global explanation should help the user to understand how the AI/ML model makes decisions globally, for example, about the patterns of right and wrong decisions made by the AI/ML model. In this context, white-box based approximations of complex AI/ML models may play an important role. Specifically, in recent years research on approximation of decisions made by black-box models using white-box approaches has been done.

This Special Issue focuses on new methods of induction of interpretable AI/ML models (rules, trees, graphs, etc.) in data mining and knowledge discovery. The methods for concept learning, contrast set mining, action mining, regression and censored data analysis are welcome. The Special Issue covers also all proposals related to white-box based XAI dedicated to global explanation of decisions made by the complex AI/ML models.

You may choose our Joint Special Issue in Applied Sciences.

Prof. Dr. Marek Sikora
Guest Editor

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• knowledge discovery
• white-box ML
• explainable artificial intelligence
• decision tree and rule induction
• rough sets