Knowledge Representation for Decision Support in eXplainable Artificial Intelligence Systems

Methodological developments based on artificial intelligence, which we can see in a wide range of practical areas, demonstrate the successful use of methods based on machine learning and deep learning models. One of the main obstacles to the implementation of such models in various applications due to their complexity is the problem of their interpretation. An analysis of the current research state in the new field of artificial intelligence systems development, eXplainable Artificial Intelligence, confirms the urgent need for interpretable explanations of decision-making and action to understand the behavior of the model by the user. The paper provides a brief overview and examples of the models in various fields applications.

Keywords: artificial intelligence systems, interpretability, machine learning models, decision making

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