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Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.

The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that lately attracts many researchers. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of firstorder logic rules expressed in the terminology of the knowledge graph.

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In this work, we study the use of convolutional neural networks for genre recognition in symbolically represented music. Specifically, we explore the effects of changing network depth, width and kernel sizes while keeping the number of trainable parameters and each block’s receptive field constant. We propose an architecture for handling MIDI data that makes use of multiple resolutions of the input, called Multiple Sequence Resolution Network (MuSeReNet). These networks accept multiple inputs, each at half the original sequence length, representing information at a lower resolution. Through our experiments, we outperform the state-of-the-art for MIDI genre recognition on the topMAGD and MASD datasets. Finally, we adapt various post hoc explainability methods to the domain of symbolic music and attempt to explain the predictions of our best performing network.