01537nas a2200121   4500008004100000245010000041210006900141520107500210100002301285700001801308700002001326856006901346       2020                        eng d00aDoctor XAI: an ontology-based approach to black-box sequential data classification explanations0 aDoctor XAI an ontologybased approach to blackbox sequential data3 aSeveral recent advancements in Machine Learning involve black-box models: algorithms that do not provide human-understandable explanations in support of their decisions. This limitation hampers the fairness, accountability and transparency of these models; the field of eXplainable Artificial Intelligence (XAI) tries to solve this problem providing human-understandable explanations for black-box models. However, healthcare datasets (and the related learning tasks) often present peculiar features, such as sequential data, multi-label predictions, and links to structured background knowledge. In this paper, we introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. We focus on explaining Doctor AI, a multilabel classifier which takes as input the clinical history of a patient in order to predict the next visit. Furthermore, we show how exploiting the temporal dimension in the data and the domain knowledge encoded in the medical ontology improves the quality of the mined explanations.1 aPanigutti, Cecilia1 aPerotti, Alan1 aPedreschi, Dino  uhttps://dl.acm.org/doi/pdf/10.1145/3351095.3372855?download=true00534nas a2200121   4500008004100000245009600041210006900137100002200206700002100228700001800249700002100267856012400288       2020                        eng d00aExplainability Methods for Natural Language Processing: Applications to Sentiment Analysis.0 aExplainability Methods for Natural Language Processing Applicati1 aBodria, Francesco1 aPanisson, André1 aPerotti, Alan1 aPiaggesi, Simone  uhttps://kdd.isti.cnr.it/publications/explainability-methods-natural-language-processing-applications-sentiment-analysis