@conference {1285,
	title = {Doctor XAI: an ontology-based approach to black-box sequential data classification explanations},
	booktitle = {Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
	year = {2020},
	abstract = {Several 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.},
	doi = {10.1145/3351095.3372855},
	url = {https://dl.acm.org/doi/pdf/10.1145/3351095.3372855?download=true},
	author = {Cecilia Panigutti and Perotti, Alan and Dino Pedreschi}
}
