Explaining multi-label black-box classifiers for health applications

Panigutti C., Guidotti R., Monreale A., Pedreschi D.

Multilabel Classification  Explainable AI  Health 

Today the state-of-the-art performance in classification is achieved by the so-called âEURoeblack boxesâEUR, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.

Source: Precision Health and Medicine. A Digital Revolution in Healthcare, edited by Arash Shaban-Nejad, Martin Michalowski, pp. 97–110, 2020

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