Explainable AI using MAP-independence

Abstract

In decision support systems the motivation and justification of the system’s diagnosis or classification is crucial for the acceptance of the system by the human user. In Bayesian networks a diagnosis or classification is typically formalized as the computation of the most probable joint value assignment to the hypothesis variables, given the observed values of the evidence variables (generally known as the MAP problem). While solving the MAP problem gives the most probable explanation of the evidence, the computation is a black box as far as the human user is concerned and it does not give additional insights that allow the user to appreciate and accept the decision. For example, a user might want to know to what extent a variable was relevant for the explanation. In this paper we introduce a new concept, MAP-independence, which tries to formally capture this notion of relevance, and explore its role towards a justification of an inference to the best explanation.