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The dramatic success of Artificial Intelligence and its applications has been accompanied by an increasing complexity, which makes its comprehension for final users more difficult and damages their trustworthiness. Within this context, the emergence of Explainable AI aims to make intelligent systems decisions more transparent and understandable for human users. In this paper, we propose a framework for the explanation of predictive inference in Bayesian Networks (BN) in natural language todoi:10.2991/eusflat-19.2019.107 dblp:conf/eusflat/Pereira-FarinaB19 fatcat:x7qiz2pxajguzanih33rg7ooim