01683nas a2200181   4500008004100000245005600041210005400097300000700151490000700158520116300165100002301328700001901351700002401370700001901394700002101413700002001434856004701454       2018                        eng d00aA survey of methods for explaining black box models0 asurvey of methods for explaining black box models  a930 v513 aIn recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://dl.acm.org/doi/abs/10.1145/3236009