01257nas a2200133   4500008004100000245009600041210006900137260001300206520077300219100002300992700001901015700002301034856006601057       2019                        eng d00aInvestigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers0 aInvestigating Neighborhood Generation Methods for Explanations o  bSpringer3 aGiven the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.1 aGuidotti, Riccardo1 aMonreale, Anna1 aCariaggi, Leonardo  uhttps://link.springer.com/chapter/10.1007/978-3-030-16148-4_5