01510nas a2200181   4500008004100000020002200041245004300063210004300106260005200149520094000201100001801141700002301159700001901182700001901201700001901220700002001239856006901259       2020                        eng d  a978-3-030-43823-400aGlobal Explanations with Local Scoring0 aGlobal Explanations with Local Scoring  aChambSpringer International Publishingc2020//3 aArtificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aCellier, Peggy1 aDriessens, Kurt  uhttps://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_14