TY - CONF T1 - Global Explanations with Local Scoring T2 - Machine Learning and Knowledge Discovery in Databases Y1 - 2020 A1 - Mattia Setzu A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Franco Turini ED - Cellier, Peggy ED - Driessens, Kurt AB - Artificial 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. JF - Machine Learning and Knowledge Discovery in Databases PB - Springer International Publishing CY - Cham SN - 978-3-030-43823-4 UR - https://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_14 ER -