02525nas a2200205   4500008004100000020001400041245007100055210006900126260001600195300001100211490000800222520189700230100001802127700002302145700001902168700001902187700002002206700002102226856007202247       2021                        eng d  a0004-370200aGLocalX - From Local to Global Explanations of Black Box AI Models0 aGLocalX From Local to Global Explanations of Black Box AI Models  c2021/05/01/  a1034570 v2943 aArtificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLocalX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S000437022100008401510nas 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_1400344nas a2200109   4500008004100000245003600041210003600077260000900113100001800122700002100140856007300161       2016                        eng d00aSPARQL Queries over Source Code0 aSPARQL Queries over Source Code  bIEEE1 aSetzu, Mattia1 aAtzori, Maurizio  uhttps://kdd.isti.cnr.it/publications/sparql-queries-over-source-code