01464nas a2200217   4500008004100000020002200041245008700063210006900150260004400219520069400263100002000957700002100977700002500998700001801023700002001041700001801061700001701079700002301096700001501119856011201134       2019                        eng d  a978-3-030-30754-700aThe Epistle to Cangrande Through the Lens of Computational Authorship Verification0 aEpistle to Cangrande Through the Lens of Computational Authorshi  aChambSpringer International Publishing3 aThe Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open.1 aCorbara, Silvia1 aMoreo, Alejandro1 aSebastiani, Fabrizio1 aTavoni, Mirko1 aCristani, Marco1 aPrati, Andrea1 aLanz, Oswald1 aMesselodi, Stefano1 aSebe, Nicu  uhttps://datasciencephd.eu/publications/epistle-cangrande-through-lens-computational-authorship-verification01736nas a2200169   4500008004100000245007300041210006900114260001300183520117100196653003301367653001501400100002301415700002301438700001901461700002001480856006601500       2019                        eng d00aExplaining multi-label black-box classifiers for health applications0 aExplaining multilabel blackbox classifiers for health applicatio  bSpringer3 aToday the state-of-the-art performance in classification is achieved by the so-called “black boxes”, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.10aExplainable Machine Learning10aHealthcare1 aPanigutti, Cecilia1 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-24409-5_901418nas a2200121   4500008004100000245011100041210006900152490000700221520089200228100002401120700002301144856012901167       2018                        eng d00aECHO CHAMBERS E POLARIZZAZIONE Uno sguardo critico sulla diffusione dell’informazione nei social network0 aECHO CHAMBERS E POLARIZZAZIONE Uno sguardo critico sulla diffusi0 v203 aUnderstanding the algorithms that contribute to the formation of our daily reality requires an in-depth look at how information is disseminated in online social networks (OSN). In this article, we will observe how news propagation is restricted by the presence of virtual borders that limit the interaction between users. This phenomenon, known as "echo chamber" formation, has the effect of polarizing the public debate on conflicting positions. Inside an echo chamber, information is not conveyed through a horizontal exchange between users, but due to the presence of like or follower aggregators, called hubs. This analysis will be carried out considering a casestudy in two of the main OSNs: Facebook and Twitter. From the study of user interaction networks we will observe how the algorithmic choices made are crucial to the polarization of the debate around a topic of discussion.1 aCarugno, Costantino1 aRadicioni, Tommaso  uhttps://thelabsquarterly.files.wordpress.com/2019/04/2018.4-the-labs-quarterly-7.-costantino-carugno-tommaso-radicioni-1.pdf