<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Andrienko, Gennady</style></author><author><style face="normal" font="default" size="100%">Barabasi, Albert-Laszlo</style></author><author><style face="normal" font="default" size="100%">Boldrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Bonchi, Francesco</style></author><author><style face="normal" font="default" size="100%">Cattuto, Ciro</style></author><author><style face="normal" font="default" size="100%">Chiaromonte, Francesca</style></author><author><style face="normal" font="default" size="100%">Comandé, Giovanni</style></author><author><style face="normal" font="default" size="100%">Conti, Marco</style></author><author><style face="normal" font="default" size="100%">Coté, Mark</style></author><author><style face="normal" font="default" size="100%">Dignum, Frank</style></author><author><style face="normal" font="default" size="100%">Dignum, Virginia</style></author><author><style face="normal" font="default" size="100%">Domingo-Ferrer, Josep</style></author><author><style face="normal" font="default" size="100%">Ferragina, Paolo</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Helbing, Dirk</style></author><author><style face="normal" font="default" size="100%">Kaski, Kimmo</style></author><author><style face="normal" font="default" size="100%">Kertész, János</style></author><author><style face="normal" font="default" size="100%">Lehmann, Sune</style></author><author><style face="normal" font="default" size="100%">Lepri, Bruno</style></author><author><style face="normal" font="default" size="100%">Lukowicz, Paul</style></author><author><style face="normal" font="default" size="100%">Matwin, Stan</style></author><author><style face="normal" font="default" size="100%">Jiménez, David Megías</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Morik, Katharina</style></author><author><style face="normal" font="default" size="100%">Oliver, Nuria</style></author><author><style face="normal" font="default" size="100%">Passarella, Andrea</style></author><author><style face="normal" font="default" size="100%">Passerini, Andrea</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Pentland, Alex</style></author><author><style face="normal" font="default" size="100%">Pianesi, Fabio</style></author><author><style face="normal" font="default" size="100%">Francesca Pratesi</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author><author><style face="normal" font="default" size="100%">Siebes, Arno</style></author><author><style face="normal" font="default" size="100%">Torra, Vicenc</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Hoven, Jeroen van den</style></author><author><style face="normal" font="default" size="100%">Vespignani, Alessandro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Give more data, awareness and control to individual citizens, and they will help COVID-19 containment</style></title><short-title><style face="normal" font="default" size="100%">Ethics and Information Technology</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021/02/02</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007/s10676-020-09572-w</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">1572-8439</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Matwin, Stan</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Brefeld, Ulf</style></author><author><style face="normal" font="default" size="100%">Fromont, Elisa</style></author><author><style face="normal" font="default" size="100%">Hotho, Andreas</style></author><author><style face="normal" font="default" size="100%">Knobbe, Arno</style></author><author><style face="normal" font="default" size="100%">Maathuis, Marloes</style></author><author><style face="normal" font="default" size="100%">Robardet, Céline</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Black Box Explanation by Learning Image Exemplars in the Latent Feature Space</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Learning and Knowledge Discovery in Databases</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020//</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-46150-8_12</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-46150-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by “morphing” into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lampridis, Orestis</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Appice, Annalisa</style></author><author><style face="normal" font="default" size="100%">Tsoumakas, Grigorios</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Matwin, Stan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars</style></title><secondary-title><style face="normal" font="default" size="100%">Discovery Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020//</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-61527-7_24</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-61527-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Francesca Naretto</style></author><author><style face="normal" font="default" size="100%">Roberto Pellungrini</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Nardini, Franco Maria</style></author><author><style face="normal" font="default" size="100%">Musolesi, Mirco</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Appice, Annalisa</style></author><author><style face="normal" font="default" size="100%">Tsoumakas, Grigorios</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Matwin, Stan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting and Explaining Privacy Risk Exposure in Mobility Data</style></title><secondary-title><style face="normal" font="default" size="100%">Discovery Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020//</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-61527-7_27</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-61527-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.</style></abstract></record></records></xml>