<?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%">Francesco Bodria</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%">Francesca Naretto</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmarking and Survey of Explanation Methods for Black Box Models</style></title><secondary-title><style face="normal" font="default" size="100%">CoRR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2102.13076</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">abs/2102.13076</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><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%">Ntoutsi, Eirini</style></author><author><style face="normal" font="default" size="100%">Fafalios, Pavlos</style></author><author><style face="normal" font="default" size="100%">Gadiraju, Ujwal</style></author><author><style face="normal" font="default" size="100%">Iosifidis, Vasileios</style></author><author><style face="normal" font="default" size="100%">Nejdl, Wolfgang</style></author><author><style face="normal" font="default" size="100%">Vidal, Maria-Esther</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Papadopoulos, Symeon</style></author><author><style face="normal" font="default" size="100%">Krasanakis, Emmanouil</style></author><author><style face="normal" font="default" size="100%">others</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bias in data-driven artificial intelligence systems—An introductory survey</style></title><secondary-title><style face="normal" font="default" size="100%">Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">e1356</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.</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%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Daniele Fadda</style></author><author><style face="normal" font="default" size="100%">Leonardo Piccini</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Patrizia Lattarulo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big Data and Public Administration: a case study for Tuscany Airports</style></title><secondary-title><style face="normal" font="default" size="100%">SEBD - Italian 	Symposium on  Advanced Database Systems </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Matematicamente.it</style></publisher><pub-location><style face="normal" font="default" size="100%">Ugento, Lecce (Italy)</style></pub-location><isbn><style face="normal" font="default" size="100%">9788896354889</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration  - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned.</style></abstract></record><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%">Sonia Bergamaschi</style></author><author><style face="normal" font="default" size="100%">Emanuele Carlini</style></author><author><style face="normal" font="default" size="100%">Michelangelo Ceci</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Donato Malerba</style></author><author><style face="normal" font="default" size="100%">Mario Mezzanzanica</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Gabriella Pasi</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Raffaele Perego</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big Data Research in Italy: A Perspective</style></title><secondary-title><style face="normal" font="default" size="100%">Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://engineering.org.cn/EN/abstract/article_12288.shtml</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">163</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.</style></abstract><call-num><style face="normal" font="default" size="100%">10-1244/N</style></call-num></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%">Michele Coscia</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Diego Pennacchioli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Behavioral Entropy and Profitability in Retail</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Paris</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer’s behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.</style></abstract></record><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%">Balliu, Alkida</style></author><author><style face="normal" font="default" size="100%">Olivetti, Dennis</style></author><author><style face="normal" font="default" size="100%">Ozalp Babaoglu</style></author><author><style face="normal" font="default" size="100%">Marzolla, Moreno</style></author><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Big Data Analyzer for Large Trace Logs</style></title><secondary-title><style face="normal" font="default" size="100%">Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/article/10.1007/s00607-015-0480-7</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></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%">Balliu, Alkida</style></author><author><style face="normal" font="default" size="100%">Olivetti, Dennis</style></author><author><style face="normal" font="default" size="100%">Ozalp Babaoglu</style></author><author><style face="normal" font="default" size="100%">Marzolla, Moreno</style></author><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BiDAl: Big Data Analyzer for Cluster Traces</style></title><secondary-title><style face="normal" font="default" size="100%">Informatika (BigSys workshop)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1410.1309</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">GI-Edition Lecture Notes in Informatics</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></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%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big data analytics for smart mobility: a case study</style></title><secondary-title><style face="normal" font="default" size="100%">EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1133/paper-57.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Athens, Greece</style></pub-location><accession-num><style face="normal" font="default" size="100%">ISSN - 1613-0073</style></accession-num></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%">Renato Fileto</style></author><author><style face="normal" font="default" size="100%">Marcelo Krger</style></author><author><style face="normal" font="default" size="100%">Nikos Pelekis</style></author><author><style face="normal" font="default" size="100%">Yannis Theodoridis</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Baquara: A Holistic Ontological Framework for Movement Analysis with Linked Data</style></title><secondary-title><style face="normal" font="default" size="100%">Entity Relationship Conference - ER 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Hong Kong</style></pub-location></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%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Maria V Masserotti</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Laura Spinsanti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building Geospatial Ontologies from Geographical Databases</style></title><secondary-title><style face="normal" font="default" size="100%">GeoS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">195-209</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></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%">Maurizio Atzori</style></author><author><style face="normal" font="default" size="100%">Francesco Bonchi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Blocking Anonymity Threats Raised by Frequent Itemset Mining</style></title><secondary-title><style face="normal" font="default" size="100%">ICDM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><pages><style face="normal" font="default" size="100%">561-564</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><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%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounded Nondeterminism of Logic Programs</style></title><secondary-title><style face="normal" font="default" size="100%">Ann. Math. Artif. Intell.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">313-343</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></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%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Robert Jeansoulin</style></author><author><style face="normal" font="default" size="100%">Yannis Theodoridis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Beyond Current Technology: The Perspective of Three EC GIS Projects</style></title><secondary-title><style face="normal" font="default" size="100%">DEXA Workshop</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><pages><style face="normal" font="default" size="100%">510</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></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%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounded Nondeterminism of Logic Programs</style></title><secondary-title><style face="normal" font="default" size="100%">ICLP</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><pages><style face="normal" font="default" size="100%">350-364</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>