@article {1476,
	title = {Benchmarking and Survey of Explanation Methods for Black Box Models},
	journal = {CoRR},
	volume = {abs/2102.13076},
	year = {2021},
	url = {https://arxiv.org/abs/2102.13076},
	author = {Francesco Bodria and Fosca Giannotti and Riccardo Guidotti and Francesca Naretto and Dino Pedreschi and S Rinzivillo}
}
@article {1428,
	title = {Bias in data-driven artificial intelligence systems{\textemdash}An introductory survey},
	journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	volume = {10},
	number = {3},
	year = {2020},
	pages = {e1356},
	abstract = {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.},
	doi = {https://doi.org/10.1002/widm.1356},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356},
	author = {Ntoutsi, Eirini and Fafalios, Pavlos and Gadiraju, Ujwal and Iosifidis, Vasileios and Nejdl, Wolfgang and Vidal, Maria-Esther and Salvatore Ruggieri and Franco Turini and Papadopoulos, Symeon and Krasanakis, Emmanouil and others}
}
@conference {1406,
	title = {Black Box Explanation by Learning Image Exemplars in the Latent Feature Space},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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 {\textquotedblleft}morphing{\textquotedblright} 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.},
	isbn = {978-3-030-46150-8},
	doi = {https://doi.org/10.1007/978-3-030-46150-8_12},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-46150-8_12},
	author = {Riccardo Guidotti and Anna Monreale and Matwin, Stan and Dino Pedreschi},
	editor = {Brefeld, Ulf and Fromont, Elisa and Hotho, Andreas and Knobbe, Arno and Maathuis, Marloes and Robardet, C{\'e}line}
}
@conference {834,
	title = {Big Data and Public Administration: a case study for Tuscany Airports},
	booktitle = {SEBD - Italian 	Symposium on  Advanced Database Systems },
	year = {2016},
	month = {06/2016},
	publisher = {Matematicamente.it},
	organization = {Matematicamente.it},
	address = {Ugento, Lecce (Italy)},
	abstract = {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.},
	isbn = {9788896354889},
	url = {http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf},
	author = {Barbara Furletti and Daniele Fadda and Leonardo Piccini and Mirco Nanni and Patrizia Lattarulo}
}
@article {852,
	title = {Big Data Research in Italy: A Perspective},
	journal = {Engineering},
	volume = {2},
	number = {2},
	year = {2016},
	month = {06/2016},
	pages = {163},
	abstract = {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.},
	issn = {print: 2095-8099 / online: 2096-0026},
	doi = {10.1016/J.ENG.2016.02.011},
	url = {http://engineering.org.cn/EN/abstract/article_12288.shtml},
	author = {Sonia Bergamaschi and Emanuele Carlini and Michelangelo Ceci and Barbara Furletti and Fosca Giannotti and Donato Malerba and Mario Mezzanzanica and Anna Monreale and Gabriella Pasi and Dino Pedreschi and Raffaele Perego and Salvatore Ruggieri}
}
@conference {763,
	title = {Behavioral Entropy and Profitability in Retail},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris},
	abstract = {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{\textquoteright}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.},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@article {801,
	title = {A Big Data Analyzer for Large Trace Logs},
	journal = {Computing},
	year = {2015},
	doi = {10.1007/s00607-015-0480-7},
	url = {http://link.springer.com/article/10.1007/s00607-015-0480-7},
	author = {Balliu, Alkida and Olivetti, Dennis and Ozalp Babaoglu and Marzolla, Moreno and Alina Sirbu}
}
@conference {807,
	title = {BiDAl: Big Data Analyzer for Cluster Traces},
	booktitle = {Informatika (BigSys workshop)},
	year = {2014},
	publisher = {GI-Edition Lecture Notes in Informatics},
	organization = {GI-Edition Lecture Notes in Informatics},
	url = {http://arxiv.org/abs/1410.1309},
	author = {Balliu, Alkida and Olivetti, Dennis and Ozalp Babaoglu and Marzolla, Moreno and Alina Sirbu}
}
@conference {574,
	title = {Big data analytics for smart mobility: a case study},
	booktitle = {EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD)},
	year = {2014},
	month = {03/2014},
	address = {Athens, Greece},
	url = {http://ceur-ws.org/Vol-1133/paper-57.pdf},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and Mirco Nanni and Dino Pedreschi}
}
@conference {538,
	title = {Baquara: A Holistic Ontological Framework for Movement Analysis with Linked Data},
	booktitle = {Entity Relationship Conference - ER 2013},
	year = {2013},
	address = {Hong Kong},
	author = {Renato Fileto and Marcelo Krger and Nikos Pelekis and Yannis Theodoridis and Chiara Renso}
}
@conference {DBLP:conf/geos/BaglioniMRS07,
	title = {Building Geospatial Ontologies from Geographical Databases},
	booktitle = {GeoS},
	year = {2007},
	pages = {195-209},
	author = {Miriam Baglioni and Maria V Masserotti and Chiara Renso and Laura Spinsanti}
}
@conference {DBLP:conf/icdm/AtzoriBGP05,
	title = {Blocking Anonymity Threats Raised by Frequent Itemset Mining},
	booktitle = {ICDM},
	year = {2005},
	pages = {561-564},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {DBLP:journals/amai/PedreschiR04,
	title = {Bounded Nondeterminism of Logic Programs},
	journal = {Ann. Math. Artif. Intell.},
	volume = {42},
	number = {4},
	year = {2004},
	pages = {313-343},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/dexaw/GiannottiJT99,
	title = {Beyond Current Technology: The Perspective of Three EC GIS Projects},
	booktitle = {DEXA Workshop},
	year = {1999},
	pages = {510},
	author = {Fosca Giannotti and Robert Jeansoulin and Yannis Theodoridis}
}
@conference {DBLP:conf/iclp/PedreschiR99,
	title = {Bounded Nondeterminism of Logic Programs},
	booktitle = {ICLP},
	year = {1999},
	pages = {350-364},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
