@conference {1412,
	title = {Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars},
	booktitle = {Discovery Science},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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 {\textendash} 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.},
	isbn = {978-3-030-61527-7},
	doi = {https://doi.org/10.1007/978-3-030-61527-7_24},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-61527-7_24},
	author = {Lampridis, Orestis and Riccardo Guidotti and Salvatore Ruggieri},
	editor = {Appice, Annalisa and Tsoumakas, Grigorios and Manolopoulos, Yannis and Matwin, Stan}
}
@conference {1411,
	title = {Predicting and Explaining Privacy Risk Exposure in Mobility Data},
	booktitle = {Discovery Science},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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{\textquoteright}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.},
	isbn = {978-3-030-61527-7},
	doi = {https://doi.org/10.1007/978-3-030-61527-7_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-61527-7_27},
	author = {Francesca Naretto and Roberto Pellungrini and Anna Monreale and Nardini, Franco Maria and Musolesi, Mirco},
	editor = {Appice, Annalisa and Tsoumakas, Grigorios and Manolopoulos, Yannis and Matwin, Stan}
}
