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F. Naretto, Monreale, A., and Giannotti, F., Evaluating the Privacy Exposure of Interpretable Global and Local Explainers, in Submitted at Journal of Artificial Intelligence and Law, 2023.
F. Naretto, Pellungrini, R., Fadda, D., and Rinzivillo, S., EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories, in Discovery Science , 2023.
A. Fedele, Explain and Interpret Few-Shot Learning, in Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023, 2023.
F. Lillo and Ruggieri, S., Estimating the Total Volume of Queries to a Search Engine, IEEE Transactions on Knowledge and Data Engineering, pp. 1-1, 2021.
F. Giannotti, Naretto, F., and Bodria, F., Explainable for Trustworthy AI, in Human-Centered Artificial Intelligence - Advanced Lectures, 18th European Advanced Course on AI, ACAI 2021, Berlin, Germany, October 11-15, 2021, extended and improved lecture notes, 2021.
L. Pappalardo, Rossi, A., Natilli, M., and Cintia, P., Explaining the difference between men’s and women’s football, PLOS ONE, vol. 16, p. e0255407, 2021.
A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts, Sensors, vol. 20, p. 7122, 2020.
V. Voukelatou, Pappalardo, L., Gabrielli, L., and Giannotti, F., Estimating countries’ peace index through the lens of the world news as monitored by GDELT, in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020.
N. Forgó, Hänold, S., van den Hoven, J., Krügel, T., Lishchuk, I., Mahieu, R., Monreale, A., Pedreschi, D., Pratesi, F., and van Putten, D., An ethico-legal framework for social data science, 2020.
R. Cazabet, Boudebza, S., and Rossetti, G., Evaluating community detection algorithms for progressively evolving graphs, arXiv preprint arXiv:2007.08635, 2020.
F. Bodria, Panisson, A., Perotti, A., and Piaggesi, S., Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis., in SEBD, 2020.
O. Lampridis, Guidotti, R., and Ruggieri, S., Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars, in Discovery Science, Cham, 2020.