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D. Pedreschi, Ruggieri, S., and Turini, F., The discovery of discrimination, in Discrimination and privacy in the information society, Springer, 2013, pp. 91–108.
A. Romei, Ruggieri, S., and Turini, F., Discrimination discovery in scientific project evaluation: A case study, Expert Systems with Applications, vol. 40, pp. 6064–6079, 2013.
S. Ruggieri, Learning from polyhedral sets, in Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013.
S. Ruggieri, Hajian, S., Kamiran, F., and Zhang, X., Anti-discrimination analysis using privacy attack strategies, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014.
S. Ruggieri, Eirinakis, P., Subramani, K., and Wojciechowski, P., On the complexity of quantified linear systems, Theoretical Computer Science, vol. 518, pp. 128–134, 2014.
M. Aldinucci, Ruggieri, S., and Torquati, M., Decision tree building on multi-core using FastFlow, Concurrency and Computation: Practice and Experience, vol. 26, pp. 800–820, 2014.
S. Mascetti, Ricci, A., and Ruggieri, S., Introduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society, Artificial Intelligence and Law, vol. 22, pp. 109–111, 2014.
A. Romei and Ruggieri, S., A multidisciplinary survey on discrimination analysis, The Knowledge Engineering Review, vol. 29, pp. 582–638, 2014.
P. Eirinakis, Ruggieri, S., Subramani, K., and Wojciechowski, P., On quantified linear implications, Annals of Mathematics and Artificial Intelligence, vol. 71, pp. 301–325, 2014.
S. Ruggieri, Using t-closeness anonymity to control for non-discrimination., Trans. Data Privacy, vol. 7, pp. 99–129, 2014.
R. Guidotti and Ruggieri, S., Assessing the Stability of Interpretable Models, arXiv preprint arXiv:1810.09352, 2018.
G. Amato, Candela, L., Castelli, D., Esuli, A., Falchi, F., Gennaro, C., Giannotti, F., Monreale, A., Nanni, M., Pagano, P., Pappalardo, L., Pedreschi, D., Pratesi, F., Rabitti, F., Rinzivillo, S., Rossetti, G., Ruggieri, S., Sebastiani, F., and Tesconi, M., How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science, in A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, S. Flesca, Greco, S., Masciari, E., and Saccà, D., Eds. Cham: Springer International Publishing, 2018, pp. 287 - 306.
R. Guidotti, Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F., Local Rule-Based Explanations of Black Box Decision Systems, 2018.
D. Pedreschi, Giannotti, F., Guidotti, R., Monreale, A., Pappalardo, L., Ruggieri, S., and Turini, F., Open the Black Box Data-Driven Explanation of Black Box Decision Systems, 2018.
R. Guidotti, Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D., A survey of methods for explaining black box models, ACM computing surveys (CSUR), vol. 51, p. 93, 2018.