J. M. Alonso, A. Ramos-soto, C. Castiello, and C. Mencar, Hybrid dataexpert explainable AI beer style classifier, IJCAI-18 Workshop on Explainable Artificial Intelligence, 2018.

O. Biran and C. Cotton, Explanation and justification in machine learning: a survey, IJCAI-17 Workshop on Explainable Artificial Intelligence, 2017.

J. Domingo-ferrer, M. , and J. M. , Practical data-oriented microaggregation for statistical disclosure control, IEEE Transactions on Knowledge and Data Enginering, vol.14, issue.1, pp.189-201, 2002.

J. Domingo-ferrer and V. Torra, Ordinal, continuous and heterogeneous k-anonymity through microaggregation, Data Mining and Knowledge Discovery, vol.11, issue.2, pp.195-212, 2005.

, Draft Ethics Guidelines for Trustworthy AI, European Comission's High-Level Expert Group on Artificial Intelligence, 2018.

, General Data Protection Regulation. Regulation (EU) 2016/679, 2016.

A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Mller, Causability and explainabilty of artificial intelligence in medicine, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p.1312, 2019.

T. Miller, Explanation in artificial intelligence: Insights from the social sciences, Artificial Intelligence, 2018.

C. Molnar, Interpretable machine learning: A guide for making black box models explainable, 2018.

M. T. Ribeiro, S. Singh, and C. Guestrin, Anchors: High-precision modelagnostic explanations, 32nd AAAI Conf. on Artificial Intelligence-AAAI'18 pp, pp.1527-1535, 2018.

P. Samarati and L. Sweeney, Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression, 1998.

S. Singh, M. T. Ribeiro, and C. Guestrin, Programs as black-box explanations, 2016.

E. Strumbelj and I. Kononenko, An efficient explanation of individual classifications using game theory, Journal of Machine Learning Research, vol.11, pp.1-18, 2010.

R. Turner, A model explanation system, IEEE Intl. Workshop on Machine Learning for Signal Processing-MLSP'16, 2016.