R. Agrawal and J. Kiernan, Watermarking relational databases, VLDB'02: Proceedings of the 28th International Conference on Very Large Databases, pp.155-166, 2002.

P. Frühwirt, P. Kieseberg, S. Schrittwieser, M. Huber, and E. Weippl, Innodb database forensics: Reconstructing data manipulation queries from redo logs, Availability, Reliability and Security, p.2012

, Seventh International Conference on, pp.625-633, 2012.

S. Garfinkel and H. Richter-lipford, Usable security: History, themes, and challenges, Synthesis Lectures on Information Security, Privacy, and Trust, vol.5, issue.2, pp.1-124, 2014.
DOI : 10.2200/s00594ed1v01y201408spt011

J. Samuel, E. J. Gershman, J. B. Horvitz, and . Tenenbaum, Computational rationality: A converging paradigm for intelligence in brains, minds, and machines, Science, vol.349, issue.6245, pp.273-278, 2015.

R. Goebel, A. Chander, K. Holzinger, F. Lecue, Z. Akata et al., Explainable ai: the new 42?, Springer Lecture Notes in Computer Science LNCS 11015, pp.xx-xx
DOI : 10.1007/978-3-319-99740-7_21
URL : https://hal.archives-ouvertes.fr/hal-01934928

. Springer, , 2018.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

D. Gunning, Explainable artificial intelligence (XAI), 2016.

J. Hendler, Avoiding another ai winter, IEEE Intelligent Systems, vol.23, issue.2, pp.2-4, 2008.
DOI : 10.1109/mis.2008.20

J. Hernndez-orallo, The measure of all minds: evaluating natural and artificial intelligence, 2016.

K. Holzinger, K. Mak, P. Kieseberg, and A. Holzinger, Can we trust machine learning results? artificial intelligence in safety-critical decision support, ERCIM News, vol.112, issue.1, pp.42-43, 2018.

S. Islam, H. Mouratidis, and E. R. Weippl, An empirical study on the implementation and evaluation of a goal-driven software development risk management model, Information and Software Technology, vol.56, issue.2, pp.117-133, 2014.

M. I. Jordan and T. M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science, vol.349, issue.6245, pp.255-260, 2015.
DOI : 10.1126/science.aaa8415

P. Kieseberg, S. Schrittwieser, M. Mulazzani, I. Echizen, and E. Weippl, An algorithm for collusion-resistant anonymization and fingerprinting of sensitive microdata. Electronic Markets, vol.24, pp.113-124, 2014.
DOI : 10.1007/s12525-014-0154-x

P. Kieseberg, E. Weippl, and A. Holzinger, Trust for the doctor-inthe-loop, ERCIM news, vol.104, pp.32-33, 2016.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

B. Malle, P. Kieseberg, S. Schrittwieser, and A. Holzinger, Privacy aware machine learning and the right to be forgotten, ERCIM News, vol.107, issue.10, pp.22-25, 2016.

J. Mccarthy, Programs with common sense. pages 75-91. RLE and MIT Computation Center, 1960.

J. Mccarthy, From here to human-level ai, Artificial Intelligence, vol.171, issue.18, pp.1174-1182, 2007.

S. Olhede and P. Wolfe, The ai spring of, Significance, vol.15, issue.3, pp.6-7, 2018.

J. Peters, D. Janzing, and B. Schlkopf, Elements of causal inference: foundations and learning algorithms, 2017.

S. Schrittwieser, P. Kieseberg, I. Echizen, S. Wohlgemuth, N. Sonehara et al., An algorithm for k-anonymity-based fingerprinting, International Workshop on Digital Watermarking, pp.439-452, 2011.

Y. Frank and . Shih, Digital watermarking and steganography: fundamentals and techniques, 2017.

D. Mitchell, M. Swanson, A. H. Kobayashi, and . Tewfik, Multimedia dataembedding and watermarking technologies, Proceedings of the IEEE, vol.86, issue.6, pp.1064-1087, 1998.

A. M. Turing, Computing machinery and intelligence, vol.Mind, pp.433-460, 1950.