Brust, Matthias R. ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Bouvry, Pascal ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Danoy, Grégoire ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)
Talbi, El-Ghazali; Polytech’Lille, University Lillie - Inria Lille, France
External co-authors :
yes
Language :
English
Title :
Design Challenges of Trustworthy Artificial Intelligence Learning Systems
Publication date :
2020
Event name :
Asian Conference on Intelligent Information and Database Systems (ACIIDS)
Event date :
23-03-2020
Audience :
International
Main work title :
Intelligent Information and Database Systems - 12th Asian Conference ACIIDS 2020, Phuket, Thailand, March 23-26, 2020, Companion Proceedings
Publisher :
Springer
Collection name :
Communications in Computer and Information Science
ISO/IEC PD TR 24028: Information technology-Artificial Intelligence (AI)-Overview of trustworthiness in Artificial Intelligence. Standard, International Organization for Standardization, Geneva, CH
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
Andronache, A., Brust, M.R., Rothkugel, S.: Hycast-podcast discovery in mobile networks. In: Proceedings of the 3rd ACM Workshop on Wireless Multimedia Networking and Performance Modeling, pp. 27–34. ACM (2007)
Bassett, D.S., Zurn, P., Gold, J.I.: On the nature and use of models in network neuroscience. Nat. Rev. Neurosci. 19, 566–578 (2018)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)
Brust, M.R., Akbaş, M.I., Turgut, D.: VBCA: a virtual forces clustering algorithm for autonomous aerial drone systems. In: 2016 Annual IEEE Systems Conference (SysCon), pp. 1–6. IEEE (2016)
Brust, M.R., Frey, H., Rothkugel, S.: Adaptive multi-hop clustering in mobile networks. In: Proceedings of the 4th International Conference on Mobile Technology, Applications, and Systems and the 1st International Symposium on Computer Human Interaction in Mobile Technology, pp. 132–138. ACM (2007)
Brust, M.R., Frey, H., Rothkugel, S.: Dynamic multi-hop clustering for mobile hybrid wireless networks. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 130–135. ACM (2008)
CEN-CENELEC: Artificial Intelligence, Blockchain and Distributed Ledger Technologies (2019). https://www.cencenelec.eu/standards/Topics/ArtificialIntell-igence
Chen, J., Brust, M., Kiremire, A., Phoha, V.: Modeling privacy settings of an online social network from a game-theoretical perspective. In: IEEE Collaborate-Com (2013)
Cihon, P.: Standards for AI governance: international standards to enable global coordination in AI research & development (2019)
Dilmaghani, S., Brust, M.R., Danoy, G., Cassagnes, N., Pecero, J., Bouvry, P.: Privacy and security of big data in AI systems: a research and standards perspective. In: Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2019), International Workshop on Privacy and Security of Big Data (PSBD 2019). IEEE BigData (2019)
Dilmaghani, S., Brust, M.R., Piyatumrong, A., Danoy, G., Bouvry, P.: Link definition ameliorating community detection in collaboration networks. Front. Big Data 2, 22 (2019)
Hinnefeld, J.H., Cooman, P., Mammo, N., Deese, R.: Evaluating fairness metrics in the presence of dataset bias. arXiv preprint arXiv:1809.09245 (2018)
Hong, S., et al.: Discriminating topology in galaxy distributions using network analysis. Mon. Not. R. Astron. Soc. 459(3), 2690–2700 (2016)
Mai, S.T., He, X., Feng, J., Böhm, C.: Efficient anytime density-based clustering. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 112–120 (2013)
Rao, B., Minakakis, L.: Evolution of mobile location-based services. Commun. ACM 46(12), 61–65 (2003)
Rubinstein, B.I., et al.: Antidote: understanding and defending against poisoning of anomaly detectors. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet measurement (2009)
Sha, Z., et al.: A network-based approach to modeling and predicting product coconsideration relations. Complexity 2018, 14 (2018)
Shirinivas, S., Vetrivel, S., Elango, N.: Applications of graph theory in computer science an overview. Int. J. Eng. Sci. Technol. 2(9), 4610–4621 (2010)
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (SP), pp. 3–18 (2017)
Siegler, M.G.: Eric schmidt: Every 2 days we create as much information as we did up to 2003, August 2010. http://social.techcrunch.com/2010/08/04/schmidt-data/. Accessed 7 Mar 2018
Steinhardt, J., Koh, P.W.W., Liang, P.S.: Certified defenses for data poisoning attacks. In: Advances in Neural Information Processing Systems (2017)
Tramèr, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: 25th USENIX Security Symposium (2016)
Wall, M.: Biased and wrong? Facial recognition tech in the dock, July 2019. https://www.bbc.com/news/business-48842750
Wu, F.J., Brust, M.R., Chen, Y.A., Luo, T.: The privacy exposure problem in mobile location-based services. In: Global Communications Conference (GLOBE-COM), 2016 IEEE, pp. 1–7. IEEE (2016)
Wu, J.: Advances in K-means Clustering: A Data Mining Thinking. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29807-3
Xu, R., Wunsch 2nd, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
Yang, J., Zhang, X.D.: Predicting missing links in complex networks based on common neighbors and distance. Sci. Rep. 6, 38208 (2016)
Zhang, S.X., Roberts, R.E., Farabee, D.: An analysis of prisoner reentry and parole risk using COMPAS and traditional criminal history measures. Crime Delinq. 60, 167–192 (2014)
