Papers

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2019
Gill, K. S. (2019). Designing AI futures: A symbiotic vision (Vol. 1083, pp. 3 - 18; A. G. Kravets, Groumpos, P. P., Shcherbakov, M., & Kultsova, M., Eds.). In (Vol. 1083, pp. 3 - 18). https://doi.org/10.1007/978-3-030-29743-5_1
Goldfarb, A., & Tucker, C.. (2019). Digital Economics. Journal Of Economic Literature, 57(1), 3 - 43. https://doi.org/10.1257/jel.20171452
Lutz, C. (2019). Digital inequalities in the age of artificial intelligence and big data. Human Behavior And Emerging Technologies, 1(2), 141 - 148. https://doi.org/10.1002/hbe2.140
Mbilini, S. N., le Roux, D. B., & Parry, D. A.. (2019). Does automation influence career decisions among South African students? (C. de Villiers & Smuts, H., Trans.). In the South African Institute of Computer Scientists and Information Technologists 2019Proceedings of the South African Institute of Computer Scientists and Information Technologists 2019 on ZZZ - SAICSIT '19 (1 - 10). https://doi.org/10.1145/335110810.1145/3351108.3351137
Groshen, E. L. (2019). Is a driverless future also jobless?. https://doi.org/10.17848/pb2019-17
2018
Data Science for Undergraduates. (2018). Data Science for Undergraduates. https://doi.org/10.17226/25104
Cai, L., Fan, L., Lai, W., LONG, Y., Wang, P., & Xin, X.. (2018). DEFINITION, APPLICATION AND INFLUENCE OF ARTI FICIAL INTELLIGENCE ON DESIGN INDUSTRIES. Landscape Architecture Frontiers, 6(2), 56. https://doi.org/10.15302/J-LAF-20180207
Wright, S. A., & Schultz, A. E.. (2018). Developing an ethical framework. Business Horizons, 61(6), 823 - 832. https://doi.org/10.1016/j.bushor.2018.07.001
Leveringhaus, A. (2018). Developing robots: The need for an ethical framework. European View, 17(1), 37 - 43. https://doi.org/10.1177/1781685818761016
Akaev, A., Rudskoi, A., & Devezas, T.. (2018). Digital economy and the models of income distribution in the society. Shs Web Of Conferences, 44, 00005. https://doi.org/10.1051/shsconf/20184400005
Akaev, A., Sarygulov, A., & Sokolov, V.. (2018). Digital economy: backgrounds, main drivers and new challenges. Shs Web Of Conferences, 44, 00006. https://doi.org/10.1051/shsconf/20184400006
Hershbein, B. (2018). Discussion for JME special issue: APST paper. Journal Of Monetary Economics, 97, 68 - 70. https://doi.org/10.1016/j.jmoneco.2018.05.003
2017
Møller, N. L. Holten, Bjørn, P., Villumsen, J. Christoffe, Hancock, T. C. Hansen, Aritake, T., & Tani, S.. (2017). Data tracking in search of workflows (C. P. Lee, Poltrock, S., Barkhuus, L., Borges, M., & Kellogg, W., Trans.). In the 2017 ACM ConferenceProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 (2153 - 2165). https://doi.org/10.1145/2998181.2998296
Markus, L. M. (2017). Datification, organizational strategy, and is research: What’s the score? . The Journal Of Strategic Information Systems, 26(3), 233 - 241. https://doi.org/10.1016/j.jsis.2017.08.003
Günther, W. Arianne, Mehrizi, M. H. Rezazad, Huysman, M., & Feldberg, F.. (2017). Debating big data: A literature review on realizing value from big data. The Journal Of Strategic Information Systems, 26(3), 191 - 209. https://doi.org/10.1016/j.jsis.2017.07.003
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N. C., et al.. (2017). A deep learning approach for quantifying tumor extent. Scientific Reports, 7(1). https://doi.org/10.1038/srep46450
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T.. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings In Bioinformatics, 19(6), 1236 - 1246. https://doi.org/10.1093/bib/bbx044

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