Papers

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Greco, C., Polonioli, A., & Tagliabue, J.. (2019). Why small data holds the key to the future of artificial intelligence. In 8th International Conference on Data Science, Technology and ApplicationsProceedings of the 8th International Conference on Data Science, Technology and Applications (340 - 347). https://doi.org/10.5220/0007956203400347
Gries, T., & Naude, W.. (2018). Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter. In Maastricht Economic and social Research institute on Innovation and Technology. Retrieved de http://ftp.iza.org/dp12005.pdf
Grimshaw, D. (2020). International organisations and the future of work: How new technologies and inequality shaped the narratives in 2019. In Journal of Industrial Relations (pp. 477–507). https://doi.org/10.1177/0022185620913129
Grote, G., Weik, S., Wafler, T., & Zolch, M.. (1995). Complementary allocation of functions in automated work systems. In International Conference on Human-Computer Interaction (HCI International) (pp. 989-994). Elsevier.
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
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Hacker, P., Krestel, R., Grundmann, S., & Naumann, F.. (2020). Explainable AI under contract and tort law: legal incentives and technical challenges. Artificial Intelligence And Law. https://doi.org/10.1007/s10506-020-09260-6
J. Hackman, R., & Oldham, G. R.. (1980). Work Redesign. Reading, MA: Addison-Wesley.
Hadorn, G. Hirsch, Hoffmann-Riem, H., Biber-Klemm, S., Grossenbacher-Mansuy, W., Joye, D., & Pohl, C.. (2008). Handbook of transdisciplinary research (pp. 427 - 432). In (pp. 427 - 432). https://doi.org/10.1007/978-1-4020-6699-3_28
Haenlein, M., & Kaplan, A.. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5 - 14. https://doi.org/10.1177/0008125619864925
Hager, P., & Beckett, D.. (2019). The emergence of complexity. https://doi.org/10.1007/978-3-030-31839-0
Hanley, D. (2018). Comment on “Should we fear the robot revolution? (The correct answer is yes)” by Andrew Berg, Ed Buffie, and Felipe Zanna. Journal Of Monetary Economics, 97, 149 - 152. https://doi.org/10.1016/j.jmoneco.2018.05.012
Harandi, M., Crowston, K., Jackson, C., & Østerlund, C.. (2020). The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.719
PDF icon Social_Construction_of_ML_in_GS_HICCS2020.pdf (124.3 KB)
Harini, B. (2019). An Extensive Review on Recent Emerging Applications of Artificial Intelligence. Asia-Pacific Journal Of Convergent Research Interchange, 5(2), 79 - 88. https://doi.org/10.21742/apjcri10.21742/apjcri.2019.0610.21742/apjcri.2019.06.09
H. Harvey, B., & Gowda, V.. (2020). How the FDA regulates AI. Academic Radiology, 27(1), 58 - 61. https://doi.org/10.1016/j.acra.2019.09.017
He, E. (2018). Can artificial intelligence make work more human?. Strategic Hr Review, 17(5), 263 - 264. https://doi.org/10.1108/SHR-10-2018-146
J. Heberling, M., & Isaac, B. L.. (2018). iNaturalist as a tool to expand the research value of museum specimens. Applications In Plant Sciences, 6(11), e01193. https://doi.org/10.1002/aps3.1193
Helbing, D. (2018). Machine intelligence: Blessing or curse? It depends on us! (pp. 25 - 39; D. Helbing, Ed.). In (pp. 25 - 39). https://doi.org/10.1007/978-3-319-90869-4_4

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