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Papers

Export 11 results:
2018
Goos, M. (2018). The impact of technological progress on labour markets: policy challenges. Oxford Review Of Economic Policy, 34(3), 362 - 375. https://doi.org/10.1093/oxrep/gry002
Arogyaswamy, B., & Hunter, J.. (2018). The impact of technology and globalization on employment and equity. International Journal Of Global Sustainability, 3(1), 49. https://doi.org/10.5296/ijgs.v3i1.14127
Acemoglu, D., & Restrepo, P.. (2018). Implications of technology for growth, factor shares, and employment. American Economic Review, 108(6), 1488 - 1542. https://doi.org/10.1257/aer.20160696
Avati, A., Jung, K., Harman, S., Downing, L., Ng, A., & Shah, N. H.. (2018). Improving palliative care with deep learning. Bmc Medical Informatics And Decision Making, 18(S4). https://doi.org/10.1186/s12911-018-0677-8
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
Mehic, A. (2018). Industrial employment and income inequality: Evidence from panel data. Structural Change And Economic Dynamics, 45, 84 - 93. https://doi.org/10.1016/j.strueco.2018.02.006
Araujo, T. (2018). The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers In Human Behavior, 85, 183 - 189. https://doi.org/10.1016/j.chb.2018.03.051
Rücker, D., Hornfeck, R., & Paetzold, K.. (2018). Investigating ergonomics in the context of human-robot collaboration as a sociotechnical system (Vol. 784, pp. 127 - 135; J. Chen, Ed.). In (Vol. 784, pp. 127 - 135). https://doi.org/10.1007/978-3-319-94346-6_12
Mlynar, J., Alavi, H. S., Verma, H., & Cantoni, L.. (2018). Lecture Notes in Computer ScienceArtificial General IntelligenceTowards a Sociological Conception of Artificial Intelligence (Vol. 10999, pp. 130 - 139; M. Iklé, Franz, A., Rzepka, R., & Goertzel, B., Eds.). In (Vol. 10999, pp. 130 - 139). https://doi.org/10.1007/978-3-319-97676-1
Gill, K. S. (2018). Looking though the Pygmalion Lens. Ai & Society, 33(4), 459 - 465. https://doi.org/10.1007/s00146-018-0866-0
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
Humphries, G., & Huettmann, F.. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. In G. Humphries, Magness, D. R., & Huettmann, F. (Eds.), Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 3 - 26). https://doi.org/10.1007/978-3-319-96978-7_1
Stilgoe, J. (2018). Machine learning, social learning and the governance of self-driving cars. Social Studies Of Science, 48, 25-56. https://doi.org/10.1177/0306312717741687
Seeber, I., Bittner, E., Briggs, R. O., de Vreede, G. - J., de Vreede, T., Druckenmiller, D., et al.. (2018). Machines as Teammates: A Collaboration Research Agenda. In Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2018.055
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Winn, A. N., & Neuner, J. M.. (2018). Making Sure We Don’t Forget the Basics When Using Machine Learning. Jnci: Journal Of The National Cancer Institute, 111(6), 529 - 530. https://doi.org/10.1093/jnci/djy179

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