Papers

Export 11 results:
2020
Kernbach, J. M., & Staartjes, V. E.. (2020). Machine learning-based clinical prediction modeling – A practical guide for clinicians. Artificial Intelligence In Precision Health, 257–278. https://doi.org/10.1016/b978-0-12-817133-2.00011-2
Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G. Jan, Elkins, A., et al.. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information And Management, 57, 103174. https://doi.org/10.1016/j.im.2019.103174
Wagner, D. Nicolas. (2020). The nature of the Artificially Intelligent Firm - An economic investigation into changes that AI brings to the firm. Telecommunications Policy, 44, 101954. https://doi.org/10.1016/j.telpol.2020.101954
Spencer, D., & Slater, G.. (2020). No automation please, we're British: Technology and the prospects for work. Cambridge Journal Of Regions, Economy And Society, 13, 117–134. https://doi.org/10.1093/cjres/rsaa003
Sampson, S. (2020). Predicting Automation of Professional Jobs in Healthcare. In Proceedings of the 53rd Hawaii International Conference on System Sciences (3, 3529–3537). https://doi.org/10.24251/hicss.2020.433
McLeay, F., Osburg, V. Sophie, Yoganathan, V., & Patterson, A.. (2020). Replaced by a Robot: Service Implications in the Age of the Machine. Journal Of Service Research. https://doi.org/10.1177/1094670520933354
Willcocks, L. (2020). Robo-Apocalypse cancelled? Reframing the automation and future of work debate. In Journal of Information Technology. https://doi.org/10.1177/0268396220925830
Riemer, K., & Peter, S.. (2020). The robo-apocalypse plays out in the quality, not in the quantity of work. In Journal of Information Technology. https://doi.org/10.1177/0268396220923677
Arduengo, M., & Sentis, L.. (2020). The Robot Economy : Here It Comes. International Journal Of Social Robotics. https://doi.org/10.1007/s12369-020-00686-1
Dottori, D. (2020). Robots and employment: evidence from Italy. In Questioni di Economia e Finanza.
Petersen, A. C. M., Christensen, L. Rune, & Hildebrandt, T. T.. (2020). The Role of Discretion in the Age of Automation. Computer Supported Cooperative Work: Cscw: An International Journal, 29, 303–333. https://doi.org/10.1007/s10606-020-09371-3
Schneider, S., Taylor, G. W., & Kremer, S. C.. (2020). Similarity Learning Networks for Animal Individual Re-Identification-Beyond the Capabilities of a Human Observer. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020 (479, 44–52). https://doi.org/10.1109/WACVW50321.2020.9096925
Ferreira, P., Teixeira, J. Grenha, & Teixeira, L. F.. (2020). Understanding the Impact of Artificial Intelligence on Services. In International Conference on Exploring Services Science (1, 202–213). https://doi.org/10.1007/978-3-030-38724-2
Krupiy, T. Tanya. (2020). A vulnerability analysis : Theorising the impact of artificial intelligence decision-making processes on individuals , society and human diversity from a social justice perspective. Computer Law & Security Review: The International Journal Of Technology Law And Practice, 38, 105429. https://doi.org/10.1016/j.clsr.2020.105429
Clifton, J., Clifton, J., Glasmeier, A., & Gray, M.. (2020). When machines think for us: The consequences for work and place. Cambridge Journal Of Regions, Economy And Society, 13, 3–23. https://doi.org/10.1093/cjres/rsaa004
2019
Garcia-Murillo, M., & MacInnes, I.. (2019). AI’s path to the present and the painful transitions along the way. Digital Policy, Regulation And Governance, 21(3), 305 - 321. https://doi.org/10.1108/DPRG-09-2018-0051
Lysaght, T., Lim, H. Yeefen, Xafis, V., & Ngiam, K. Yuan. (2019). AI-Assisted Decision-making in Healthcare. Asian Bioethics Review, 11(3), 299 - 314. https://doi.org/10.1007/s41649-019-00096-0
Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., et al.. (2019). AI-Based Digital Assistants. Business & Information Systems Engineering, 61(4), 535 - 544. https://doi.org/10.1007/s12599-019-00600-8

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