Papers
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. (2015). Four fundamentals of workplace automation. Mckinsey Quarterly, 1–9.
. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46 - 60. https://doi.org/10.1016/j.futures.2017.03.006
. (2018). foo.castr: visualising the future AI workforce. Big Data Analytics, 3(1). https://doi.org/10.1186/s41044-018-0034-z
. (2018). Fedex follows Amazon into the robotic future. The New York Times. Retrieved de https://www.nytimes.com/2018/03/18/technology/fedex-robots.html
. (2017). Exploring the tension between transparency and datification effects of open government IS through the lens of Complex Adaptive Systems. The Journal Of Strategic Information Systems, 26(3), 210 - 232. https://doi.org/10.1016/j.jsis.2017.07.001
. (2019). Exploring and developing policies to future proof Washington’s workers and businesses. In Future of Work Task Force Plan of Action. Retrieved de http://www.wtb.wa.gov/Documents/FutureofWork2018Report.pdf
. (2018). Explanation to Avert Surprise. Iui Workshops. Retrieved de https://www.semanticscholar.org/paper/Explanation-to-Avert-Surprise-Gervasio-Myers/8e3b3d9e9075095a87c96fa9690d2ccbf127234e
. (2018). The evolving role of ICT in the economy. In The London School of Economics and Political Science. Retrieved de http://www.lse.ac.uk/business-and-consultancy/consulting/consulting-reports/the-evolving-role-of-ict-in-the-economy
. (2018). Ethics of using Artificial Intelligence to augment drafting legal documents. Texas A&M Journal Of Property Law, 4(5). Retrieved de https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=1080&context=journal-of-property-law
. (2019). Emotional processes in human-robot interaction during brief cognitive testing. Computers In Human Behavior, 90, 331 - 342. https://doi.org/10.1016/j.chb.2018.08.013
. (2019). Educating those most impacted by artificial intelligence (Vol. 11626, pp. 344 - 349; ). In (Vol. 11626, pp. 344 - 349). https://doi.org/10.1007/978-3-030-23207-8_63
. (2019). Educating those most impacted by artificial intelligence (Vol. 11626, pp. 344 - 349; ). In (Vol. 11626, pp. 344 - 349). https://doi.org/10.1007/978-3-030-23207-8_63
. (2018). The economics of artificial intelligence: Implications for the future of work. In International Labour Office. Retrieved de https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_647306.pdf
. (2019). Does automation influence career decisions among South African students? (). 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
. (2020). Does AI qualify for the job? A bidirectional model mapping labour and AI intensities. In AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (94–100). https://doi.org/10.1145/3375627.3375831
. (2017). Do technological innovations affect unemployment? Some empirical evidence from European countries. Economies, 5(4), 48. https://doi.org/10.3390/economies5040048
. (2016). Degree of Automation in Command and Control Decision Support Systems. In IEEE International Conference on Systems, Man and Cybernetics. Presented at the IEEE International Conference on Systems, Man and Cybernetics. Retrieved de https://ieeexplore.ieee.org/document/7844402
. (2016). Degree of Automation in Command and Control Decision Support Systems. In IEEE International Conference on Systems, Man and Cybernetics. Presented at the IEEE International Conference on Systems, Man and Cybernetics. Retrieved de https://ieeexplore.ieee.org/document/7844402
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001