Papers
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. (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637 - 643. https://doi.org/10.1007/s12599-019-00595-2
. (2021). Hybrid intelligence in business networks. Electronic Markets. https://doi.org/10.1007/s12525-021-00481-4
Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf (605.72 KB). (2016). If you're not collecting productivity data, you'll never succeed at work. Havard Business Review. Retrieved de https://hbr.org/2016/02/if-youre-not-collecting-productivity-data-youll-never-succeed-at-work
. (2018). The impact of automation on employment: Just the usual structural change?. Sustainability, 10(5), 1661. https://doi.org/10.3390/su10051661
. (2014). The impact of robotics on employment and motivation of employees in the service sector, with special reference to health care. Safety And Health At Work, 5(4), 198 - 202. https://doi.org/10.1016/j.shaw.2014.07.003
. (2019). Impacts of Artificial Intelligence on Public Administration: A Systematic Literature Review. In 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). Presented at the 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). Retrieved de https://ieeexplore.ieee.org/document/8760778
. (2018). Improving palliative care with deep learning. Bmc Medical Informatics And Decision Making, 18(S4). https://doi.org/10.1186/s12911-018-0677-8
. (2015). Intelligible Models for HealthCare (). In the 21th ACM SIGKDD International ConferenceProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 (1721 - 1730). https://doi.org/10.1145/278325810.1145/2783258.2788613
. (2017). Jobs lost, jobs gained: Workforce transactions in a time of automation. In McKinsey Global Institute. Retrieved de https://www.mckinsey.com/~/media/mckinsey/featured%20insights/future%20of%20organizations/what%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/mgi-jobs-lost-jobs-gained-report-december-6-2017.ashx
. (2020). Key Advantages and Risks of Implementing Artificial Intelligence in the Activities of Professional Communicators. In Proceedings of the 2020 IEEE Communication Strategies in Digital Society Seminar, ComSDS 2020 (82–86). https://doi.org/10.1109/ComSDS49898.2020.9101238
. (2020). Learning occupational task-shares dynamics for the future of work. In AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (36–42). https://doi.org/10.1145/3375627.3375826
. (2018). A literature Analysis of Research on Artificial Intelligence in Management Information System (MIS). In Americas Conference on Information Systems.
. (2006). Literature review of teamwork models (No. CMU-RI-TR-06-50). In (No. CMU-RI-TR-06-50). Retrieved de https://www.researchgate.net/publication/246704657_Literature_Review_of_Teamwork_Models
. (2006). Literature review of teamwork models (No. CMU-RI-TR-06-50). In (No. CMU-RI-TR-06-50). Retrieved de https://www.researchgate.net/publication/246704657_Literature_Review_of_Teamwork_Models
. (2019). Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatric Radiology, 49(4), 509 - 516. https://doi.org/10.1007/s00247-018-4277-7
. (2019). Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatric Radiology, 49(4), 509 - 516. https://doi.org/10.1007/s00247-018-4277-7
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2019). Machine learning in Artificial Intelligence: Towards a common understanding (). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.630
. (2019). A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. Jco Clinical Cancer Informatics, (3), 1 - 17. https://doi.org/10.1200/CCI.18.00056
. (2019). A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. Jco Clinical Cancer Informatics, (3), 1 - 17. https://doi.org/10.1200/CCI.18.00056
. (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
. (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
. (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
SeeberEtAl_2018_MachinesAsTeammates.pdf (949.98 KB)