Papers
. (2014). Leveraging the cross-cultural capacities of artificial agents as leaders of human virtual teams. In European 10th Conference on Management Leadership and Governance. Presented at the European 10th Conference on Management Leadership and Governance. Retrieved de https://www.researchgate.net/publication/268982256_Leveraging_the_Cross-Cultural_Capacities_of_Artificial_Agents_as_Leaders_of_Human_Virtual_Teams
. (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
. (2018). Looking though the Pygmalion Lens. Ai & Society, 33(4), 459 - 465. https://doi.org/10.1007/s00146-018-0866-0
. (2018). Machine intelligence: Blessing or curse? It depends on us! (pp. 25 - 39; ). In (pp. 25 - 39). https://doi.org/10.1007/978-3-319-90869-4_4
. (2019). A machine is cheaper than a human for the same task. Ai & Society. https://doi.org/10.1007/s00146-018-0874-0
. (2016). Machine learning and decision support in critical care. Proceedings Of The Ieee, 104(2), 444 - 466. https://doi.org/10.1109/JPROC.2015.2501978
. (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 for clinical psychology and clinical neuroscience. In researchgate.net. Retrieved de https://www.researchgate.net/publication/331000572_Machine_Learning_for_Clinical_Psychology_and_Clinical_Neuroscience
. (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 ManagementMachine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? (pp. 353 - 377; ). In (pp. 353 - 377). https://doi.org/10.1007/978-3-319-96978-7_18
. (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
. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. In , Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 3 - 26). https://doi.org/10.1007/978-3-319-96978-7_1
. (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
. (2017). Machine Learning to Detect Anomalies from Application Logs. Retrieved de Druva website: https://www.druva.com/blog/machine-learning-detect-anomalies-application-logs/
. (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). (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
. (2018). Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Systematic Reviews, (77). https://doi.org/10.1186/s13643-018-0740-7
. (2018). Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Systematic Reviews, 7(1). https://doi.org/10.1186/s13643-018-0740-7
. (2024). Making sense of AI systems development. Ieee Transactions On Software Engineering, 50(1), 123–140. https://doi.org/10.1109/TSE.2023.3338857
sensemaking_tse_to_share.pdf (619.73 KB). (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
. (2019). Managing Machines: The governance of artificial intelligence. In FCA Conference on Governance in Banking. Retrieved de https://www.bankofengland.co.uk/speech/2019/james-proudman-speech-at-fca-conference-on-governance-in-banking-london
. (2018). Managing talented worker in the era of new psychological contract. Jurnal Aplikasi Manajemen, 16(1), 20 - 26. https://doi.org/10.21776/ub.jam.2018.016.01.03