Papers
. (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
. (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). 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). 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
. (2017). Machine Learning to Detect Anomalies from Application Logs. Retrieved de Druva website: https://www.druva.com/blog/machine-learning-detect-anomalies-application-logs/
. (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
. (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 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). 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 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
. (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 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
. (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
. (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). A machine is cheaper than a human for the same task. Ai & Society. https://doi.org/10.1007/s00146-018-0874-0
. (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
. (2018). Looking though the Pygmalion Lens. Ai & Society, 33(4), 459 - 465. https://doi.org/10.1007/s00146-018-0866-0
. (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). A literature Analysis of Research on Artificial Intelligence in Management Information System (MIS). In Americas Conference on Information Systems.
. (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
. (2020). Lessons for Supporting Data Science from the Everyday Automation Experience of Spell-Checkers. In Automation Experience across Domains (AutomationXP20), CHI'20 Workshop, 26 April 2020, Virtual. Presented at the Automation Experience across Domains (AutomationXP20), CHI'20 Workshop, 26 April 2020, Virtual, Virtual workshop.
Everyday_automation camera ready.pdf (421.84 KB). (2018). Lecture Notes in Computer ScienceDesign, User Experience, and Usability: Theory and PracticeComparing Human Against Computer Generated Designs: New Possibilities for Design Activity Within Agile Projects (Vol. 10918, pp. 693 - 710; ). In (Vol. 10918, pp. 693 - 710). https://doi.org/10.1007/978-3-319-91797-9_48
. (2018). Lecture Notes in Computer ScienceArtificial General IntelligenceTowards a Sociological Conception of Artificial Intelligence (Vol. 10999, pp. 130 - 139; ). In (Vol. 10999, pp. 130 - 139). https://doi.org/10.1007/978-3-319-97676-1
. (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