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Making sense of AI systems development. Ieee Transactions On Software Engineering, 50(1), 123–140. https://doi.org/10.1109/TSE.2023.3338857
. (2024). sensemaking_tse_to_share.pdf (619.73 KB)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
. (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
. (2020). Matching the future capabilities of an artificial intelligence-based software for social media marketing with potential users' expectations. Technological Forecasting And Social Change, 151, 119794. https://doi.org/10.1016/j.techfore.2019.119794
. (2020). A machine is cheaper than a human for the same task. Ai & Society. https://doi.org/10.1007/s00146-018-0874-0
. (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). . (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). 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
. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers In Human Behavior, 90, 215 - 222. https://doi.org/10.1016/j.chb.2018.09.009
. (2019). Massive technological unemployment without redistribution: A case for cautious optimism. Science And Engineering Ethics, 25(5), 1389 - 1407. https://doi.org/10.1007/s11948-018-0070-0
. (2019). . (2019).
The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172 - 181. https://doi.org/10.1016/j.tourman.2019.02.006
. (2019). Moral reasoning at work automation and ethics (pp. 69 - 77). In (pp. 69 - 77). Retrieved de 10.1007/978-3-030-15191-1_8
. (2019). 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). 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
. (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
. (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
. (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
. (2018). SeeberEtAl_2018_MachinesAsTeammates.pdf (949.98 KB)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
. (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 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
. (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
. (2018).