Papers

Export 11 results:
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
M
Helbing, D. (2018). Machine intelligence: Blessing or curse? It depends on us! (pp. 25 - 39; D. Helbing, Ed.). In (pp. 25 - 39). https://doi.org/10.1007/978-3-319-90869-4_4
Pereira, L. Moniz. (2019). A machine is cheaper than a human for the same task. Ai & Society. https://doi.org/10.1007/s00146-018-0874-0
Johnson, A. E. W., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D., & Clifford, G. D.. (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
Moore, M. M., Slonimsky, E., Long, A. D., Sze, R. W., & Iyer, R. S.. (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
Kühl, N., Goutier, M., Hirt, R., & Satzger, G.. (2019). Machine learning in Artificial Intelligence: Towards a common understanding (T. Bui, Tran.). 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
Humphries, G., & Huettmann, F.. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. In G. Humphries, Magness, D. R., & Huettmann, F. (Eds.), Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 3 - 26). https://doi.org/10.1007/978-3-319-96978-7_1
Stilgoe, J. (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
Kernbach, J. M., & Staartjes, V. E.. (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
Seeber, I., Bittner, E., Briggs, R. O., de Vreede, G. - J., de Vreede, T., Druckenmiller, D., et al.. (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
PDF icon SeeberEtAl_2018_MachinesAsTeammates.pdf (949.98 KB)
Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G. Jan, Elkins, A., et al.. (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
Dolata, M., & Crowston, K.. (2024). Making sense of AI systems development. Ieee Transactions On Software Engineering, 50(1), 123–140. https://doi.org/10.1109/TSE.2023.3338857
PDF icon sensemaking_tse_to_share.pdf (619.73 KB)
Winn, A. N., & Neuner, J. M.. (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
Haryadi, H., Anggraeni, A. Irma, & Ibrahim, D. Nasir. (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

Pages