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Papers

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C
Clifton, J., Clifton, J., Glasmeier, A., & Gray, M.. (2020). When machines think for us: The consequences for work and place. Cambridge Journal Of Regions, Economy And Society, 13, 3–23. https://doi.org/10.1093/cjres/rsaa004
Cockshott, P., & Renaud, K.. (2016). Humans, robots and values. Technology In Society, 45, 19 - 28. https://doi.org/10.1016/j.techsoc.2016.01.002
Coelho, H. (2018). Reflections on the meaning of automated education. Education Policy Analysis Archives, 26, 115. https://doi.org/10.14507/epaa.26.3863
Colbert, A., Yee, N., & George, G.. (2016). The digital workforce and the workplace of the future. Academy Of Management Journal, 59(3), 731 - 739. https://doi.org/10.5465/amj.2016.4003
Data Science for Undergraduates. (2018). Data Science for Undergraduates. https://doi.org/10.17226/25104
Duckworth, P., Graham, L., & Osborne, M.. (2019). Inferring work task automatability from AI expert evidence (V. Conitzer, Hadfield, G., & Vallor, S., Trans.). In the 2019 AAAI/ACM ConferenceProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES '19 (485 - 491). https://doi.org/10.1145/330661810.1145/3306618.3314247
Nathan, L. F. (2018). Creativity, the arts, and the future of work (pp. 283 - 310; J. W. Cook, Ed.). In (pp. 283 - 310). https://doi.org/10.1007/978-3-319-78580-6_9
Crews, A. (2015). The big move toward big data in employment. In Employment& Labor Law Solutions Worldwide. Retrieved de https://www.littler.com/files/wp_big_data_8-04-15.pdf
Croghan, S. M., Carroll, P., Reade, S., Gillis, A. E., & Ridgway, P. F.. (2018). Robot Assisted Surgical Ward Rounds: Virtually Always There. Journal Of Innovation In Health Informatics, 25(1), 041. https://doi.org/10.14236/jhi.v25i1.982
Crone, T., & Zargar, M. Shafeie. (2018). How the anthropormorphization of virtual assistants influences user's trust. Academy Of Management Proceedings, 2018(1), 16328. https://doi.org/10.5465/AMBPP.2018.16328abstract
Crowston, K., & Bolici, F.. (2019). Impacts of machine learning on work. In Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52). https://doi.org/10.24251/HICSS.2019.719
PDF icon Impacts_of_machine_learning_on_work__revision_.pdf (300.76 KB)
Crowston, K., & Bolici, F.. (2020). Impacts of the Use of Machine Learning on Work Design. In 8th International Conference on Human-Agent Interaction. https://doi.org/10.1145/3406499.3415070
PDF icon Impacts_of_ML_for_HAI_2020.pdf (453.59 KB)
Crowston, K., Østerlund, C., & Lee, T. Kyoung. (2017). Blending machine and human learning processes. In Hawai'i International Conference on System Sciences. https://doi.org/10.24251/HICSS.2017.009
PDF icon training v3 to share.pdf (245.57 KB)
Crowston, K. (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.
PDF icon Everyday_automation camera ready.pdf (421.84 KB)
PDF icon GAI_and_skills.pdf (281.94 KB)
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N. C., et al.. (2017). A deep learning approach for quantifying tumor extent. Scientific Reports, 7(1). https://doi.org/10.1038/srep46450
Cui, Y. (2020). Artificial Intelligence and Judicial Modernization. https://doi.org/10.1007/978-981-32-9880-4
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Dahlin, E. (2019). Are robots stealing our jobs?. Socius: Sociological Research For A Dynamic World, 5, 237802311984624. https://doi.org/10.1177/2378023119846249

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