crowston

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2020
Dalgali, A., & Crowston, K.. (2020). Algorithmic Journalism and Its Impacts on Work. In Computation + Journalism Symposium. Presented at the Computation + Journalism Symposium . Retrieved de https://cpb-us-w2.wpmucdn.com/express.northeastern.edu/dist/d/53/files/2020/02/CJ_2020_paper_26.pdf
PDF icon CJ_2020_paper_26.pdf (1.24 MB)
Dalgali, A., & Crowston, K.. (2020). Factors Influencing Approval of Wikipedia Bots. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.018
PDF icon HICSS_WikipediaPaper_3.9.new kc (2).pdf (473.63 KB)
Harandi, M., Crowston, K., Jackson, C., & Østerlund, C.. (2020). The Genie in the Bottle: Different Stakeholders, Different Interpretations of Machine Learning. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.719
PDF icon Social_Construction_of_ML_in_GS_HICCS2020.pdf (124.3 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. (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)
2019
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)
2017
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)
Zevin, M., Coughlin, S., Bahaadini, S., Besler, E., Rohani, N., Allen, S., et al.. (2017). Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science. Classical And Quantum Gravity, 34, 064003. https://doi.org/10.1088/1361-6382/aa5cea