Papers
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. (2020). Factors Influencing Approval of Wikipedia Bots. In Hawai'i International Conference on System Science. https://doi.org/10.24251/HICSS.2020.018
HICSS_WikipediaPaper_3.9.new kc (2).pdf (473.63 KB). (2020). Feminist economic geography and the future of work. Epa: Economy And Space, 1–12. https://doi.org/10.1177/0308518X20947101
. (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
Social_Construction_of_ML_in_GS_HICCS2020.pdf (124.3 KB). (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
Impacts_of_ML_for_HAI_2020.pdf (453.59 KB). (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
. (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). (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). 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). The Role of Discretion in the Age of Automation. Computer Supported Cooperative Work: Cscw: An International Journal, 29, 303–333. https://doi.org/10.1007/s10606-020-09371-3
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. 1–9. Retrieved de http://arxiv.org/abs/2004.07213
. (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
. (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
. (2020). White Paper on Artificial Intelligence - A European approach to excellence and trust. In COM(2020) 65 final. Retrieved de https://www.cambridge.org/core/product/identifier/CBO9781107415324A009/type/book_part
. (2019). Algorithms at War: The Promise, Peril, and Limits of Artificial Intelligence. International Studies Review. https://doi.org/10.1093/isr/viz025
. (2019). Applications of artificial intelligence to imaging and diagnosis. Biophysical Reviews, 11(1), 111 - 118. https://doi.org/10.1007/s12551-018-0449-9
. (2019). Artificial Intelligence and the Future of the Drug Safety Professional. Drug Safety, 42(4), 491 - 497. https://doi.org/10.1007/s40264-018-0746-z
. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411 - 1430. https://doi.org/10.1108/IMDS-08-2018-0368
. (2019). Beyond Dyadic Interactions: Considering Chatbots as Community Members (). In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI '19 (1-13). https://doi.org/10.1145/3290605
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. European Journal Of Cancer, 111, 148 - 154. https://doi.org/10.1016/j.ejca.2019.02.005
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001
. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal Of Cancer, 113, 47 - 54. https://doi.org/10.1016/j.ejca.2019.04.001