Papers

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Aragon, C. R., Poon, S., & Silva, C. T.. (2009). The changing face of digital science (D. R. Olsen, Arthur, R. B., Hinckley, K., Morris, M. Ringel, Hudson, S., & Greenberg, S., Trans.). In the 27th international conference extended abstractsProceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09 (4819). https://doi.org/10.1145/1520340.1520749
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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
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Oldham, G. R., & Fried, Y.. (2016). Job design research and theory: Past, present and future. Organizational Behavior And Human Decision Processes, 136, 20 - 35. https://doi.org/10.1016/j.obhdp.2016.05.002
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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
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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
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Oldham, G. R., & J. Hackman, R.. (2010). Not what it was and not what it will be: The future of job design research. Journal Of Organizational Behavior, 31(2-3), 463 - 479. https://doi.org/10.1002/job.v31:2/310.1002/job.678
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Obermeyer, Z., & Emanuel, E. J.. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. New England Journal Of Medicine, 375(13), 1216 - 1219. https://doi.org/10.1056/NEJMp1606181
Saba, L., Biswas, M., Kuppili, V., Godia, E. Cuadrado, Suri, H. S., Edla, D. Reddy, et al.. (2019). The present and future of deep learning in radiology. European Journal Of Radiology, 114, 14 - 24. https://doi.org/10.1016/j.ejrad.2019.02.038

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