Papers

Export 11 results:
2020
Acemoglu, D., Lelarge, C., & Restrepo, P.. (2020). Competing with Robots: Firm-Level Evidence from France. Aea Papers And Proceedings, 110, 383–388. https://doi.org/10.1257/pandp.20201003
Gerber, A., Derckx, P., Döppner, D. A., & Schoder, D.. (2020). Conceptualization of the Human-Machine Symbiosis – A Literature Review. In Proceedings of the 53rd Hawaii International Conference on System Sciences (3, 289–298). https://doi.org/10.24251/hicss.2020.036
Završnik, A.. (2020). Criminal justice, artificial intelligence systems, and human rights. Era Forum, 20, 567–583. https://doi.org/10.1007/s12027-020-00602-0
Mart'nez-Plumed, F., Tolan, S. 'l, Pesole, A., Hern'ndez-Orallo, J., Fern'ndez-Mac'as, E., & G'mez, E.. (2020). Does AI qualify for the job? A bidirectional model mapping labour and AI intensities. In AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (94–100). https://doi.org/10.1145/3375627.3375831
Huang, M. Hui, & Rust, R. T.. (2020). Engaged to a Robot? The Role of AI in Service. Journal Of Service Research. https://doi.org/10.1177/1094670520902266
de Ágreda, Á. Gómez. (2020). Ethics of autonomous weapons systems and its applicability to any AI systems. Telecommunications Policy, 5, 101953. https://doi.org/10.1016/j.telpol.2020.101953
Hacker, P., Krestel, R., Grundmann, S., & Naumann, F.. (2020). Explainable AI under contract and tort law: legal incentives and technical challenges. Artificial Intelligence And Law. https://doi.org/10.1007/s10506-020-09260-6
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)
Reid-musson, E., Cockayne, D., Frederiksen, L., & Worth, N.. (2020). Feminist economic geography and the future of work. Epa: Economy And Space, 1–12. https://doi.org/10.1177/0308518X20947101
Suran, S., Pattanaik, V., & Draheim, D.. (2020). Frameworks for collective intelligence: A systematic literature review. In ACM Computing Surveys (pp. 1–36). https://doi.org/10.1145/3368986
Abeliansky, A. L., Algur, E., Bloom, D. E., & Prettner, K.. (2020). The Future of Work: Meeting the Global Challenge of Demographic Change and Automation. International Labour Review, 1–28. https://doi.org/10.1111/ilr.12168
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)
H. Harvey, B., & Gowda, V.. (2020). How the FDA regulates AI. Academic Radiology, 27(1), 58 - 61. https://doi.org/10.1016/j.acra.2019.09.017
Bærøe, K., Miyata-Sturm, A., & Henden, E.. (2020). How to achieve trustworthy artificial intelligence for health. Bulletin Of The World Health Organization, 98, 257–262. https://doi.org/10.2471/BLT.19.237289
Delfanti, A., & Frey, B.. (2020). Humanly Extended Automation or the Future of Work Seen through Amazon Patents. Science, Technology, & Human Values, 016224392094366. https://doi.org/10.1177/0162243920943665
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)
Jung, J. Hwa, & Lim, D. Geon. (2020). Industrial robots, employment growth, and labor cost: A simultaneous equation analysis. Technological Forecasting And Social Change, 159, 120202. https://doi.org/10.1016/j.techfore.2020.120202
Grimshaw, D. (2020). International organisations and the future of work: How new technologies and inequality shaped the narratives in 2019. In Journal of Industrial Relations (pp. 477–507). https://doi.org/10.1177/0022185620913129
Azarova, L., Kudryavtseva, M., & Sharakhina, L.. (2020). Key Advantages and Risks of Implementing Artificial Intelligence in the Activities of Professional Communicators. In Proceedings of the 2020 IEEE Communication Strategies in Digital Society Seminar, ComSDS 2020 (82–86). https://doi.org/10.1109/ComSDS49898.2020.9101238
Das, S., Steffen, S., Clarke, W., Reddy, P., Brynjolfsson, E., & Fleming, M.. (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
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)

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