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Devesh Narayanan Stanford University
Bas Becker Vrije Universiteit Amsterdam
Luciana Dadderio Edinburgh University
Jocelyn Cranefield Victoria University of Wellington
Wayne Wang Fundação Getulio Vargas

Recent publications

Technological Frames: Interpretations about the Futures of Work and Intelligent Machines on Social Media , Ayşe Öcal; Kevin Crowston
ReelFramer: Human-AI Co-Creation for News-to-Video Translation , Sitong Wang; Samia Menon; Tao Long; Keren Henderson; Dingzeyu Li; Kevin Crowston; Mark Hansen; Jeffrey V. Nickerson; Lydia B. Chilton
Making sense of AI systems development , Mateusz Dolata; Kevin Crowston
AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models , Savvas Petridis; Nicholas Diakopoulos; Kevin Crowston; Mark Hansen; Keren Henderson; Stan Jastrzebski; Jefrey V. Nickerson; Lydia B. Chilton
Project archetypes: A blessing and a curse for AI development , Mateusz Dolata; Kevin Crowston; Gerhard Schwabe

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Wiggers, K. (2018). Researchably's AI parsers medical research for pharmaceutical companies. Retrieved de venturebeat.com website: https://venturebeat.com/2018/10/02/researchablys-ai-parses-medical-research-for-pharmaceutical-companies/

Wilcox, J., & McMurran, L.. (2019). Exploring and developing policies to future proof Washington’s workers and businesses. In Future of Work Task Force Plan of Action. Retrieved de http://www.wtb.wa.gov/Documents/FutureofWork2018Report.pdf

Willcocks, L. (2020). Robo-Apocalypse cancelled? Reframing the automation and future of work debate. In Journal of Information Technology. https://doi.org/10.1177/0268396220925830

Williamson, B. (2017). Computing brains: Learning algorithms and neurocomputation in the smart city. Information, Communication & Society, 20(1), 81 - 99. https://doi.org/10.1080/1369118X.2016.1181194

Willis, M., Duckworth, P., Coulter, A., Meyer, E. T., & Osborne, M.. (2019). The future of health care: Protocol for measuring the potential of task automation grounded in the national health service primary care system. Jmir Research Protocols, 8(4), e11232. https://doi.org/10.2196/11232

WIllis, M., & Meyer, E. T.. (2018). Work that Enables Care: Understanding Tasks, Automation, and the National Health Service. In iConference. Presented at the iConference. https://doi.org/10.1007/978-3-319-78105-1_60

Willson, M. (2017). Algorithms (and the) everyday. Information, Communication & Society, 20(1), 137 - 150. https://doi.org/10.1080/1369118X.2016.1200645

Wilson, L., & Flynn, P. Mac. (2019). The impact of automation technologies for employment in Northern Ireland. Retrieved de https://www.nerinstitute.net/research/the-future-of-work-the-impact-of-automation-techno-1/

H. Wilson, J., & Daugherty, P. R.. (2018). Collaborative intelligence: Humans and AI are joining forces. Retrieved de Havard Business Review website: https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces

Wingfield, N. (2017). As amazon pushes forward with robots, workers find new roles. Retrieved de The New York Times website: https://www.nytimes.com/2017/09/10/technology/amazon-robots-workers.html

Winn, A. N., & Neuner, J. M.. (2018). Making Sure We Don’t Forget the Basics When Using Machine Learning. Jnci: Journal Of The National Cancer Institute, 111(6), 529 - 530. https://doi.org/10.1093/jnci/djy179

Winter, J. Sunrise, & Davidson, E.. (2019). Governance of artificial intelligence and personal health information. Digital Policy, Regulation And Governance, 21(3), 280 - 290. https://doi.org/10.1108/DPRG-08-2018-0048

Wong, C. -sum, & Campion, M. A.. (1991). Development and test of a task level model of motivational job design. Journal Of Applied Psychology, 76, 825–837.

Woods, D. D., Patterson, E. S., & Roth, E. M.. (2002). Can we ever escape from data overload? A cognitive systems diagnosis. Cognition, Technology & Work, 4(1), 22 - 36. https://doi.org/10.1007/s101110200002

Wright, S. A., & Schultz, A. E.. (2018). Developing an ethical framework. Business Horizons, 61(6), 823 - 832. https://doi.org/10.1016/j.bushor.2018.07.001

Wu, J., Ray, J., & Stewart, W. F.. (2010). Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Medical Care, 48. Retrieved de https://www.jstor.org/stable/20720782

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Xu, P., Dherbomez, G., Hery, E., Abidli, A., & Bonnifait, P.. (2018). System architecture of a driverless electric car in the grand cooperative driving challenge. Ieee Intelligent Transportation Systems Magazine, 10(1), 47 - 59. https://doi.org/10.1109/MITS.2017.2776135

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Yang, A. (2019). What happens when the jobs disappear?. Retrieved de https://super.journalxr.com/journal/index.php/TSJ

Yawalkar, V. V. (2019). A study of artificial intelligence and it's role on human resource management. International Journal Of Research And Analytical Review, 6(1). Retrieved de https://www.researchgate.net/publication/331596981_A_Study_of_Artificial_Intelligence_and_its_role_in_Human_Resource_Management

Yeung, K. (2017). ‘Hypernudge’: Big Data as a mode of regulation by design. Information, Communication & Society, 20(1), 118 - 136. https://doi.org/10.1080/1369118X.2016.1186713

Young, A. G., Young, A. G., & Kane, G. C.. (2018). Theorizing human and bot co - production effects on information quality. In Thirty - n inth International Conference on Information Systems,. Presented at the Thirty - n inth International Conference on Information Systems. Retrieved de https://icis2018postergallery.weebly.com/416-social-media-and-digital-collaboration.html

Yuan, L. (2018). How cheap labor drives China’s AI Ambitions. Retrieved de nytimes website: https://www.nytimes.com/2018/11/25/business/china-artificial-intelligence-labeling.html

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Zanona, M. (2017). House gets serious about driverless cars. The Hill. Retrieved de https://thehill.com/policy/transportation/344141-driverless-car-bill-speeds-through-house

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

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

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