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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

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
Mutual learning in human-AI interaction , Carsten Østerlund; Kevin Crowston; Casey B. Jackson; Marlene Takou-Ayaoh; Yunan Wu; Aggelos K. Katsaggelos
Technological Frames: Interpretations about the Futures of Work and Intelligent Machines on Social Media , Ayşe Öcal; Kevin Crowston
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

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data literacy

Carter, D. (2018). How real is the impact of artificial intelligence? The business information survey 2018. Business Information Review, 35(3), 99 - 115. https://doi.org/10.1177/0266382118790150

data lakes

Carter, D. (2018). How real is the impact of artificial intelligence? The business information survey 2018. Business Information Review, 35(3), 99 - 115. https://doi.org/10.1177/0266382118790150

data governance

Carter, D. (2018). How real is the impact of artificial intelligence? The business information survey 2018. Business Information Review, 35(3), 99 - 115. https://doi.org/10.1177/0266382118790150

data economy

Carter, D. (2018). How real is the impact of artificial intelligence? The business information survey 2018. Business Information Review, 35(3), 99 - 115. https://doi.org/10.1177/0266382118790150

data and analytics

Bean, R. (2017). How big data is empowering AI and machine learning at scale. Mit Slogan Management Review. Retrieved de https://sloanreview.mit.edu/article/how-big-data-is-empowering-ai-and-machine-learning-at-scale/

data

Noonpakdee, W. (2020). The Adoption of Artificial Intelligence for Financial Investment Service. 2020 22Nd International Conference On Advanced Communication Technology (Icact), 396–400.

cybersecurity

Carter, D. (2018). How real is the impact of artificial intelligence? The business information survey 2018. Business Information Review, 35(3), 99 - 115. https://doi.org/10.1177/0266382118790150

cyber-physical technologies

Vishnevsky, V. P., & Chekina, V. D.. (2018). Robot vs. tax inspector or how the fourth industrial revolution will change the tax system: a review of problems and solutions. Journal Of Tax Reform, 4(1), 6 - 26. https://doi.org/10.15826/jtr.2018.4.1.042

Customer feelings

Sidaoui, K., Jaakkola, M., & Burton, J.. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal Of Service Management. https://doi.org/10.1108/JOSM-11-2019-0341

Customer experience

Sidaoui, K., Jaakkola, M., & Burton, J.. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal Of Service Management. https://doi.org/10.1108/JOSM-11-2019-0341

Crowdsourcing

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

criminal justice system

Sushina, T., & Sobenin, A.. (2020). Artificial Intelligence in the Criminal Justice System: Leading Trends and Possibilities. In Proceedings of the 6th International Conference on Social, economic, and academic leadership (ICSEAL-6-2019) (441, 432–437). https://doi.org/10.2991/assehr.k.200526.062

Criminal justice

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

creativity

Theresa, E., Alessandro, B., Michelle, B., Eriksson, T., Bigi, A., Bonera, M., et al.. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The Tqm Journal, ahead-of-p. https://doi.org/10.1108/TQM-12-2019-0303

corresponding author

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

Corporate takeovers

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

Coordination

Malone, T. W., Nickerson, J. V., Laubacher, R. J., Fisher, L. Hesse, de Boer, P., Han, Y., & Ben Towne, W.. (2017). Putting the Pieces Back Together Again: Contest Webs for Large-Scale Problem Solving. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (1661–1674). https://doi.org/10.1145/2998181.2998343

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

Impacts_of_ML_for_HAI_2020.pdf (453.59 KB)

Convolutional neural networks

Kernbach, J. M., & Staartjes, V. E.. (2020). Machine learning-based clinical prediction modeling – A practical guide for clinicians. Artificial Intelligence In Precision Health, 257–278. https://doi.org/10.1016/b978-0-12-817133-2.00011-2

Contract law

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

contests

Malone, T. W., Nickerson, J. V., Laubacher, R. J., Fisher, L. Hesse, de Boer, P., Han, Y., & Ben Towne, W.. (2017). Putting the Pieces Back Together Again: Contest Webs for Large-Scale Problem Solving. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (1661–1674). https://doi.org/10.1145/2998181.2998343

contest webs

Malone, T. W., Nickerson, J. V., Laubacher, R. J., Fisher, L. Hesse, de Boer, P., Han, Y., & Ben Towne, W.. (2017). Putting the Pieces Back Together Again: Contest Webs for Large-Scale Problem Solving. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (1661–1674). https://doi.org/10.1145/2998181.2998343

consulting reports

Castro, D., & McLaughlin, M.. (2019). Ten ways the precautionary principle undermines progress in artificial intelligence. In Information Technology & Innovation Foundation. Retrieved de https://itif.org/publications/2019/02/04/ten-ways-precautionary-principle-undermines-progress-artificial-intelligence

Manyika, J., Lund, S., Chui, M., Woetzel, J., Ko, R., Sanghvi, S., et al.. (2017). Jobs lost, jobs gained: Workforce transactions in a time of automation. In McKinsey Global Institute. Retrieved de https://www.mckinsey.com/~/media/mckinsey/featured%20insights/future%20of%20organizations/what%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/mgi-jobs-lost-jobs-gained-report-december-6-2017.ashx

Madgavkar, A., Manyika, J., Krishnan, M., Ellingrud, K., Yee, L., Woetzel, J., et al.. (2019). The future of women at work. Retrieved de https://www.mckinsey.com/~/media/mckinsey/featured%20insights/gender%20equality/the%20future%20of%20women%20at%20work%20transitions%20in%20the%20age%20of%20automation/mgi-the-future-of-women-at-work-full-report-june%202019.ashx

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