Keywords
collective intelligence, predictive models, hybrid groups, big data, thick data
Abstract
An increasing proportion of decisions, design choices, and predictions are being made by hybrid groups consisting of humans and artificial intelligence (AI). In this paper, we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks, the primary use of AI. Our analysis relies on interpretive and generative signal frameworks as well as a distinction between the big data used by AI and the thick, often narrative data used by humans. We derive several conditions on accuracy and correlation necessary for humans to remain in the loop. We conclude that human adaptability along with the potential for atypical cases that mislead AI will likely mean that humans always add value on predictive tasks.
Publisher
Tsinghua University Press
Recommended Citation
Hong, Lu; Lamberson, PJ; and Page, Scott E
(2021)
"Hybrid Predictive Ensembles: Synergies Between Human and Computational Forecasts,"
Journal of Social Computing: Vol. 2:
Iss.
2, Article 1.
DOI: https://doi.org/10.23919/JSC.2021.0009
Available at:
https://dc.tsinghuajournals.com/journal-of-social-computing/vol2/iss2/1
Included in
Computer Engineering Commons, Computer Sciences Commons, Science and Technology Studies Commons