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Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation

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  • Fügener, A.
  • Grahl, J.
  • Gupta, A.
  • Ketter, W.

Abstract

We study how humans make decisions when they collaborate with an artificial intelligence (AI): each instance of a classification task could be classified by themselves or by the AI. Experimental results suggest that humans and AI who work together can outperform the superior AI when it works alone. However, this only occurred when the AI delegated work to humans, not when humans delegated work to the AI. The AI profited, even from working with low-performing subjects, but humans did not delegate well. This bad delegation performance cannot be explained with algorithm aversion. On the contrary, subjects tried to follow a provided delegation strategy diligently and appeared to appreciate the AI support. However, human results suffered due to a lack of metaknowledge. They were not able to assess their own capabilities correctly, which in turn leads to poor delegation decisions. In contrast to reluctance to use AI, lacking metaknowledge is an unconscious trait. It limits fundamentally how well human decision makers can collaborate with AI and other algorithms. The results have implications for the future of work, the design of human-AI collaborative environments and education in the digital age.

Suggested Citation

  • Fügener, A. & Grahl, J. & Gupta, A. & Ketter, W., 2019. "Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation," ERIM Report Series Research in Management ERS-2019-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  • Handle: RePEc:ems:eureri:115830
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    References listed on IDEAS

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    1. Martin Bichler & Alok Gupta & Wolfgang Ketter, 2010. "Research Commentary ---Designing Smart Markets," Information Systems Research, INFORMS, vol. 21(4), pages 688-699, December.
    2. Micha T. Kahlen & Wolfgang Ketter & Jan van Dalen, 2018. "Electric Vehicle Virtual Power Plant Dilemma: Grid Balancing Versus Customer Mobility," Production and Operations Management, Production and Operations Management Society, vol. 27(11), pages 2054-2070, November.
    3. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    4. Jing Wang & Panagiotis G. Ipeirotis & Foster Provost, 2017. "Cost-Effective Quality Assurance in Crowd Labeling," Information Systems Research, INFORMS, vol. 28(1), pages 137-158, March.
    5. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    6. Dario Floreano & Robert J. Wood, 2015. "Science, technology and the future of small autonomous drones," Nature, Nature, vol. 521(7553), pages 460-466, May.
    7. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    8. Nosek, Brian A. & Ebersole, Charles R. & DeHaven, Alexander Carl & Mellor, David Thomas, 2018. "The Preregistration Revolution," OSF Preprints 2dxu5, Center for Open Science.
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    Keywords

    Future of work; Artificial Intelligence; Deep Learning; Machine Learning; Deep Learning; Human-AI Collaboration; Machine Learning; Intelligent Software Agents;
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