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What do we loose when machines take the decisions?

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  • Thomas Bolander

    (Technical University of Denmark)

Abstract

This paper concerns the technical issues raised when humans are replaced by artificial intelligence (AI) in organisational decision making, or decision making in general. Such automation of human tasks and decision making can of course be beneficial through saving human resources, and through (ideally) leading to better solutions and decisions. However, to guarantee better decisions, the current AI techniques still have some way to go in most areas, and many of the techniques also suffer from weaknesses such as lack of transparency and explainability. The goal of the paper is not to argue against using any kind of AI in organisational decision making. AI techniques have a lot to offer, and can for instance assess a lot more possible decisions—and much faster—than any human can. The purpose is just to point to the weaknesses that AI techniques still have, and that one should be aware of when considering to implement AI to automate human decisions. Significant current AI research goes into reducing its limitations and weaknesses, but this is likely to become a fairly long-term effort. People and organisations might be tempted to fully automate certain crucial aspects of decision making without waiting for these limitations and weaknesses to be reduced—or, even worse, not even being aware of those weaknesses and what is lost in the automatisation process.

Suggested Citation

  • Thomas Bolander, 2019. "What do we loose when machines take the decisions?," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(4), pages 849-867, December.
    • Handle: RePEc:kap:jmgtgv:v:23:y:2019:i:4:d:10.1007_s10997-019-09493-x
    DOI: 10.1007/s10997-019-09493-x
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    References listed on IDEAS

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    1. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    2. Oliveira, Fabio & Kakabadse, Nada & Khan, Nadeem, 2022. "Board engagement with digital technologies: A resource dependence framework," Journal of Business Research, Elsevier, vol. 139(C), pages 804-818.
    3. Fink, Matthias & Maresch, Daniela & Gartner, Johannes, 2023. "Programmed to do good: The categorical imperative as a key to moral behavior of social robots," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    4. Peiyi Jia & Ciprian Stan, 2021. "Artificial Intelligence Factory, Data Risk, and VCs’ Mediation: The Case of ByteDance, an AI-Powered Startup," JRFM, MDPI, vol. 14(5), pages 1-19, May.
    5. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    6. Vesa Tiitola & Maria Marek & Tuomas Korhonen & Teemu Laine, 2023. "Enabling value-in-use with digital healthcare technologies: combining service logic and pragmatic constructivism," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 841-871, September.
    7. Schmutz, Iris & Reinecke, Sven & Manole, Anne-Marie, 2020. "Allocating resources in pricing – which capabilities are worth fostering in the face of AI?," Die Unternehmung - Swiss Journal of Business Research and Practice, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 74(4), pages 349-364.
    8. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2021. "An Artificial Intelligence (Ai)-Readiness and Adoption Framework for Agritech Firms," QBS Working Paper Series 271255, Queen's University Belfast, Queen's Business School.
    9. Su Jung Jee & So Young Sohn, 2023. "Firms’ influence on the evolution of published knowledge when a science-related technology emerges: the case of artificial intelligence," Journal of Evolutionary Economics, Springer, vol. 33(1), pages 209-247, January.

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