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Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability

Author

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  • Junyi Wu

    (Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan)

  • Shari Shang

    (Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan)

Abstract

Artificial intelligence (AI) has been applied to various decision-making tasks. However, scholars have yet to comprehend how computers can integrate decision making with uncertainty management. Obtaining such comprehension would enable scholars to deliver sustainable AI decision-making applications that adapt to the changing world. This research examines uncertainties in AI-enabled decision-making applications and some approaches for managing various types of uncertainty. By referring to studies on uncertainty in decision making, this research describes three dimensions of uncertainty, namely informational, environmental and intentional. To understand how to manage uncertainty in AI-enabled decision-making applications, the authors conduct a literature review using content analysis with practical approaches. According to the analysis results, a mechanism related to those practical approaches is proposed for managing diverse types of uncertainty in AI-enabled decision making.

Suggested Citation

  • Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8758-:d:432755
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    1. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    2. Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    3. Warren E. Walker & Marjolijn Haasnoot & Jan H. Kwakkel, 2013. "Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty," Sustainability, MDPI, vol. 5(3), pages 1-25, March.
    4. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    5. Lipshitz, Raanan & Strauss, Orna, 1997. "Coping with Uncertainty: A Naturalistic Decision-Making Analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 69(2), pages 149-163, February.
    6. John Rust, 2019. "Has Dynamic Programming Improved Decision Making?," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 833-858, August.
    7. T.K. Das & Bing‐Sheng Teng, 1999. "Cognitive Biases and Strategic Decision Processes: An Integrative Perspective," Journal of Management Studies, Wiley Blackwell, vol. 36(6), pages 757-778, November.
    8. James G. March, 1978. "Bounded Rationality, Ambiguity, and the Engineering of Choice," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 587-608, Autumn.
    9. George P. Huber, 1991. "Organizational Learning: The Contributing Processes and the Literatures," Organization Science, INFORMS, vol. 2(1), pages 88-115, February.
    10. Willcock, Simon & Martínez-López, Javier & Hooftman, Danny A.P. & Bagstad, Kenneth J. & Balbi, Stefano & Marzo, Alessia & Prato, Carlo & Sciandrello, Saverio & Signorello, Giovanni & Voigt, Brian & Vi, 2018. "Machine learning for ecosystem services," Ecosystem Services, Elsevier, vol. 33(PB), pages 165-174.
    11. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
    12. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    13. James Love-Koh & Alison Peel & Juan Carlos Rejon-Parrilla & Kate Ennis & Rosemary Lovett & Andrea Manca & Anastasia Chalkidou & Hannah Wood & Matthew Taylor, 2018. "The Future of Precision Medicine: Potential Impacts for Health Technology Assessment," PharmacoEconomics, Springer, vol. 36(12), pages 1439-1451, December.
    14. Nissan-Rozen, Ittay, 2015. "Against Moral Hedging," Economics and Philosophy, Cambridge University Press, vol. 31(3), pages 349-369, November.
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    Cited by:

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    2. Jing Wang & Zeyu Xing & Rui Zhang, 2023. "AI technology application and employee responsibility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.

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