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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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    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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