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Article

Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability

Department of Management Information Systems, National Chengchi University, Taipei 11605, Taiwan
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Sustainability 2020, 12(21), 8758; https://doi.org/10.3390/su12218758
Received: 28 August 2020 / Revised: 14 October 2020 / Accepted: 18 October 2020 / Published: 22 October 2020
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. View Full-Text
Keywords: artificial intelligence; AI-enabled decision making; uncertainty artificial intelligence; AI-enabled decision making; uncertainty
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MDPI and ACS Style

Wu, J.; Shang, S. Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability. Sustainability 2020, 12, 8758. https://doi.org/10.3390/su12218758

AMA Style

Wu J, Shang S. Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability. Sustainability. 2020; 12(21):8758. https://doi.org/10.3390/su12218758

Chicago/Turabian Style

Wu, Junyi, and Shari Shang. 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability" Sustainability 12, no. 21: 8758. https://doi.org/10.3390/su12218758

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