Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability

Junyi Wu and Shari Shang
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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

Sustainability, 2020, vol. 12, issue 21, 1-17

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.

Keywords: artificial intelligence; AI-enabled decision making; uncertainty (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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