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.
Highlights
Various artificial intelligence (AI) technologies have been rapidly developed and implemented for an array of crucial decision-making tasks
Informational uncertainty can be managed by establishing norms, collecting available information, and extrapolating potential information
Environmental uncertainty can be managed through continual exploration and updates, the solicitation of advice, and improved readiness
Summary
Various artificial intelligence (AI) technologies have been rapidly developed and implemented for an array of crucial decision-making tasks. AI-enabled decision-making applications have enabled civilizations to enhance humans’ quality of life. The autonomous vehicle is a novel AI application that provides humans with convenient transport services by autonomously analyzing road conditions and making driving decisions. The autonomous driving technology is expected to provide benefits such as improved life convenience, time efficiency, reduction in congestion, and efficient use of traffic resources. When an autonomous vehicle encounters an unfamiliar situation (such as heavy rain, flooding, or mud puddles), the uncertainty of the road situation increases, and so does the risk to passengers. Autonomous vehicles may cause safety risks if their algorithmic decision-making mechanism cannot address ethical challenges [2]. The aforementioned scenarios indicate that AI applications cannot anticipate every situation and inevitably must deal with various uncertainties in the decision-making process. The uncertainty challenge is critical to AI-enabled decision-making applications; AI technologies must grapple with uncertainty to adapt to the changing world in the long term [3] and ensure the sustainability of AI-enabled decision-making applications
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