PNNL

Abstract

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis from business, healthcare, and technology. However, these methods have developed largely in the applied mathematics, computer science, and engineering fields, in relative isolation from risk science, specifically applications in the disaster risk domain. The disaster risk field has yet to define itself further as a necessary application domain for AI implementation, by defining how to responsibly balance AI and risk. It is critical to ask the questions: 1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, 2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, 3) What are the synergies between AI and risk science applications, and 4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This paper identifies the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced to highlight the positive qualities of each domain. This paper is the first to develop a classification system for applying risk principles for AI-based applications. The framework of this paper will be useful for risk researchers, data analysts, and business leaders who are actively managing risk in applications with increased self-learning and automation.