Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities
Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities
复制标题
DOI：
10.1111/risa.14038
复制
发表时间：
2022-10
期刊：
影响因子：
3.8
通讯作者：
S. Thekdi;Unal Tatar;J. Santos;S. Chatterjee
中科院分区：
文献类型：
--
作者：
S. Thekdi;Unal Tatar;J. Santos;S. Chatterjee
人工智能（AI）方法已经彻底改变并重新定义了商业、医疗保健和技术领域的数据分析领域。这些方法创新了应用数学、计算机科学和工程领域，并在风险科学，特别是灾害风险领域显示出相当大的潜力。灾害风险领域尚未通过定义如何负责任地平衡人工智能和灾害风险，将自己定义为人工智能实施的必要应用领域。(1)人工智能如何用于灾害风险应用；以及这些应用如何解决风险科学的原则和假设，(2)人工智能用于风险应用的好处是什么；在基于人工智能的应用中应用风险原则和假设的好处是什么？(3)人工智能与风险科学应用之间的协同作用是什么？(4)在基于人工智能的应用中有效使用基本风险原则和假设的特征是什么？本研究开发并传播了一份在线调查问卷，利用风险和人工智能专业人士的专业知识，确定与人工智能和风险相关的最重要特征，然后提出一个衡量人工智能和灾害风险如何平衡的框架。这项研究是第一个为基于人工智能的应用开发应用风险原则的分类系统。通过探索人工智能如何用于管理风险，人工智能方法如何引入新的或额外的风险，以及基本的风险原则和假设是否足以用于基于人工智能的应用，这种分类有助于理解人工智能和风险。
Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (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 study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI‐based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI‐based applications.