COVID-19 has illuminated the need for clearer AI-based risk management strategies. Issue 10 (3rd October 2022)

• Record Type: Journal Article
• Authors: Swanson, Tessa; Zelner, Jon; Guikema, Seth
• Is Part Of: Journal of risk research. Volume 25:Issue 10(2022)
• Page Start: 1223
• Page End: 1238
• Publication Date: 2022-10-03
• Subjects: COVID-19 -- Artificial Intelligence (AI) -- AI applications -- disaster response; Technology -- Risk assessment -- Periodicals; Risk management -- Periodicals; Risk assessment -- Periodicals; 658.155
• DOI: 10.1080/13669877.2022.2077411
• Languages: English
• ISSNs: 1366-9877

Abstract

Abstract: Machine learning methods offer opportunities improve pandemic response and risk management by supplementing mechanistic modeling approaches to pandemic planning and response based on diverse sources of data at every level from the local to global scale. However, such solutions rely on the availability of appropriate data as well as communication and dissemination of that data to develop tools and guidance for decision making. A lack of consistency in the reporting and availability of disaggregated, detailed data on COVID-19 in the US has limited the application of artificial intelligence methods and the effectiveness of those methods for projecting the spread and subsequent impacts of this disease in communities. These limitations are missed opportunities for AI methods to make a positive contribution, and they introduce the possibility of inappropriate use of AI methods when not acknowledged. Going forward, governing bodies should develop data collection and sharing standards in collaboration with AI researchers and industry experts to facilitate preparedness for pandemics and other disasters in the future.