Super-forecasting the 'technological singularity' risks from artificial intelligence

Ani

Abstract

This article investigates cybersecurity (and risk) in the context of ‘technological singularity’ from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.

Citation

Ani, Radanliev, P., De Roure, D., Maple, C., & Ani, U. (2022). Super-forecasting the 'technological singularity' risks from artificial intelligence. Evolving Systems, 13(5), 747-757. https://doi.org/10.1007/s12530-022-09431-7

• Journal Article Type: Article
• Acceptance Date: Feb 20, 2022
• Publication Date: Jun 4, 2022
• Journal: Evolving Systems
• Print ISSN: 1868-6478
• Publisher: Springer Verlag
• Volume: 13
• Issue: 5
• Pages: 747-757
• DOI: https://doi.org/10.1007/s12530-022-09431-7
• Keywords: Super-forecasting, Cyber-risks, Cybersecurity, Artificial intelligence
• Publisher URL: https://link.springer.com/article/10.1007/s12530-022-09431-7
• Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.