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Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
The risks associated with Artificial General Intelligence: A systematic review
Journal of Experimental & Theoretical Artificial Intelligence, Vol.35(5), pp.649-663
2023
Abstract
Artificial General intelligence (AGI) offers enormous benefits for humanity, yet it also poses great risk. The aim of this systematic review was to summarise the peer reviewed literature on the risks associated with AGI. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen articles were deemed eligible for inclusion. Article types included in the review were classified as philosophical discussions, applications of modelling techniques, and assessment of current frameworks and processes in relation to AGI. The review identified a range of risks associated with AGI, including AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks. Several limitations of the AGI literature base were also identified, including a limited number of peer reviewed articles and modelling techniques focused on AGI risk, a lack of specific risk research in which domains that AGI may be implemented, a lack of specific definitions of the AGI functionality, and a lack of standardised AGI terminology. Recommendations to address the identified issues with AGI risk research are required to guide AGI design, implementation, and management.
Details
- Title
- The risks associated with Artificial General Intelligence: A systematic review
- Authors
- Scott McLean (Author) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems ScienceGemma J. M Read (Author) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems ScienceJason Thompson (Author) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems ScienceChris Baber (Author) - University of BirminghamNeville A Stanton (Author) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems SciencePaul M Salmon (Author) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems Science
- Publication details
- Journal of Experimental & Theoretical Artificial Intelligence, Vol.35(5), pp.649-663
- Publisher
- Taylor & Francis
- Date published
- 2023
- DOI
- 10.1080/0952813X.2021.1964003
- ISSN
- 1362-3079; 0952-813X
- Copyright note
- Copyright © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
- Organisation Unit
- Centre for Human Factors and Systems Science; University of the Sunshine Coast, Queensland; School of Health - Psychology; School of Law and Society
- Language
- English
- Record Identifier
- 99569208702621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Artificial Intelligence