Artificial intelligence, systemic risks, and sustainability
| cg.authorship.types | CGIAR and advanced research institute | en |
| cg.contributor.affiliation | Royal Swedish Academy of Sciences | en |
| cg.contributor.affiliation | Stockholm Resilience Centre | en |
| cg.contributor.affiliation | Princeton University | en |
| cg.contributor.affiliation | University College London | en |
| cg.contributor.affiliation | Global Catastrophic Risk Institute | en |
| cg.contributor.affiliation | Pennsylvania State University | en |
| cg.contributor.affiliation | Leuphana Universitaet Lueneburg | en |
| cg.contributor.affiliation | Medical University of Vienna | en |
| cg.contributor.affiliation | New School | en |
| cg.contributor.affiliation | Cornell University | en |
| cg.contributor.affiliation | CGIAR Platform for Big Data in Agriculture | en |
| cg.contributor.affiliation | International Center for Tropical Agriculture | en |
| cg.contributor.affiliation | University of Cambridge | en |
| cg.contributor.affiliation | Graz University of Technology | en |
| cg.contributor.affiliation | Universidad ICESI | en |
| cg.contributor.affiliation | Cary Institute of Ecosystem Studies | en |
| cg.contributor.affiliation | Stockholm Environment Institute | en |
| cg.contributor.crp | Big Data | |
| cg.contributor.crp | Climate Change, Agriculture and Food Security | |
| cg.creator.identifier | Daniel Jiménez: 0000-0003-4218-4306 | en |
| cg.creator.identifier | Brian King: 0000-0002-7056-9214 | en |
| cg.identifier.doi | https://doi.org/10.1016/j.techsoc.2021.101741 | en |
| cg.isijournal | ISI Journal | en |
| cg.issn | 0160-791X | en |
| cg.journal | Technology in Society | en |
| cg.reviewStatus | Peer Review | en |
| cg.subject.alliancebiovciat | STANDARDS | en |
| cg.subject.impactArea | Climate adaptation and mitigation | |
| cg.subject.sdg | SDG 8 - Decent work and economic growth | en |
| cg.subject.sdg | SDG 9 - Industry, innovation and infrastructure | en |
| cg.subject.sdg | SDG 16 - Peace, justice and strong institutions | en |
| cg.volume | 67 | en |
| dc.contributor.author | Galaz, Victor | en |
| dc.contributor.author | Centeno, Miguel A | en |
| dc.contributor.author | Callahan, Peter W. | en |
| dc.contributor.author | Causevic, Amar | en |
| dc.contributor.author | Patterson, Thayer | en |
| dc.contributor.author | Brass, Irina | en |
| dc.contributor.author | Baum, Seth | en |
| dc.contributor.author | Farber, Darryl | en |
| dc.contributor.author | Fischer, Joern | en |
| dc.contributor.author | Garcia, David | en |
| dc.contributor.author | McPhearson, Timon | en |
| dc.contributor.author | Jiménez, Daniel | en |
| dc.contributor.author | King, Brian | en |
| dc.contributor.author | Larcey, Paul | en |
| dc.contributor.author | Levy, Karen | en |
| dc.date.accessioned | 2021-09-21T16:20:04Z | en |
| dc.date.available | 2021-09-21T16:20:04Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/115075 | |
| dc.title | Artificial intelligence, systemic risks, and sustainability | en |
| dcterms.abstract | Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors. | en |
| dcterms.accessRights | Open Access | |
| dcterms.available | 2021-09-17 | en |
| dcterms.bibliographicCitation | Galaz, V.; Centeno, M.A.; Callahan, P.W.; Causevic, A.; Patterson, T.; Brass, I.; Baum, S.; Farber, D.; Fischer, J.; Garcia, D.; McPhearson, T.; Jiménez, D.; King, B.; Larcey, P.; Levy, K. (2021) Artificial intelligence, systemic risks, and sustainability. Technology in Society 67: 101741. 10 p. ISSN: 0160-791X | en |
| dcterms.extent | 10 p. | en |
| dcterms.issued | 2021-11 | en |
| dcterms.language | en | |
| dcterms.license | CC-BY-4.0 | |
| dcterms.publisher | Elsevier | en |
| dcterms.subject | artificial intelligence | en |
| dcterms.subject | climate change | en |
| dcterms.subject | sustainability | en |
| dcterms.subject | resilience | en |
| dcterms.subject | automation | en |
| dcterms.subject | risk analysis | en |
| dcterms.subject | inteligencia artificial | en |
| dcterms.subject | cambio del clima | en |
| dcterms.subject | sostenibilidad | en |
| dcterms.subject | education | en |
| dcterms.type | Journal Article |