TY - JOUR

T1 - Artificial intelligence, systemic risks, and sustainability

AU - Galaz, Victor

AU - Centeno, Miguel A.

AU - Callahan, Peter W.

AU - Causevic, Amar

AU - Patterson, Thayer

AU - Brass, Irina

AU - Baum, Seth

AU - Farber, Darryl

AU - Fischer, Joern

AU - Garcia, David

AU - McPhearson, Timon

AU - Jimenez, Daniel

AU - King, Brian

AU - Larcey, Paul

AU - Levy, Karen

PY - 2021/11/1

Y1 - 2021/11/1

N2 - 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.

AB - 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.

KW - Anthropocene

KW - Artificial intelligence

KW - Automation

KW - Climate change

KW - Digitalization

KW - Resilience

KW - Social-ecological systems

KW - Sustainability

KW - Systemic risks

KW - Environmental planning

UR - http://www.scopus.com/inward/record.url?scp=85114991759&partnerID=8YFLogxK

U2 - 10.1016/j.techsoc.2021.101741

DO - 10.1016/j.techsoc.2021.101741

M3 - Journal articles

AN - SCOPUS:85114991759

VL - 67

JO - Technology in Society

JF - Technology in Society

SN - 0160-791X

M1 - 101741

ER -