Journal
TECHNOLOGY IN SOCIETY
Volume 67, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.techsoc.2021.101741
Keywords
Artificial intelligence; Climate change; Sustainability; Systemic risks; Anthropocene; Resilience; Social-ecological systems; Automation; Digitalization
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Funding
- Beijer Institute of Ecological Economics (Royal Swedish Academy of Sciences)
- Princeton Institute for International and Regional Studies (Princeton University)
- Stockholm Resilience Centre (Stockholm University)
- Zennstrom Philanthropies
- Vienna Science and Technology Fund [VRG16-005]
- Microsoft
- College of Engineering, Penn State University
- U.S. National Science Foundation [1444755, 1934933, 1927167]
- SMARTer Greener Cities project through the Nordforsk Sustainable Urban Development and Smart Cities grant program
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This article discusses the progress and potential systemic risks of artificial intelligence technologies in sectors such as agriculture, forestry, and marine resources, including algorithmic bias, unequal access and benefits, cascading failures, and trade-offs between efficiency and resilience.
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.
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