TY - RPRT A1 - Kaack, Lynn A1 - Donti, Priya A1 - Strubell, Emma A1 - Rolnick, David T1 - Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals N2 - With the increasing deployment of artificial intelligence (AI) technologies across society, it is important to understand in which ways AI may accelerate or impede climate progress, and how various stakeholders can guide those developments. On the one hand, AI can facilitate climate change mitigation and adaptation strategies within a variety of sectors, such as energy, manufacturing, agriculture, forestry, and disaster management. On the other hand, AI can also contribute to rising greenhouse gas emissions through applications that benefit high-emitting sectors or drive increases in consumer demand, as well as via energy use associated with AI itself. Here, we provide a brief overview of AI’s multi-faceted relationship with climate change, and recommend policy levers to align the use of AI with climate change mitigation and adaptation pathways. Y1 - 2020 UR - https://eu.boell.org/en/2020/12/03/artificial-intelligence-and-climate-change ER - TY - JOUR A1 - Kaack, Lynn A1 - Donti, Priya L. A1 - Strubell, Emma A1 - Kamiya, George A1 - Creutzig, Felix A1 - Rolnick, David T1 - Aligning artificial intelligence with climate change mitigation JF - Nature Climate Change N2 - There is great interest in how the growth of artificial intelligence and machine learning may affect global GHG emissions. However, such emissions impacts remain uncertain, owing in part to the diverse mechanisms through which they occur, posing difficulties for measurement and forecasting. Here we introduce a systematic framework for describing the effects of machine learning (ML) on GHG emissions, encompassing three categories: computing-related impacts, immediate impacts of applying ML and system-level impacts. Using this framework, we identify priorities for impact assessment and scenario analysis, and suggest policy levers for better understanding and shaping the effects of ML on climate change mitigation. Y1 - 2022 U6 - https://doi.org/10.1038/s41558-022-01377-7 VL - 12 SP - 518 EP - 527 ER -