@techreport{CluttonBrockRolnickDontietal., type = {Working Paper}, author = {Clutton-Brock, Peter and Rolnick, David and Donti, Priya L. and Kaack, Lynn}, title = {Climate Change and AI. Recommendations for Government Action}, pages = {94}, abstract = {The report, Climate Change and AI: Recommendations for Government, highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses.}, language = {en} } @article{KaackDontiStrubelletal., author = {Kaack, Lynn and Donti, Priya L. and Strubell, Emma and Kamiya, George and Creutzig, Felix and Rolnick, David}, title = {Aligning artificial intelligence with climate change mitigation}, series = {Nature Climate Change}, volume = {12}, journal = {Nature Climate Change}, doi = {10.1038/s41558-022-01377-7}, pages = {518 -- 527}, abstract = {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.}, language = {en} } @article{KaackRolnickDontietal., author = {Kaack, Lynn and Rolnick, David and Donti, Priya L. and Kochanski, Kelly and Lacoste, Alexandre and Sankaran, Kris and Ross, Andrew S. and Milojevic-Dupont, Nikola and Jaques, Natasha and Waldman-Brown, Anna and Luccioni, Alexandra S. and Maharaj, Tegan and Sherwin, Evan D. and Mukkavilli, Karthik and Kording, Konrad P. and Gomes, Carla P. and Ng, Andrew Y. and Hassabis, Demis and Platt, John C. and Creutzig, Felix and Chayes, Jennifer and Bengio, Yoshua}, title = {Tackling Climate Change with Machine Learning}, series = {ACM Computing Surveys}, volume = {55}, journal = {ACM Computing Surveys}, number = {2}, doi = {10.1145/3485128}, pages = {1 -- 96}, abstract = {Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.}, language = {en} } @techreport{YaakoubiDontiKaacketal., type = {Working Paper}, author = {Yaakoubi, Yassine and Donti, Priya L. and Kaack, Lynn H. and Rolnick, David and Dunietz, Jesse and Malik, Muneeb and Afolabi, Temilola and Kuhne, Paul and Ndzimande, Onezwa and Chinyamakobvu, Mutsa and Gombakomba, Ruvimbo and Chibvongodze, Vongai and Leslie, Timothy and Bj{\"a}rby, Effuah and Letuka, Teboho}, title = {Grand Challenge Initiatives in AI for Climate \& Nature: Landscape Assessment and Recommendations}, publisher = {Climate Change AI (CCAI)}, language = {en} } @article{KaackRolnickReischetal., author = {Kaack, Lynn and Rolnick, David and Reisch, Lucia A. and Joppa, Lucas and Howson, Peter and Gil, Artur and Alevizou, Panayiota and Michaelidou, Nina and Appiah-Campbell, Ruby and Santarius, Tilman and K{\"o}hler, Susanne and Pizzol, Massimo and Schweizer, Pia-Johanna and Srinivasan, Dipti and Kaack, Lynn and Donti, Priya L.}, title = {Digitizing a sustainable future}, series = {One Earth}, volume = {4}, journal = {One Earth}, number = {6}, doi = {10.1016/j.oneear.2021.05.012}, pages = {768 -- 771}, abstract = {Digital technologies have a crucial role in facilitating transitions toward a sustainable future. Yet there remain challenges to overcome and pitfalls to avoid. This Voices asks: how do we leverage the digital transformation to successfully support a sustainability transition?}, language = {en} } @techreport{KaackDontiStrubelletal., type = {Working Paper}, author = {Kaack, Lynn and Donti, Priya and Strubell, Emma and Rolnick, David}, title = {Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals}, abstract = {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.}, language = {en} }