HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Aligning artificial intelligence with climate change mitigation

Abstract : Assessing and shaping the effects of artificial intelligence (AI) and machine learning (ML) on climate change mitigation demands a concerted effort across research, policy, and industry. However, there is great uncertainty regarding how ML may affect present and future greenhouse gas (GHG) emissions. This is owed in part to insufficient characterization of the different mechanisms through which such emissions impacts may occur, posing difficulties in measuring and forecasting them. We therefore introduce a systematic framework for describing ML's effects on GHG emissions, comprising three categories: (A) compute-related impacts, (B) immediate impacts of applying ML, and (C) system-level impacts. Using this framework, we assess and prioritize research and data needs for impact assessment and scenario analysis, and identify important policy levers.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03368037
Contributor : Lynn Kaack Connect in order to contact the contributor
Submitted on : Wednesday, October 6, 2021 - 3:23:24 PM
Last modification on : Saturday, October 9, 2021 - 3:54:00 AM
Long-term archiving on: : Friday, January 7, 2022 - 7:13:37 PM

File

Kaack_2021_Aligning.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03368037, version 1

Collections

Citation

Lynn Kaack, Priya Donti, Emma Strubell, George Kamiya, Felix Creutzig, et al.. Aligning artificial intelligence with climate change mitigation. 2021. ⟨hal-03368037⟩

Share

Metrics

Record views

815

Files downloads

1259