Energy Usage Reports: Environmental awareness as part of algorithmic accountability
November 19, 2019 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Kadan Lottick, Silvia Susai, Sorelle A. Friedler, Jonathan P. Wilson
arXiv ID
1911.08354
Category
cs.LG: Machine Learning
Cross-listed
cs.CY,
stat.ML
Citations
73
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also include comparisons with energy mixtures employed in electrical grids around the world. We propose including these automatically-generated Energy Usage Reports as part of standard algorithmic accountability practices, and demonstrate the use of these reports as part of model-choice in a machine learning context.
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