Energy Usage Reports: Environmental awareness as part of algorithmic accountability
This addresses the need for environmental awareness in algorithmic accountability, making carbon footprint analysis accessible to individual computer science researchers, though it is incremental by applying existing industrial-level analyses to a new context.
The paper tackles the problem of measuring and reporting the carbon footprint of algorithms by developing an easy-to-use Python package that converts energy usage to CO2 emissions, contextualized with benchmarks like automobile miles driven, and demonstrates its use in model-choice for machine learning.
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.