Energy Usage Reports: Environmental awareness as part of algorithmic accountability

By Kadan Lottick, Silvia Susai, Sorelle A. Friedler, Jonathan P. Wilson. 2019

91 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Electrical and Electronic Engineering, Safety, Robustness & Interpretability

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Citations in the corpus

Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “While the open source community has started building tools to enable automatic measurement of AI training’s environmental footprint [39], [40], [98], [99] and the ML research community requiring a broader impact statement for the submitted research manuscript, more can be done in order to incorporat”
Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools (2021, background): “Energy Usage (Lottick et al., 2019) was developed by researchers in the United States with the goal of improving accountability in machine learning research.”
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models (2021, background): “[16] generates static energy usage reports at the end of an experiment, and includes a set of fxed, location based counterfactuals.”
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (2021, background): “Since then, works such as [57, 75] have released online tools to help researchers benchmark their energy usage.”