Energy Usage Reports: Environmental awareness as part of algorithmic accountability

TL;DR

This paper introduces a Python tool for computer scientists to measure and report the environmental impact of algorithms, including CO2 emissions and energy benchmarks, promoting accountability and sustainability.

Contribution

It provides an accessible, localized method for calculating algorithmic energy consumption and emissions, integrating global energy mix comparisons and practical reporting for researchers.

Findings

The tool accurately converts energy use to CO2 emissions based on local energy sources.

Energy reports influence model selection in machine learning tasks.

Global energy mix comparisons contextualize environmental impact.

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.