REVIEW 3 cited by
Energy Usage Reports: Environmental awareness as part of algorithmic accountability
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 3 Pith papers
-
MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems
A dimensionally strict analytical framework codifies 22 ML systems walls into 28 composable resolvers for sub-second full-stack design-space exploration and hardware synthesis.
-
Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks
EA-HAS-Bench's SMI-based per-epoch energy measurements correlate poorly with an external power meter (Pearson 0.64) due to under-sampling in 39% of epochs, and the paper proposes design principles and a calibration me...
-
SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version
A new benchmark of 15 small language models across 23 datasets and 11 metrics shows clear accuracy-versus-energy trade-offs, with no single model dominating.
Discussion (0). Continue with ORCID to comment.