Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models

2023/06/29 by Guido Zuccon, Zuccon, Guido, Harrisen Scells +3 · 3 citations
Environmental Science · Materials Science · Computer Science · #Air Quality Monitoring and Forecasting #Machine Learning in Materials Science #Explainable Artificial Intelligence (XAI)

paper · pdf · doi:10.48550/arxiv.2306.16668

Abstract

As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search. This interest has become particularly relevant as the energy consumption of information retrieval models has risen with new neural models based on large language models, leading to an associated increase of CO2 emissions, albeit relatively low compared to fields such as natural language processing.

Cited by

• Environmental Impact of CI/CD Pipelines
• ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments
• Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing

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