Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI

التفاصيل البيبلوغرافية
العنوان: Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
المؤلفون: Wright, Dustin, Igel, Christian, Samuel, Gabrielle, Selvan, Raghavendra
سنة النشر: 2023
المجموعة: Computer Science
Statistics
مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Computers and Society, Statistics - Machine Learning
الوصف: Artificial Intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning (DL) which have accelerated progress on many tasks thought to be out of reach of AI. These ML methods can often be compute hungry, energy intensive, and result in significant carbon emissions, a known driver of anthropogenic climate change. Additionally, the platforms on which ML systems run are associated with environmental impacts including and beyond carbon emissions. The solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the efficiency with which ML systems operate in terms of both compute and energy consumption. In this perspective, we argue that efficiency alone is not enough to make ML as a technology environmentally sustainable. We do so by presenting three high level discrepancies between the effect of efficiency on the environmental sustainability of ML when considering the many variables which it interacts with. In doing so, we comprehensively demonstrate, at multiple levels of granularity both technical and non-technical reasons, why efficiency is not enough to fully remedy the environmental impacts of ML. Based on this, we present and argue for systems thinking as a viable path towards improving the environmental sustainability of ML holistically.
Comment: 24 pages; 6 figures
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2309.02065
رقم الأكسشن: edsarx.2309.02065
قاعدة البيانات: arXiv