  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.
