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

Artificial intelligence (AI) is rapidly becoming ubiquitous, so much so it has been argued that "AI … is becoming an infrastructure that many services of today and tomorrow will depend upon."25 Current progress in the field of AI is spearheaded by machine learning (ML) techniques such as deep learning, which has rendered many tasks previously thought to be out of reach of AI more or less solved. The past decades have seen an exponential rise in the amount of compute used by ML systems,29 which has led to a subsequent rise in energy consumption and carbon emissions.17,23,37 Beyond carbon emissions, increased production and use of the hardware infrastructure needed for ML is potentially exacerbating broader environmental impacts.15 While on the one hand ML systems can be used for making progress toward the sustainable development goals (SDGs),27,34 on the other hand the factors mentioned here limit the sustainability of ML from an environmental perspective.