Awarded the Inaugural Gradient Prize for excellence in technical and scientific writing.
AI systems are compute-intensive: the AI lifecycle often requires long-running training jobs, hyperparameter searches, inference jobs, and other costly computations. They also require massive amounts of data that might be moved over the wire, and require specialized hardware to operate effectively, especially large-scale AI systems. All of these activities require electricity — which has a carbon cost. There are also carbon emissions in ancillary needs like hardware and datacenter cooling.
Thus, AI systems have a massive carbon footprint. This carbon footprint also has consequences in terms of social justice as we will explore in this article. Here, we use sustainability to talk about not just environmental impact, but also social justice implications and impacts on society. Though an important area, we don’t use the term sustainable AI here to mean applying AI to solve environmental issues. Instead, a critical examination of the impacts of AI on the physical and social environment is the focus of our discussion.