The Imperative for Sustainable AI Systems
There are immediate next steps that we can take to improve the environmental posture of the AI systems that we build.
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….
Sustainable AI can be thought of as another dimension along which we can guide the development of AI systems in addition to typical functional and business requirements. As van Wynsberghe elucidates, “Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice.” There are two important considerations that jump out from this that we must take into account: Sustainable AI can allow us to (1) achieve social justice when we utilize this approach, and (2) especially so when these systems operate in an inherently socio-technical context. Indeed, a harmonized approach accounting for both societal and environmental considerations in the design, development, and deployment of AI systems can lead us to gains that support the triple bottom line: profits, people, and planet.
There are immediate next steps that we can take to improve the environmental posture of the AI systems that we build: normalizing the practice of carbon accounting for AI systems, implementing basic instrumentation and telemetry to gather the necessary data, and making carbon impacts a core consideration alongside functional and business requirements.
A holistic approach that takes into account the business and functional needs at the same time boosts the likelihood that measures to be more green in our AI lifecycle will be taken up by both researchers and practitioners.