Does the growing prevalent usage of artificial intelligence (AI) benefit or hinder as the world combats climate change? Researchers are constructing a framework to find out.
A team of experts in AI, climate change, and public policy proposes a framework for comprehending the intricate and multifaceted relationship between AI and greenhouse gas emissions in a paper that was published in Nature Climate Change. They also make recommendations for how to better integrate AI with climate change objectives.
AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified. For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy.
David Rolnick, Study Co-Author and Assistant Professor, Computer Science, McGill University
The effects of AI on greenhouse gas emissions are divided into three groups in the paper: 1) Influences from the computing power and hardware used to create, train, and run AI algorithms; 2) immediate effects brought on by applications of AI, such as optimizing energy use in buildings (which reduces emissions) or speeding up fossil fuel exploration (which tends to increase emissions); and 3) system-level effects brought on by how AI applications influence behavior patterns and society at large, such as through advertising systems and self-driving cars.
Climate change should be a key consideration when developing and assessing AI technologies. We find that those impacts that are easiest to measure are not necessarily those with the largest impacts. So, evaluating the effect of AI on the climate holistically is important.
Lynn Kaack, Study Lead Author and Assistant Professor, Computer Science and Public Policy, Hertie School
AI’s Impacts on Greenhouse Gas Emissions—A Matter of Choice
The study authors stated, “Its ultimate effect on the climate is far from predestined, and societal decisions will play a large role in shaping its overall impacts.”
For instance, the paper points out that autonomous vehicle innovations powered by AI can aid in reducing emissions if they are created to improve public transit. Still, they can also raise emissions if employed in personal vehicles and lead to increased driving.
The researchers also point out that a small number of individuals frequently own the majority of the machine learning skills.
Since it could result in the creation or widening of the digital divide or the transfer of power from small, public entities to large, private ones based on who holds the control of the necessary data or intellectual property, this raises potential issues with regard to governance and incorporation of machine learning in the context of climate change.
Prof. Rolnick added, “The choices that we make implicitly as technologists can matter a lot. Ultimately, AI for Good shouldn't just be about adding beneficial applications on top of business as usual, it should be about shaping all the applications of AI to achieve the impact we want to see.”
Kaack, L. H., et al. (2022) Aligning artificial intelligence with climate change mitigation. Nature Climate Change. doi:10.1038/s41558-022-01377-7