The Cost of Training a Machine: Lighting the Way for a Climate-Aware Policy Framework that Addresses Artificial Intelligence's Carbon Footprint Problem

29 Pages Posted: 27 Mar 2022 Last revised: 23 May 2022

Date Written: December 31, 2021

Abstract

While artificial intelligence (AI) has been a subject of great debate in spaces such as due process, discrimination, and privacy, an area that is lacking in legal scholarship is the technology’s environmental impact. AI promises to be a silver bullet in the increasingly urgent fight against climate change, yet it comes with a considerable cost to our planet. Current industry trends involve AI models being trained on increasingly larger datasets and training methodologies that prioritize brute-force over efficiency. Thus, as AI models increase in complexity and size, so too does the computing power—and energy—required to train and deploy them. Every stage of AI research and development, from training the model, storing its data, and deploying it in the real world, consumes the Earth’s resources. The interplay between AI and climate change is further complicated by the fact that AI is often lauded as an essential component of the new clean energy economy.

If AI is meant to be a critical component of our new clean energy economy, its ever-increasing energy consumption must be addressed. By analyzing the processes and industry trends that cause AI to be a burden on the environment, this Article argues for mandating transparency around energy usage; empowering the newly formed National Artificial Intelligence Initiative Office to direct sustainable AI design; and pushing data centers to adopt clean energy. The Article then proposes a policy framework that not only minimizes AI’s carbon footprint but maximizes its potential to address key climate change concerns. For if we remain complacent, the very technology that could save our planet could very well be one of its greatest antagonists.

Keywords: artificial intelligence, environmental law, climate change, carbon footprint, policy, energy consumption, national artificial intelligence initiative office, NAIIO, big tech, data centers, united states, department of energy, sustainability, regulation, carbon emissions, machine learning

JEL Classification: K32, O32, Q48, Q56

Suggested Citation

Lin, Patrick K., The Cost of Training a Machine: Lighting the Way for a Climate-Aware Policy Framework that Addresses Artificial Intelligence's Carbon Footprint Problem (December 31, 2021). 34 Fordham Environmental Law Review __ (forthcoming 2022), Available at SSRN: https://ssrn.com/abstract=4066935

Patrick K. Lin (Contact Author)

Brooklyn Law School ( email )

Brooklyn, NY
United States

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