Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
This addresses the pressing issue of freshwater scarcity by uncovering and quantifying AI's environmental impact, which has been largely overlooked, making it a novel contribution rather than incremental.
The paper tackles the problem of AI's hidden water footprint, revealing that training GPT-3 can evaporate 700,000 liters of water and projecting global AI water withdrawal to reach 4.2-6.6 billion cubic meters by 2027, comparable to Denmark's total usage.
The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of AI has largely remained under the radar. For example, training the GPT-3 language model in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand is projected to account for 4.2-6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4-6 Denmark or half of the United Kingdom. This is concerning, as freshwater scarcity has become one of the most pressing challenges. To respond to the global water challenges, AI can, and also must, take social responsibility and lead by example by addressing its own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI, and also discuss the unique spatial-temporal diversities of AI's runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.