Thirsty AI Challenge for a Sustainable Future
Bibliographic record
Abstract
The carbon footprint of AI has been increasingly under scrutiny by the public. However, the no less significant water (withdrawal and consumption) footprint of AI has mostly been overlooked. For instance, training the GPT-3 language model in Microsoft’s cutting-edge U.S. data centers amounts to directly evaporating 700,000 liters of clean freshwater, but that information has been hidden from view. More importantly, the AI demand at the world level is predicted to require a withdrawal of 4.2–6.6 bm 3 in 2027, which is higher than the whole annual water withdrawal for four to six Denmark’s or half of the UK. This is alarming given that there is currently a global freshwater crisis. In a response to the world’s water challenges, AI can and also should assume social responsibility and set an example by mitigating its own water footprint. In this paper, we propose a principled approach to measure the water footprint of AI, and we raise the question of spatial-temporal diversities to AI’s runtime water efficiency. Lastly, we emphasize that just like carbon footprint, water footprint must be addressed holistically to realize truly sustainable AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".