SustainAI tracks water use and fairness in AI data centers
Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI
Computers and SocietyArtificial IntelligenceDistributed, Parallel, and Cluster ComputingHuman-Computer Interaction
Summary
AI systems use a lot of water, especially to cool data centers, but this water use often goes unnoticed. The authors created SustainAI, a system that measures water use in real time and adjusts AI tasks to reduce water consumption based on local water scarcity. Their tests showed water use varies widely by location, and much water is wasted on incorrect AI answers. SustainAI also looks beyond technology to encourage fairness and care for the environment.
What this means in practice
- •For data center operators: Monitor and manage AI-related water use by linking real-time water data with task routing to reduce environmental impact.
- •For health ai development teams: Improve efficiency in health misinformation detection by understanding the trade-off between water use and output accuracy across locations.
Authors
Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad
Abstract
As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisible. While energy and carbon impacts are widely recognized, the substantial freshwater demands of data center cooling and electricity generation receive little attention. To address this gap, we introduce SustainAI, a water-aware, closed-loop framework incorporating environmental accountability into AI deployment. SustainAI integrates real-time water metering, a hallucination-aware penalty model, and a water-aware routing algorithm that accounts for regional water stress. Evaluated via Small Language Models (SLMs) extracting health misinformation, results reveal an 11-fold variation in water footprint across geographically distributed data centers (0.0477 mL to 0.5360 mL per inference). Across 1,335 inference runs, the system consumed approximately 399 mL of water but produced only 240 correct outputs, demonstrating that substantial resources are spent on inaccurate responses. Crucially, SustainAI extends beyond technical optimization through a Care by Design lens, framing AI sustainability around relational ethics, regional equity, and ecological stewardship. By combining water monitoring, adaptive accountability, and Care by Design principles, SustainAI provides a practical foundation for integrating ethical care and environmental responsibility into AI infrastructure design and lifecycle management.