Sustainability
Generative models are known for their substantial energy requirements, necessitating significant amounts of electricity, cooling water, and hardware containing rare metals. The extraction and utilization of these resources frequently occur in unsustainable ways. Consequently, papers highlight the urgency of mitigating environmental costs for instance by adopting renewable energy sources and utilizing energy-efficient hardware in the operation and training of generative AI systems.
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
3 - Other
Risk ID
mit81
Domain lineage
6. Socioeconomic and Environmental
6.6 > Environmental harm
Mitigation strategy
1. Prioritize Hardware and Algorithmic Optimization: Mandate the deployment of specialized, energy-efficient AI accelerators (e.g., ASICs, TPUs) over general-purpose hardware. Concurrently, enforce software and algorithmic optimizations, such as model quantization, weight pruning, and early stopping criteria in training, to substantially reduce the computational and electrical energy demand for both model training and inference. 2. Implement Sustainable Infrastructure and Sourcing: Require the strategic location of new data centers in regions with low-carbon energy grids (carbon-aware computing) and the compulsory adoption of advanced, closed-loop cooling systems (e.g., direct-to-chip or immersion cooling) to minimize freshwater consumption for thermal management. 3. Establish Environmental Transparency and Accountability: Institute standardized procedures and regulatory frameworks that require developers and providers to transparently measure and report the full environmental footprint of AI models and services, including operational and embodied carbon emissions, and Water Usage Effectiveness (WUE), to enable informed, sustainable procurement and governance.