Environmental impacts
Increasing use of AI systems, and their growing energy needs, could also have environmental impacts. All of these could become more acute as AI becomes more capable.
ENTITY
1 - Human
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit910
Domain lineage
6. Socioeconomic and Environmental
6.6 > Environmental harm
Mitigation strategy
1. Establish mandatory and standardized full-lifecycle environmental transparency and disclosure requirements for all large-scale AI infrastructure, covering energy consumption, water usage, and electronic waste generation. Concurrently, implement regulatory minimum efficiency standards for data center Power Usage Effectiveness (PUE) and computational efficiency to create a systemic incentive for optimization. 2. Accelerate the transition of AI compute infrastructure to carbon-free energy sources by mandating the strategic co-location of data centers in regions with high renewable energy capacity and low water-stress, and by implementing dynamic load-balancing to align computational workloads with periods of peak grid decarbonization. 3. Prioritize investment in the research, development, and commercial deployment of specialized, energy-efficient AI-optimized hardware (e.g., neuromorphic and optical processors, next-generation GPUs/TPUs) and actively promote the adoption of "Green AI" practices, such as model distillation, efficient architecture design, and the use of smaller, task-specific models to reduce energy consumption per inference.