Environmental and socioeconomic harms
At a time of increasing climate urgency, energy consumption and the carbon footprint of AI applications are also matters of ethics and responsibility [68]. As with other energy-intensive technologies like proof-of-work blockchain, the call is to research more environmentally sustainable algorithms to offset the increasing use scale.
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit62
Domain lineage
6. Socioeconomic and Environmental
6.6 > Environmental harm
Mitigation strategy
1. Prioritize the development and deployment of energy-efficient AI algorithms and models (termed "Green AI"), employing techniques such as model compression, quantization, and knowledge distillation to reduce computational requirements during both training and inference. Concurrently, optimize data center operations by utilizing energy-efficient hardware and implementing advanced, water-recycling cooling systems. 2. Mandate the establishment of standardized, transparent metrics for measuring and reporting the full lifecycle environmental footprint of AI systems, including energy consumption, water usage, and both operational and embodied carbon emissions, to enhance accountability and inform policy. 3. Implement strategic infrastructure planning by locating new AI computing facilities in regions with a low-carbon electricity mix and minimal water stress, thereby directly leveraging grid decarbonization to mitigate associated greenhouse gas emissions.