Cyber & IT Supervisory Forum - Additional Resources

Measure 2.12 Environmental impact and sustainability of AI model training and management activities – as identified in the MAP function – are assessed and documented. About Large-scale, high-performance computational resources used by AI systems for training and operation can contribute to environmental impacts. Direct negative impacts to the environment from these processes are related to energy consumption, water consumption, and greenhouse gas (GHG) emissions. The OECD has identified metrics for each type of negative direct impact. Indirect negative impacts to the environment reflect the complexity of interactions between human behavior, socio-economic systems, and the environment and can include induced consumption and “rebound effects”, where efficiency gains are offset by accelerated resource consumption. Other AI related environmental impacts can arise from the production of computational equipment and networks (e.g., mining and extraction of raw materials), transporting hardware, and electronic waste recycling or disposal. Suggested Actions Include environmental impact indicators in AI system design and development plans, including reducing consumption and improving efficiencies. Identify and implement key indicators of AI system energy and water consumption and efficiency, and/or GHG emissions. Establish measurable baselines for sustainable AI system operation in accordance with organizational policies, regulatory compliance, legal frameworks, and environmental protection and sustainability norms. Assess tradeoffs between AI system performance and sustainable operations in accordance with organizational principles and policies, regulatory compliance, legal frameworks, and environmental protection and sustainability norms. 153