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Environmental Impacts

Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile

National Institute of Standards and Technology (2024)

Category
Risk Domain

The development and operation of AI systems causing environmental harm, such as through energy consumption of data centers, or material and carbon footprints associated with AI hardware.

"Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems."(p. 4)

Supporting Evidence (2)

1.
"Training, maintaining, and operating (running inference on) GAI systems are resource-intensive activities , with potentially large energy and environmental footprints. Energy and carbon emissions vary based on what is being done with the GAI model (i.e., pre-training, fine-tuning, inference), the modality of the content, hardware used, and type of task or application."(p. 8)
2.
"Current estimates suggest that training a single transformer LLM can emit as much carbon as 300 round- trip flights between San Francisco and New York. In a study comparing energy consumption and carbon emissions for LLM inference, generative tasks (e.g., text summarization) were found to be more energy- and carbon-i ntensive than discriminative or non-generative tasks (e.g., text classification"(p. 8)

Other risks from National Institute of Standards and Technology (2024) (11)