MIT AI Risk Repository · domain 6: Socioeconomic & Environmental
6.6 Environmental harm
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.
- 57
- 12
- 1
- 1
| Label | Value |
|---|---|
| AI | 24 |
| Human | 15 |
| Other | 13 |
| Not coded | 4 |
| Label | Value |
|---|---|
| Unintentional | 35 |
| Other | 12 |
| Intentional | 5 |
| Not coded | 4 |
| Label | Value |
|---|---|
| Other | 24 |
| Post-deployment | 20 |
| Pre-deployment | 8 |
| Not coded | 4 |
| Label | Value |
|---|---|
| 2024 | 1 |
| Label | Value |
|---|---|
| Risk Category | 19 |
| Risk Sub-Category | 38 |
Risk entries
Browse and export all- Foregone benefits
"AI is disused (not developed or deployed) in directions that would benefit animals (and instead developments that harm or do no benefit to animals are invested in)"
- 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...
- Exacerbating Climate Change
"the growing field of generative AI, which brings with it direct and severe impacts on our climate: generative AI comes with a high carbon footprint and similarly high resource price tag, which largel...
- Negative Externality Domains (Manufacturing of AI Hardware)
- Environmental harms from exploitation of natural resources
- Negative Externality Domains (Running AI Hardware)
- Environmental harms from energy usage
- Impacts of AI (Environment)
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- High energy consumption of large models
"Training and deploying large models require substantial energy expenditure. The trend toward developing larger models exacerbates this issue. This can lead to excessive energy usage and have a negati...
- 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."
- Environmental cost (energy consumption)
"Training large AI models requires a substantial amount of computing power to handle vast datasets, which translates into high energy consumption."
- Environmental cost (water consumption)
"Data centers use water for cooling to prevent servers from overheating. The water consumption associated with AI training and inference processes can be substantial, impacting local water resources."
- 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 utilizat...
- Impact on the environment
"AI, and large generative models in particular, might produce increased carbon emissions and increase water usage for their training and operation."
- Environmental cost
"Large-scale DL systems can produce signicant carbon emissions as a result of the computational demands of training runs and inference [539]"
- Property damage
"Action(s) that lead directly or indirectly to the damage or destruction of tangible property eg. buildings, possessions, vehicles, robots"
- Pollution
"Actual or potential pollution to the air, ground, noise, or water caused by a technology system"
- Excessive energy consumption
"Excessive energy use resulting in energy bottlenecks and shortages for communities, organisations and businesses"
- Environmental Impacts
"Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems."
- Environmental Impacts
"The production process of these devices requires raw materials such as nickel, cobalt, and lithium in such high quantities that the Earth may soon no longer be able to sustain them in sufficient quan...
- Energy Consumption
Some learning algorithms, including deep learning, utilize iterative learning processes [23]. This approach results in high energy consumption.
- Environmental harms
depletion or contamination of natural resources, and damage to built environments... that may occur throughout the lifecycle of digital technologies [170, 237] from “crale (mining) to usage (consumpti...
- Environmental Costs
"The computing power used in training, testing, and deploying generative AI systems, especially large scale systems, uses substantial energy resources and thereby contributes to the global climate cri...
- Ecosystem and Environment
"Impacts at a high-level, from the AI ecosystem to the Earth itself, are necessarily broad but can be broken down into components for evaluation."
- Environmental impacts
Environmental harm, Sustainability