MIT AI Risk Repository
Browse AI risks
57 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.
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03.06.00 · Risk Category
"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."
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05.15.00 · Risk Category
Generative models are known for their substantial energy requirements, necessitating significant amounts of electricity, cooling water, and hardware containing rare metals. The extraction and utilization of these resources frequently occur in unsustainable ways. Consequently, papers highlight the urgency of mitigating environmental costs for instance by adopting renewable energy sources and utilizing energy-efficient hardware in the operation and training of generative AI systems.
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10.09.00 · Risk Category
"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 quantities."
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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 (consumption) to grave (waste)”
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"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 crisis by emitting greenhouse gasses."
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"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."
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The risk of harm to the natural environment posed by the ML system.
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16.06.01 · Risk Sub-Category
Risk area 6: Environmental and Socioeconomic harms
Environmental harms from operating LMs
"LMs (and AI more broadly) can have an environmental impact at different levels, including: (1) direct impacts from the energy used to train or operate the LM, (2) secondary impacts due to emissions from LM-based applications, (3) system-level impacts as LM-based applications influence human behaviour (e.g. increasing environmental awareness or consumption), and (4) resource impacts on precious metals and other materials required to build hardware on which the computations are run e.g. data centres, chips, or devices. Some evidence exists on (1), but (2) and (3) will likely be more significant
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17.06.01 · Risk Sub-Category
Automation, Access and Environmental Harms
Environmental harms from operation LMs
"Large-scale machine learning models, including LMs, have the potential to create significant environmental costs via their energy demands, the associated carbon emissions for training and operating the models, and the demand for fresh water to cool the data centres where computations are run (Mytton, 2021; Patterson et al., 2021)."
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"Creating negative environmental impacts though model development and deployment"
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31.06.00 · Risk Category
"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 largely flies under the radar of public AI discourse. Training and running generative AI tools requires companies to use extreme amounts of energy and physical resources. Training one natural language processing model with normal tuning and experiments emits, on average, the same amount of carbon that seven people do over an entire year.121'
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32.04.00 · Risk Category
Environmental harm, Sustainability
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39.02.00 · Risk Category
Some learning algorithms, including deep learning, utilize iterative learning processes [23]. This approach results in high energy consumption.
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41.06.00 · Risk Category
"AI is already helping to combat the impact of climate change with smart technology and sensors reducing emissions. However, it is also a key component in the development of nanobots, which could have dangerous environmental impacts by invisibly modifying substances at nanoscale."
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41.06.01 · Risk Sub-Category
Accelerated development of nanotechnology produces uncontrolled production of toxic nanoparticles
"AI is a key component for the development of nanobots, which could have dangerous environmental implications by invisibly modifying substances at nanoscale. For example, nanobots could start chemical reactions that would create invisible nanoparticles that are toxic and potentially lethal."
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44.01.00 · Risk Category
"Many intentional harms, including confinement, husbandry procedures like tail-docking, and slaughter, are legal or socially accepted, while others such as wildlife trafficking and violence against companion animals are generally socially condemned and often illegal. AI can be designed or adopted by humans who harm animals to pursue their goals more effectively. We therefore distinguish AI-facilitated intentional harms that are currently socially accepted and generally legal, from uses and abuses of AI that cause harms that are not socially accepted and are often illegal."
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44.01.01 · Risk Sub-Category
Intentional: socially condemned/illegal
AI intentionally designed and used to harm animals in ways that contradict social values or are illegal
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44.01.02 · Risk Sub-Category
Intentional: socially condemned/illegal
AI designed to benefit animals, humans, or ecosystems is intentionally abused to harm animals in ways that contradict social values or are illegal
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44.02.00 · Risk Category
"AI designed to impact animals in harmful ways that reflect and amplify existing social values or are legal"
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44.03.00 · Risk Category
"AI designed to benefit animals, humans, or ecosystems has unintended harmful impact on animals"
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44.03.01 · Risk Sub-Category
AI is designed in a way that shows ignorant, reckless, or prejudiced lack of consideration for its impact on animals
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44.03.02 · Risk Sub-Category
AI harms animals due to mistake or misadventure in the way the AI operates in practice
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44.04.00 · Risk Category
"AI impacts human or ecological systems in ways that ultimately harm animals"
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"AI proliferation causes harm to the environment through energy use and e-waste thereby destroying animal habitat"
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"Replacement by AI of human observation and interaction leads to neglect of certain interests"
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"Algorithmic recommender systems reinforce and amplify anthropocentric bias or desire of some people for animal cruelty as entertainment — leading to greater harm to animals through reinforcement of meat eating from factory farms, cruel uses of animals for entertainment, etc"
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44.05.00 · Risk Category
"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)"
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47.04.05 · Risk Sub-Category
Environmental, economical, and societal challenges
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."
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47.04.06 · Risk Sub-Category
Environmental, economical, and societal challenges
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."
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48.05.00 · Risk Category
"Impacts due to high compute resource utilization in training or operating GAI models, and related outcomes that may adversely impact ecosystems."
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"Growing compute use in general- purpose AI development and deployment has rapidly increased energy usage associated with general- purpose AI. This trend might continue, potentially leading to strongly increasing CO2 emissions."
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"Large-scale DL systems can produce signicant carbon emissions as a result of the computational demands of training runs and inference [539]"
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56.03.00 · Risk Category
"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."
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58.09.00 · Risk Category
"Environmental - Damage to the environment directly or indirectly caused by a technology system or set of systems."
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"Biodiversity loss - Over-expansion of technology infrastructure, or inadequate alignment of technology with sustainable practices, leading to deforestation, habitat destruction, and fragmentation and loss of biodiversity."
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"Carbon emissions - Release of carbon dioxide, nitric oxide and other gases, increasing carbon emissions, exacerbating climate change, and negatively impacting local communities."
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"Electronic waste - Electrical or electronic equipment that is waste, including all components, sub-assemblies and consumables that are part of the equipment at the time the equipment becomes waste"
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"Excessive energy consumption - Excessive energy use, leading to energy bottlenecks and shortages for communities, organisations, and businesses."
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"Excessive landfill - Excessive disposal of electrical or electronic equipment leading to ecological/biodiversity damage, and disrupting the livelihoods and eroding the rights of local communities."
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"Excessive water consumption - Excessive use of water to cool data centres and for other purposes, leading to water restrictions or shortages for local communities or businesses."
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"Natural resource depletion - Extraction of minerals, metals, rare earths, and fossil fuels that deplete natural resources and increase carbon emissions."
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"Pollution - Actual or potential pollution to the air, ground, noise, or water caused by a technology system."
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"General- purpose AI is a moderate but rapidly growing contributor to global environmental impacts through energy use and greenhouse gas (GHG) emissions. Current estimates indicate that data centres and data transmission account for an estimated 1% of global energy- related GHG emissions, with AI consuming 10–28% of data centre energy capacity. AI energy demand is expected to grow substantially by 2026, with some estimates projecting a doubling or more, driven primarily by general-purpose AI systems such as language models."
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"The impact of AI on the environment, including risks related to climate change and pollution."
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61.02.23 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Energy-intensive processes
"AI data collection, storage, and model training are energy-intensive, contributing to environmental risks."
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62.11.00 · Risk Category
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62.11.01 · Risk Sub-Category
Negative Externality Domains (Manufacturing of AI Hardware)
Environmental harms from exploitation of natural resources
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62.12.00 · Risk Category
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62.12.01 · Risk Sub-Category
Negative Externality Domains (Running AI Hardware)
Environmental harms from energy usage
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62.39.00 · Risk Category
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Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.