MIT AI Risk Repository
Browse AI risks
2,500 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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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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"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 negative environmental impact."
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"AI, and large generative models in particular, might produce increased carbon emissions and increase water usage for their training and operation."
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"Action(s) that lead directly or indirectly to the damage or destruction of tangible property eg. buildings, possessions, vehicles, robots"
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"Actual or potential pollution to the air, ground, noise, or water caused by a technology system"
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"Excessive energy use resulting in energy bottlenecks and shortages for communities, organisations and businesses"
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68.05.00 · Risk Category
"AI models are often trained using large amounts of computation. This process is very energy intensive, potentially leading to significant greenhouse emissions depending on the energy sources [132]. Experts believe drastically increasing carbon emissions could accelerate climate change, which may constitute a catastrophic risk [133]."
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"Short-term or long-term Negative effects on the natural environment"
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15.01.00 · Risk Category
"First-order risks can be generally broken down into risks arising from intended and unintended use, system design and implementation choices, and properties of the chosen dataset and learning components."
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"This is the risk posed by the intended application or use case. It is intuitive that some use cases will be inherently "riskier" than others (e.g., an autonomous weapons system vs. a customer service chatbot)."
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"This is the risk posed by the choice of data used for training and validation."
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"This is the risk of system failure due to code implementation choices or errors."
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"Limitations of Human Feedback. During the training of LLMs, inconsistencies can arise from human dataannotators (e.g., the varied cultural backgrounds of these annotators can introduce implicit biases (Peng et al.,2022)) (OpenAI, 2023a). Moreover, they might even introduce biases deliberately, leading to untruthful preferencedata (Casper et al., 2023b). For complex tasks that are hard for humans to evaluate (e.g., the value ofgame state), these challenges become even more salient (Irving et al., 2018)."
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40.05.00 · Risk Category
"While it is most likely that any advanced intelligent software will be directly designed or evolved, it is also possible that we will obtain it as a complete package from some unknown source. For example, an AI could be extracted from a signal obtained in SETI (Search for Extraterrestrial Intelligence) research, which is not guaranteed to be human friendly (Carrigan Jr 2004, Turchin March 15, 2013)."
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40.06.00 · Risk Category
"While highly rare, it is known, that occasionally individual bits may be flipped in different hardware devices due to manufacturing defects or cosmic rays hitting just the right spot (Simonite March 7, 2008). This is similar to mutations observed in living organisms and may result in a modification of an intelligent system."
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40.07.00 · Risk Category
"One of the most likely approaches to creating superintelligent AI is by growing it from a seed (baby) AI via recursive self-improvement (RSI) (Nijholt 2011). One danger in such a scenario is that the system can evolve to become self-aware, free-willed, independent or emotional, and obtain a number of other emergent properties, which may make it less likely to abide by any built-in rules or regulations and to instead pursue its own goals possibly to the detriment of humanity."
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40.08.00 · Risk Category
"Previous research has shown that utility maximizing agents are likely to fall victims to the same indulgences we frequently observe in people, such as addictions, pleasure drives (Majot and Yampolskiy 2014), self-delusions and wireheading (Yampolskiy 2014). In general, what we call mental illness in people, particularly sociopathy as demonstrated by lack of concern for others, is also likely to show up in artificial minds."
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43.01.00 · Risk Category
"A comprehensive assessment of LLM safety is fundamental to the responsible development and deployment of these technologies, especially in sensitive fields like healthcare, legal systems, and finance, where safety and trust are of the utmost importance."
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43.02.00 · Risk Category
"This category encompasses the evaluation of potential catastrophic consequences that might arise from the use of LLMs. "
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49.02.00 · Risk Category
None provided.
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59.07.00 · Risk Category
"The choice of a trustworthy data source is a first prerequisite in order to fulfill data quality requirements. This is especially the case if third-party data sources are used to develop the AI system."
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59.08.00 · Risk Category
"The correct understanding of the used data for developing an AI system is a prerequisite to avoid data shortcomings and hinders the development of an AI system which is best suiting for the intended functionality."
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59.15.00 · Risk Category
"In data-driven AI development, the annotated data set is commonly split into training, validation, and test sets, whereby it is essential that the latter is not used for development but only for evaluation. Using the test set for training manipulates the testing strategy, which is the basis of the system’s quality assurance."
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59.21.00 · Risk Category
"AI systems should be able not only to return output for a given instance but also to provide a corresponding level of confidence. If such a method is not implemented or not working correctly, this can have a negative impact on performance and safety."
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62.07.02 · Risk Sub-Category
Direct Harm Domains (system and operational)
Operational harms (financial markets)
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62.15.09 · Risk Sub-Category
Fine-tuning related (Degrading safety training due to benign fine-tuning)
"When downstream providers of AI systems fine-tune AI models to be more suitable for their needs, the resulting AI model can be more likely to produce undesired or harmful outputs (as compared to the non-fine-tuned model), even if the fine-tuning was done with harmless and commonly used data [154]."
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A primary concern is the emergence of human-level or superhuman generative models, commonly referred to as AGI, and their potential existential or catastrophic risks to humanity. Connected to that, AI safety aims at avoiding deceptive or power-seeking machine behavior, model self-replication, or shutdown evasion. Ensuring controllability, human oversight, and the implementation of red teaming measures are deemed to be essential in mitigating these risks, as is the need for increased AI safety research and promoting safety cultures within AI organizations instead of fueling the AI race. Further
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.