MIT AI Risk Repository
Browse AI risks
430 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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14.08.00 · Risk Category
"The technological maturity level describes how mature and error-free a certain technology is in a certain application context. If new technologies with a lower level of maturity are used in the development of the AI system, they may contain risks that are still unknown or difficult to assess.Mature technologies, on the other hand, usually have a greater variety of empirical data available, which means that risks can be identified and assessed more easily. However, with mature technologies, there is a risk that risk awareness decreases over time"
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22.02.00 · Risk Category
"The immense potential of AIs has created competitive pressures among global players contending for power and influence. This “AI race” is driven by nations and corporations who feel they must rapidly build and deploy AIs to secure their positions and survive."
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"The development of AIs for military applications is swiftly paving the way for a new era in military technology, with potential consequences rivaling those of gunpowder and nuclear arms in what has been described as the “third revolution in warfare.”
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"Although competition between companies can be beneficial, creating more useful products for consumers, there are also pitfalls. First, the benefits of economic activity may be unevenly distributed, incentivizing those who benefit most from it to disregard the harms to others. Second, under intense market competition, businesses tend to focus much more on short-term gains than on long-term outcomes. With this mindset, companies often pursue something that can make a lot of profit in the short term, even if it poses a societal risk in the long term."
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"Opacity is not solely due to the technological complexity that limits developers’ and users’ understanding of how generative models function on a technical level. It is further exacerbated by the practices of organizations and companies that are advancing the field. Many are private companies that choose to withhold from the public many of the precise characteristics of their most advanced models."
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55.02.00 · Risk Category
"Cooperation and conflict: we’re seeing more focus and investment on the kinds of AI capabilities that make conflict more likely and severe, rather than those likely to improve cooperation. So, on our current trajectory, AI seems more likely to have negative long-term impacts in this area."
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"The international and national security threats, including cyber warfare, arms races, and geopolitical instability."
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61.02.26 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Geopolitical competition for superiority
"Strategic competition between nations over AI capabilities could heighten global tensions and destabilize international relations."
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"In competitive situations, developers of general-purpose AI systems might cut corners on the safety evaluation of their GPAI model and instead spend more time and effort on the capabilities of those systems [183, 69]. This is especially dangerous if the capabilities of such AI systems are correlated with the risk they pose [162]."
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68.06.00 · Risk Category
"As AI is increasingly seen as a powerful technology, countries are racing to develop it ahead of their geopolitical rivals, a competition that could lead to geopolitical tensions [138], [139]... The emphasis of this risk is on harms that result from second-order effects, where geopolitical instabilities result from the race to develop AI, rather than on the direct consequences of the deployment or use of AI itself."
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01.01.00 · Risk Category
Societal-scale harm can arise from AI built by a diffuse collection of creators, where no one is uniquely accountable for the technology's creation or use, as in a classic "tragedy of the commons".
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19.01.05 · Risk Sub-Category
Technological, Data and Analytical AI Risks
Lack of AI experts with comprehensive AI knowledge
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19.06.00 · Risk Category
"Legal and regulatory risks comprise in particular the unclear definition of responsibilities and accountability in case of AI failures and autonomous decisions with negative impacts (Reed, 2018; Scherer, 2016). Another great risk in this context refers to overlooking the scope of AI governance and missing out on important governance aspects, resulting in negative consequences (Gasser & Almeida, 2017; Thierer et al., 2017)."
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19.06.03 · Risk Sub-Category
Great scope and ubiquity of AI make appropriate governance difficult, coverage of governance scope almost impossibl
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"The challenge of responsibility and accountability is an important concept for the process of governance and regulation. It addresses the question of who is to be held legally responsible for the actions and decisions of AI algorithms. Although humans operate AI systems, questions of legal responsibility and liability arise. Due to the self-learning ability of AI algorithms, the operators or developers cannot predict all actions and results. Therefore, a careful assessment of the actors and a regulation for transparent and explainable AI systems is necessary (Helbing et al., 2017; Wachter et
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accidents can cascade into catastrophes, can be caused by sudden unpredictable developments and it can take years to find severe flaws and risks (not a quote)
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"Efforts to deploy advanced assistant technology in society, in a way that is broadly beneficial, can be viewed as a wicked problem (Rittel and Webber, 1973). Wicked problems are defined by the property that they do not admit solutions that can be foreseen in advance, rather they must be solved iteratively using feedback from data gathered as solutions are invented and deployed. With the deployment of any powerful general-purpose technology, the already intricate web of sociotechnical relationships in modern culture are likely to be disrupted, with unpredictable externalities on the convention
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33.03.00 · Risk Category
"Given that generative AI, including ChatGPT, is still evolving, relevant regulations and policies are far from mature. With generative AI creating different forms of content, the copyright of these contents becomes a significant yet complicated issue. Table 3 presents the challenges associated with regulations and policies, which are copyright and governance issues."
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"Generative AI can create new risks as well as unintended consequences. Different entities such as corporations (Mäntymäki et al., 2022), universities, and governments (Taeihagh, 2021) are facing the challenge of creating and deploying AI governance. To ensure that generative AI functions in a way that benefits society, appropriate governance is crucial. However, AI governance is challenging to implement. First, machine learning systems have opaque algorithms and unpredictable outcomes, which can impede human controllability over AI behavior and create difficulties in assigning liability and a
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55.01.02 · Risk Sub-Category
Risks from accelerating scientific progress
Faster scientific progress makes it harder for governance to keep pace with development
"Exacerbating these problems is that faster scientific progress would make it even harder for governance to keep pace with the deployment of new technologies. When these technologies are especially powerful or dangerous, such as those discussed above, insufficient governance can magnify their harms.8 This is known as the pacing problem, and it is an issue that technology governance already faces [47], for a variety of reasons"
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61.02.12 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Challenges in perceiving, measuring, and recognizing harm
"Harm from AI often manifests subtly or over the long term, making it difficult to identify, measure, and address effectively."
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"Harms could result from a combination of regulatory, management, and operational failures."
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61.02.14 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Complex attribution and responsibility
"When multiple actors are involved in AI development and deployment, it becomes difficult to assign responsibility for harm, complicating accountability."
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61.02.40 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Rapid development outpacing regulation
"The fast pace of AI development may outstrip regulatory and legal frameworks."
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61.02.41 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Resistance to international law
"AI models and systems may prove difficult to regulate or control under international law."
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61.02.47 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Unpredictability of AI development trajectory
"The unpredictable trajectory of AI development complicates governance and risk management."
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"Determining who is responsible for an AI model is challenging without good documentation and governance processes."
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"Insufficient documentation of the system that uses the model and the model’s purpose within the system in which it is used."
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"Determining responsibility when EAI causes harm requires new accountability and liability frameworks that address the complexities of highly autonomous physical systems. Human users may disagree with decisions taken by expert EAI systems, raising significant questions of delegation and responsibility [108]. Lack of EAI accountability could lead to confusion for users and breakdowns in traditional justice systems [109]."
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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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"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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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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44.03.00 · Risk Category
"AI designed to benefit animals, humans, or ecosystems has unintended harmful impact on animals"
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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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"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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"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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"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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"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."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.