MIT AI Risk Repository
Browse AI risks
594 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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"The use of large amounts of copyrighted data for training general- purpose AI models poses a challenge to traditional intellectual property laws, and to systems of consent, compensation, and control over data. The use of copyrighted data at scale by organisations developing general- purpose AI is likely to alter incentives around creative expression."
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56.07.00 · Risk Category
"There are also issues around intellectual property rights for content in training datasets"
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"The use of vast amounts of data for training general- purpose AI models has caused concerns related to data rights and intellectual property. Data collection and content generation can implicate a variety of data rights laws, which vary across jurisdictions and may be under active litigation. Given the legal uncertainty around data collection practices, AI companies are sharing less information about the data they use. This opacity makes third- party AI safety research harder."
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64.02.02 · Risk Sub-Category
Misuse tactics that exploit GenAI capabilities (Realistic depictions of non-humans)
Intellectual Property (IP) Infringement
"Use a person's IP without their permission"
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01.04.00 · Risk Category
As a side effect of a primary goal like profit or influence, AI creators can willfully allow it to cause widespread societal harms like pollution, resource depletion, mental illness, misinformation, or injustice.
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08.03.00 · Risk Category
"The risks associated with the race to develop the first AGI, including the development of poor quality and unsafe AGI, and heightened political and control issues."
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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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45.01.13 · Risk Sub-Category
Risks from AI systems (Risks of supply chain security)
"The AI industry relies on a highly globalized supply chain. However, certain countries may use unilateral coercive measures, such as technology barriers and export restrictions, to create development obstacles and maliciously disrupt the global AI supply chain. This can lead to significant risks of supply disruptions for chips, software, and tools."
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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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"Increased competition - The inappropriate or unethical use of technology to gain market share."
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61.02.17 · Risk Sub-Category
Sources of systemic risks from general-purpose AI
Dangerous development races
"Competitive pressures could lead to the neglect of safety measures in AI development."
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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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62.29.03a · Additional evidence
Competitive pressures in GPAI product release
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08.05.00 · Risk Category
"The capabilities of current risk management and legal processes in the context of the development of an AGI."
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"When considering legal frameworks, we note that at present no such framework has been identified in literature which would apply blame and responsibility to an autonomous agent for its actions. (Though we do suggest that the recent establishment of laws regarding autonomous vehicles may provide some early frameworks that can be evaluated for efficacy and gaps in future research.) Frequently the literature refers to existing liability and negligence laws which might apply to the manufacturer or operator of a device."
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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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20.01.00 · Risk Category
"This area strongly focuses on the control of AI by means of mechanisms like laws, standards or norms that are already established for different technological applications. Here, there are some challenges special to AI that need to be addressed in the near future, including the governance of autonomous intelligence systems, responsibility and accountability for algorithms as well as privacy and data security."
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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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31.08.00 · Risk Category
"Like manufactured items like soda bottles, mechanized lawnmowers, pharmaceuticals, or cosmetic products, generative AI models can be viewed like a new form of digital products developed by tech companies and deployed widely with the potential to cause harm at scale....Products liability evolved because there was a need to analyze and redress the harms caused by new, mass-produced technological products. The situation facing society as generative AI impacts more people in more ways will be similar to the technological changes that occurred during the twentieth century, with the rise of industr
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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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"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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62.16.02 · Risk Sub-Category
General Evaluations (Limited coverage of capabilities evaluations)
"GPAI model developers might run capabilities evaluations to determine whether it has dangerous or dual-use capabilities, and then decide whether it is safe to deploy. Such capabilities evaluations can fail to demonstrate all the capabilities of a model. For example, evaluations may miss certain capabilities that are difficult to assess, prohibitively costly to verify, or obscured by the model’s tendency to refuse responses due to safety training, even if it possesses some of these capabilities."
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62.16.06 · Risk Sub-Category
General Evaluations (Biased evaluations of encoded human values)
"Encoded human values in AI models that are easier to evaluate might be preferred for inclusion in evaluations over those that are more difficult to measure [13]. This might come at the expense of more desirable but harder-to-quantify values. This bias can lead to an imbalance, where easier-to-measure values dominate the evaluation process, while other important values are underrepresented."
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62.16.08 · Risk Sub-Category
Benchmarking (Benchmark leakage or data contamination)
"Benchmark leakage [235, 224, 221, 161] can happen when an AI model is trained or fine-tuned with evaluation-related data. This can lead to an unreliable model evaluation, especially if the data contains question-answer pairs from bench- marks."
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"This type of contamination [170] occurs when the raw and unlabeled data of a benchmark is used as part of the training set. Such data may not be properly formatted and may contain noise, especially if the contamination happens before the data is pre-processed into the benchmark. If this contamination occurs, it could cast doubt on the few-shot and zero-shot performance of the model on that benchmark."
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"Models that have been trained on data encoded in multiple languages, such as LLMs trained on web-crawled data, may contain contamination that is obscured by translation [226]. The most basic form of this is when a benchmark is trans- lated to another language and then fed to the model as training data. The fact that the benchmark is translated before becoming training data can obscure the contamination from detection methods, giving false assurance that the model has generalized on the capabilities that the benchmark tests for."
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"Guideline contamination refers to scenarios where instructions for the collec- tion, annotation, or use of the dataset are exposed to the model [170]. These instructions may contain explicit data-label pairs that can improve the model’s capabilities for the task."
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"Annotation contamination refers to scenarios where the model is exposed to the benchmark labels during training [170]. This type of contamination can make the model learn the acceptable distribution of outputs. Combining this with raw data contamination of the test split, any evaluation made with the benchmark is invalidated because the entire test split is essentially leaked to the model."
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62.16.14 · Risk Sub-Category
Benchmark Inaccuracy (Benchmarks may not accurately evaluate capabilities)
"Benchmarks of AI systems can both underestimate and overestimate the capa- bilities of those AI systems. Underestimates can happen if an evaluation is not comprehensive enough, if the benchmark is saturated by existing models, or if the capabilities in question depend on a complicated setup, such as realistic computer programming tasks. Overestimates of capabilities can occur if an AI system is trained or fine-tuned on the contents of the benchmark, leading to overfitting."
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62.17.00 · Risk Category
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62.17.01 · Risk Sub-Category
Conflicts of interest in auditor selection
"Conflicts of interest can arise if there is no independence in the auditor selection process or if the auditors are closely associated with the developer [123, 157]. In such cases, the conflict of interest can appear even if third-party evaluators are involved. In the case of external auditing, the potential candidates might be selected from a narrow group of auditors, or have conflicting financial incentives for whether to report model shortcomings publicly."
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"Auditors may not be able to address all of the specific safety, performance, or validation needs. Reports of passing audits may be more inclusive than can be justified due to a lack of knowledge of specific risks and how they can be tested, or a lack of capacity to perform sufficiently rigorous testing."
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"Auditors may not publicly disclose risks they find, may be required to not pub- licize shortcomings, or may not receive sufficient cooperation from the relevant internal parties."
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"Without accurate documentation on how a model's data was collected, curated, and used to train a model, it might be harder to satisfactorily explain the behavior of the model with respect to the data."
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"Data provenance refers to tracing history of data, which includes its ownership, origin, and transformations. Without standardized and established methods for verifying where the data came from, there are no guarantees that the data is the same as the original source and has the correct usage terms."
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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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"Testing is unrepresentative when the test inputs are mismatched with the inputs that are expected during deployment."
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"Since foundation models can be used for many purposes, a model’s intended use is important for defining the relevant risks of that model. As the use changes, the relevant risks might correspondingly change."
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"Lack of data transparency is due to insufficient documentation of training or tuning dataset details. "
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"A metric selected to measure or track a risk is incorrectly selected, incompletely measuring the risk, or measuring the wrong risk for the given context."
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"AI model risks are socio-technical, so their testing needs input from a broad set of disciplines and diverse testing practices."
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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."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.