MIT AI Risk Repository
Browse AI risks
61 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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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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05.11.00 · Risk Category
In response to the multitude of new risks associated with generative AI, papers advocate for legal regulation and governmental oversight. The focus of these discussions centers on the need for international coordination in AI governance, the establishment of binding safety standards for frontier models, and the development of mechanisms to sanction non-compliance. Furthermore, the literature emphasizes the necessity for regulators to gain detailed insights into the research and development processes within AI labs. Moreover, risk management strategies of these labs shall be evaluated. However,
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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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"Liability and negligence are legal gray areas in artificial intelligence. If you leave your children in the care of a robotic nanny, and it malfunctions, are you liable or is the manufacturer [45]? We see here a legal gray area which can be further clarified through legislation at the national and international levels; for example, if by making the manufacturer responsible for defects in operation, this may provide an incentive for manufactures to take safety engineering and machine ethics into consideration, whereas a failure to legislate in this area may result in negligentlydeveloped AI sy
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12.02.00 · Risk Category
"The potential for AI systems to violate laws, regulations, and ethical guidelines (including copyrights). Non-compliance can lead to legal penalties, reputation damage, and loss of trust.While other risks in our taxonomy apply to system developers, users, and broader society, this risk is generally restricted to the former two groups."
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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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19.06.01 · Risk Sub-Category
Unclear definition of responsibilities and accountability for AI judgments and their consequences
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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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19.06.05 · Risk Sub-Category
Capturing future AI development and their threats with appropriate mechanism
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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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"Governance of autonomous intelligence systemaddresses the question of how to control autonomous systems in general. Since nowadays it is very difficult to conceive automated decisions based on AI, the latter is often referred to as a ‘black box’ (Bleicher, 2017). This black box may take unforeseeable actions and cause harm to humanity."
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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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24.01.02 · Risk Sub-Category
Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
"Another difficulty facing AI assistant systems is that it is challenging to develop metrics for evaluating particular aspects of benefits or harms caused by the assistant – especially in a sufficiently expansive sense, which could involve much of society (see Chapter 19). Having these metrics is useful both for assessing the risk of harm from the system and for using the metric as a training signal."
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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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39.10.00 · Risk Category
HLI-based systems such as self-driving drones and vehicles will act autonomously in our world. In these systems, a challenging question is “who is liable when a self-driving system is involved in a crash or failure?”.
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"Despite the promise of streamlined travel, AI also brings concerns about who is liable in case of accidents and which ethical principles autonomous transportation agents should follow when making decisions with a potentially dangerous impact to humans, for example, in case of an accident."
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"Despite the promise of streamlined travel, AI also brings concerns about who is liable in case of accidents and which ethical principles autonomous transportation agents should follow when making decisions with a potentially dangerous impact to humans, for example, in case of an accident."
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"When it causes harm to others the losses caused by the harm will be sustained by the injured victims themselves and not by the manufacturers, operators or users of the system, as appropriate."
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53.04.04 · Risk Sub-Category
Indirect AI contributions to existential risks
Erosion of international law and global governance architectures;
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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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"The complex and rapidly evolving nature of AI makes them inherently difficult to govern effectively, leading to systemic regulatory and oversight failures."
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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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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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"Once a model is deployed, it can be exposed to benchmark data provided by the users [95, 170]. The model may then be further trained by these user inputs containing benchmark data."
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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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"Benchmark saturation refers to benchmarks reaching their evaluation ceiling. The tendency towards benchmark saturation has been demonstrated in various benchmarks [19]. When benchmarks reach or are close to saturation, they stop being effective measures for new models, as more nuanced capability gains might not be detected."
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62.16.16 · Risk Sub-Category
Benchmark Limitations (Insufficient benchmarks for AI safety evaluation)
"Benchmarks dedicated to measuring the performance of AI systems (e.g., on programming or math tasks) are more well-developed than those for assessing safety and harms in AI systems [234]. This gap can lead to AI systems excelling in specific tasks while exhibiting harmful behaviors that go undetected. More safety-related evaluation datasets can help in identifying previously overlooked undesirable model behaviors."
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62.16.17 · Risk Sub-Category
Benchmark Limitations (Underestimating capabilities that are not covered by benchmarks)
"A lack of test coverage by benchmarks on specific abilities of a model can obscure the model’s capabilities from both the developer and the user [160]. This can lead to a false sense of safety and trust due to a lack of understanding of the model’s limitations."
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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."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.