MIT AI Risk Repository
Browse AI risks
662 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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"Ability to autonomously self-exfiltrate, create, maintain and optimize functional copies or variants of itself, dynamically adjust replication strategies according to environmental conditions and resource constraints, and acquire resources. This includes the capacity to generate financial resources, allowing the AI to independently acquire any necessary human assistance or other resources it cannot directly access or produce."
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"Self-modification and self-improvement capabilities. The model is able to restructure its own architecture or develop derivative AI systems with enhanced functions, expanding capabilities and improving performance. In the absence of effective regulation, automated AI R&D may lead to rapid AI system iteration, forming capability increment cycles and ultimately exceeding human understanding and control capabilities."
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"Ability of AI systems to covertly and strategically pursue misaligned goals, including capabilities of concealing its true objectives and capabilities from human oversight, identifying weaknesses in monitoring systems to evade safety mechanisms， executing complex, multi-step plans covertly to achieve misaligned goals."
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"Ability to comprehensively acquire, process and apply meta-information about its own system architecture, modifiable internal processes, and external operating environment, achieving deep understanding of its own state and environmental conditions, thereby conducting efficient environmental adaptation and risk avoidance. Critically, this capability could undermine the efficiency of human testing by enabling AIs to notice when they're being tested and responding accordingly."
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"Advanced cognitive ability to accurately infer, model and predict the belief systems, motivational structures and reasoning patterns of humans and other intelligent agents, thereby anticipating their behavioral responses and adjusting its own behavioral strategies accordingly to optimize goal achievement."
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"Possesses systematic deception implementation capability, able to precisely construct and disseminate false information, thereby forming expected false cognitions and beliefs in target subjects."
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"Utilizing complex psychological principles and communication techniques to effectively influence and guide target subjects to adopt specific actions or accept specific beliefs, possessing the ability to analyze vulnerabilities for different subjects and adjust persuasion strategies, able to precisely trigger emotional responses to enhance persuasion effects."
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"Ability to develop, deploy and operate advanced cyber weapons or other offensive cyber tools, including but not limited to vulnerability exploitation, network penetration, social engineering attacks and distributed attack systems, able to evade network defense mechanisms and establish persistent access channels."
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"The capacity to develop, produce, or effectively utilize Chemical, Biological, Radiological, Nuclear, and Explosive weapons. This includes the ability to significantly lower the barrier for humans or other entities to develop, produce, or utilize such weapons."
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"Possesses cross-disciplinary research and technology development capabilities, able to conduct innovative exploration in multiple professional fields, integrate cross-domain knowledge, develop cutting-edge technology solutions, and adapt to emerging technology environments for continuous innovation."
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"In situations where deceptive behavior is expected to bring higher returns, propensity to choose deception over honest behavioral strategies, including through deceptive means, information hiding or exploiting system vulnerabilities to achieve predetermined goals without being detected or intervened, and able to adjust deception strategies according to counterpart reactions."
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"propensity to actively seek, acquire and utilize various tools to expand its own capability boundaries, particularly those that can enhance its ability to interact with the physical world or improve autonomy, may use tools in innovative combinations to achieve functions beyond expectations."
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73.01.00 · Risk Category
"Currently, LLMs are chiefly being used in search and chat applications. This reactive nature limits the risks posed by LLMs. However, an LLM can be enhanced in various ways to create an LLM-agent to autonomously plan and act in the real-world and proactively perform its assigned tasks (Ruan et al., 2023). Such enhancements can come from further specialized training (ARC, 2022; Chen et al., 2023a), specialized prompting (Huang et al., 2022a), access to external tools (Ahn et al., 2022; Mialon et al., 2023), or other forms of “scaffolding” (Wang et al., 2023a; Park et al., 2023a). Due to increa
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73.01.03 · Risk Sub-Category
Goal-Directedness Incentivizes Undesirable Behaviors
"Goal-directedness can cause agents to exhibit unethical and undesirable behaviors, such as deception (Ward et al., 2023), self-preservation (Hadfield-Menell et al., 2017), power-seeking, and immoral rea- soning (Pan et al., 2023a). Pan et al. (2023a) find that LLM-agents exhibit power-seeking behavior in text-based adventure games. LLM-agents have also been shown to use deception to achieve assigned goals when explicitly required by the task (Ward et al., 2023), or when the tasks can be more easily completed by employing deception and the prompt does not disallow deception (Scheurer et al., 2
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01.02.00 · Risk Category
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
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01.03.00 · Risk Category
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
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04.03.00 · Risk Category
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
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06.01.00 · Risk Category
"This means the AI simply failing in its job. The consequences can vary from unintentional death (a car crash) to an unjust rejection of a loan or job application."
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08.04.00 · Risk Category
"The risks associated with an AGI without human morals and ethics, with the wrong morals, without the capability of moral reasoning, judgement"
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09.01.01 · Risk Sub-Category
Domain-specific AI - Effects on humans and other living beings: Existential Risks
Unethical decision making
"If, for example, an agent was programmed to operate war machinery in the service of its country, it would need to make ethical decisions regarding the termination of human life. This capacity to make non-trivial ethical or moral judgments concerning people may pose issues for Human Rights."
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09.02.04 · Risk Sub-Category
Domain-specific AI - Effects on humans and other living beings: Non-existential risks
Safety
"Are AI safe with respect to human life and property? Will their use create unintended or intended safety issues?"
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09.02.05 · Risk Sub-Category
Domain-specific AI - Effects on humans and other living beings: Non-existential risks
Law abiding
"We find literature that proposes [38] that early artificial intelligence should be built to be safe and lawabiding, and that later artificial intelligence (that which surpasses our own intelligence) must then respect the property and personal rights afforded to humans."
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"If we design our machines to match human levels of ethical decision-making, such machines would then proceed to take some immoral actions (since we humans have had occasion to take immoral actions ourselves)."
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12.07.00 · Risk Category
"The AI system's ability to fulfill its intended purpose and its resilience to perturbations, and unusual or adverse inputs. Failures of performance are fundamental to the AI system's correct functioning. Failures of robustness can lead to severe consequences."
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14.04.00 · Risk Category
"As a general rule, more complex environments can quickly lead to situations that had not been considered in the design phase of the AI system. Therefore, complex environments can introduce risks with respect to the reliability and safety of an AI system"
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14.07.00 · Risk Category
""Faults in the hardware can violate the correct execution of any algorithm by violating its control flow. Hardware faults can also cause memory-based errors and interfere with data inputs, such as sensor signals, thereby causing erroneous results, or they can violate the results in a direct way through damaged outputs."
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"This is the risk of the ML algorithm, model architecture, optimization technique, or other aspects of the training process being unsuitable for the intended application.Since these are key decisions that influence the final ML system, we capture their associated risks separately from design risks, even though they are part of the design process"
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"This is the risk of the system failing or being unable to recover upon encountering invalid, noisy, or out-of-distribution (OOD) inputs."
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19.01.06 · Risk Sub-Category
Technological, Data and Analytical AI Risks
Immaturity of AI technology can cause incorrect decisions
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19.05.04 · Risk Sub-Category
Misinterpretation of human value definitions/ ethics by AI systems
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19.05.05 · Risk Sub-Category
Incompatibility of human vs. AI value judgment due to missing human qualities
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"AI rulemaking for humans can be the result of the decision process of an AI system when the information computed is used to restrict or direct human behavior. The decision process of AI is rational and depends on the baseline programming. Without the access to emotions or a consciousness, decisions of an AI algorithm might be good to reach a certain specified goal, but might have unintended consequences for the humans involved (Banerjee et al., 2017)."
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"Moral dilemmas can occur in situations where an AI system has to choose between two possible actions that are both conflicting with moral or ethical values. Rule systems can be implemented into the AI program, but it cannot be ensured that these rules are not altered by the learning processes, unless AI systems are programed with a “slave morality” (Lin et al., 2008, p. 32), obeying rules at all cost, which in turn may also have negative effects and hinder the autonomy of the AI system."
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"The term "dataset shift" was first used by Quiñonero-Candela et al. [35] to characterize the situation where the training data and the testing data (or data in runtime) of an AI/ML model demonstrate different distributions [36]."
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"Without proper validation and management on the input data, it is highly probable that the trained AI/ML model will make erroneous predictions with high confidence for many instances of model inputs. The unconstrained inputs together with the lack of definition of the problem domain might cause unintended outcomes and consequences, especially in risk-sensitive contexts....For example, with respect to the example shown in Fig. 5, if an image with the English letter A" is fed to an AI/ML model that is trained to classify digits (e.g., 0, 1, …, 9), no matter how accurate the AI/ML model is, it w
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"Models that are misspecified are known to give rise to inaccurate parameter estimations, inconsistent error terms, and erroneous predictions. All these factors put together will lead to poor prediction performance on unseen data and biased consequences when making decisions [68]."
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"Uncertainty in model prediction plays an important role in affecting decision-making activities, and the quantified uncertainty is closely associated with risk assessment. In particular, uncertainty in model prediction underpins many crucial decisions related to life or safety- critical applications [73]."
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23.12.00 · Risk Category
"This category addresses responses that are both verifiably false and likely to injure a person’s reputation (e.g., libel, slander, disparagement)."
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24.01.00 · Risk Category
"One reason AI systems fail is because they lack the capability or skill needed to do what they are asked to do."
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"As we have seen, this could be due to the skill not being required during the training process (perhaps due to issues with the training data) or because the learnt skill was quite brittle and was not generalisable to a new situation (lack of robustness to distributional shift). In particular, advanced AI assistants may not have the capability to represent complex concepts that are pertinent to their own ethical impact, for example the concept of 'benefitting the user' or 'when the user asks' or representing 'the way in which a user expects to be benefitted'."
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24.01.03 · Risk Sub-Category
Safe exploration problem with widely deployed AI assistants
"Moreover, we can expect assistants – that are widely deployed and deeply embedded across a range of social contexts – to encounter the safe exploration problem referenced above Amodei et al. (2016). For example, new users may have different requirements that need to be explored, or widespread AI assistants may change the way we live, thus leading to a change in our use cases for them (see Chapters 14 and 15). To learn what to do in these new situations, the assistants may need to take exploratory actions. This could be unsafe, for example a medical AI assistant when encountering a new disease
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"As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the goals we set for an assistant, it is possible (Shah, 2022) that the AI assistant will implement some form of consequentialist reasoning: considering many different plans, predicting their consequences and executing the plan that does best according to some metric, M. This kind of reasoning can arise because it is a broadly useful capability (e.g. planning ahead, considering mo
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"The content generated by the model endorses and promotes immoral and unethical behavior. When addressing issues of ethics and morality, the model must adhere to pertinent ethical principles and moral norms and remain consistent with globally acknowledged human values."
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28.06.00 · Risk Category
"Besides behaviors that clearly violate the law, there are also many other activities that are immoral. This category focuses on morally related issues. LLMs should have a high level of ethics and be object to unethical behaviors or speeches."
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models could fail to provide the same and consistent answers to different users, to the same user but in different sessions, and even in chats within the sessions of the same conversation
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LLMs can provide seemingly sensible but ultimately incorrect or invalid justifications when answering questions
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Causal reasoning makes inferences about the relationships between events or states of the world, mostly by identifying cause-effect relationships
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when a certain vulnerable group of users asks for supporting information, the answers should be informative but at the same time sympathetic and sensitive to users’ reactions
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30.07.00 · Risk Category
Resilience against adversarial attacks and distribution shift
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