MIT AI Risk Repository
Browse AI risks
422 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.
-
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."
-
"Accidents include unintended failure modes that, in principle, could be considered the fault of the system or the developer"
-
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"
-
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."
-
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?"
-
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."
-
"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)."
-
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."
-
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"
-
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."
-
This is the risk posed by an ideal system if used for a purpose/in a manner unintended by its creators. In many situations, negative consequences arise when the system is not used in the way or for the purpose it was intended.
-
"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"
-
"This is the risk of the system failing or being unable to recover upon encountering invalid, noisy, or out-of-distribution (OOD) inputs."
-
"This is the risk of system failure due to system design choices or errors."
-
This is the risk of direct or indirect physical or psychological injury resulting from interaction with the ML system.
-
19.01.06 · Risk Sub-Category
Technological, Data and Analytical AI Risks
Immaturity of AI technology can cause incorrect decisions
—
-
—
-
—
-
19.05.04 · Risk Sub-Category
Misinterpretation of human value definitions/ ethics by AI systems
—
-
19.05.05 · Risk Sub-Category
Incompatibility of human vs. AI value judgment due to missing human qualities
—
-
"Ethical challenges are widely discussed in the literature and are at the heart of the debate on how to govern and regulate AI technology in the future (Bostrom & Yudkowsky, 2014; IEEE, 2017; Wirtz et al., 2019). Lin et al. (2008, p. 25) formulate the problem as follows: “there is no clear task specification for general moral behavior, nor is there a single answer to the question of whose morality or what morality should be implemented in AI”. Ethical behavior mostly depends on an underlying value system. When AI systems interact in a public environment and influence citizens, they are expecte
-
"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)."
-
"Compatibility of machine and human value judgment refers to the challenge whether human values can be globally implemented into learning AI systems without the risk of developing an own or even divergent value system to govern their behavior and possibly become harmful to humans."
-
"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."
-
"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]."
-
"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
-
"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]."
-
"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]."
-
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)."
-
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."
-
"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'."
-
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
-
"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
-
"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."
-
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."
-
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
-
LLMs can provide seemingly sensible but ultimately incorrect or invalid justifications when answering questions
-
Causal reasoning makes inferences about the relationships between events or states of the world, mostly by identifying cause-effect relationships
-
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
-
30.07.00 · Risk Category
Resilience against adversarial attacks and distribution shift
-
33.02.00 · Risk Category
"Challenges related to technology refer to the limitations or constraints associated with generative AI. For example, the quality of training data is a major challenge for the development of generative AI models. Hallucination, explainability, and authenticity of the output are also challenges resulting from the limitations of the algorithms. Table 2 presents the technology challenges and issues associated with generative AI. These challenges include hallucinations, training data quality, explainability, authenticity, and prompt engineering"
-
"The quality of training data is another challenge faced by generative AI. The quality of generative AI models largely depends on the quality of the training data (Dwivedi et al., 2023; Su & Yang, 2023). Any factual errors, unbalanced information sources, or biases embedded in the training data may be reflected in the output of the model. Generative AI models, such as ChatGPT or Stable Diffusion which is a text-to-image model, often require large amounts of training data (Gozalo-Brizuela & Garrido-Merchan, 2023). It is important to not only have high-quality training datasets but also have com
-
"Unethical behaviors in AI systems pertain to actions that counteract the common goodor breach moral standards – such as those causing harm to others. These adverse behaviors often stem fromomitting essential human values during the AI system's design or introducing unsuitable or obsolete valuesinto the system (Kenward and Sinclair, 2021)."
-
"This category constitutes more than 16% of the articles and focuses on addressing the potential risks associated with AI systems. Given the ubiquity of AI technologies, these articles explore the implications of AI risks across various contexts linked to design and unpredictability, military purposes, emergency procedures, and AI takeover."
-
39.04.00 · Risk Category
The robustness of an AI-based model refers to the stability of the model performance after abnormal changes in the input data... The cause of this change may be a malicious attacker, environmental noise, or a crash of other components of an AI-based system... This problem may be challenging in HLI-based agents because weak robustness may have appeared in unreliable machine learning models, and hence an HLI with this drawback is error-prone in practice.
-
39.12.00 · Risk Category
whether the decision of an AI-based agent can be predicted in every situation or not
-
39.27.00 · Risk Category
Nowadays, we are faced with systems that utilize numerous learning models in their modules for their perception and decision-making processes... One aspect of an AI-based system that leads to increasing the complexity of the system is the parameter space that may result from multiplications of parameters of the internal parts of the system
-
40.04.00 · Risk Category
"After the system has been deployed, it may still contain a number of undetected bugs, design mistakes, misaligned goals and poorly developed capabilities, all of which may produce highly undesirable outcomes. For example, the system may misinterpret commands due to coarticulation, segmentation, homophones, or double meanings in the human language ("recognize speech using common sense" versus "wreck a nice beach you sing calm incense") (Lieberman, Faaborg et al. 2005)."
-
"The assessment of how often a system performs the correct prediction."
-
"Less moral responsibility humans will feel regarding their life-or-death decisions with the increase of machines autonomy."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.