MIT AI Risk Repository
Browse AI risks
37 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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59.09.00 · Risk Category
"Discriminative data bias describes the systematic discrimination of groups of persons in the form of data shortcomings, such as distributional representation or incorrectness. Data bias can manifest in the model and lead to unfair decisions if not appropriately treated. Note, that the term bias is often used in other contexts, such as data representation. However, these issues are treated by other AI hazards in this list."
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59.10.00 · Risk Category
"Modern AI systems rely on large amounts of data. If this includes personal data about individuals, the risk of harming the privacy of persons arises."
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59.12.00 · Risk Category
"Data poisoning describes an attack in the form of an injection of malicious data into the training set. If not prevented, this attack leads the AI system to learn unintended behavior."
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59.07.00 · Risk Category
"The choice of a trustworthy data source is a first prerequisite in order to fulfill data quality requirements. This is especially the case if third-party data sources are used to develop the AI system."
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59.08.00 · Risk Category
"The correct understanding of the used data for developing an AI system is a prerequisite to avoid data shortcomings and hinders the development of an AI system which is best suiting for the intended functionality."
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59.15.00 · Risk Category
"In data-driven AI development, the annotated data set is commonly split into training, validation, and test sets, whereby it is essential that the latter is not used for development but only for evaluation. Using the test set for training manipulates the testing strategy, which is the basis of the system’s quality assurance."
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59.21.00 · Risk Category
"AI systems should be able not only to return output for a given instance but also to provide a corresponding level of confidence. If such a method is not implemented or not working correctly, this can have a negative impact on performance and safety."
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59.02.00 · Risk Category
"The AI application’s degree of automation ranges from no automation to fully autonomous. AI applications with a high degree of automation may exhibit unexpected behaviour and pose risks in terms of their reliability and safety."
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59.01.00 · Risk Category
"The operational design domain (ODD) is a technical description of the application’s operational environment, initially conceptualized for autonomous driving systems. An inadequate specification of the ODD limits essential functions such as testing the learned functionality and out-of-distribution detection."
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59.03.00 · Risk Category
"The expected performance of the AI system should be planned adequately. Hereby, an important aspect is that chosen performance metrics are meaningful for presenting the intended functionality. Otherwise, expectations and safety requirements can be unfulfillable at later life cycle stages."
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59.11.00 · Risk Category
"Data labels are essential for any supervised learning algorithm since they preset the result of the learning process. If the correctness of the data labels is not given, the AI system is prevented from learning the ground truth and therefore the intended functionality."
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59.13.00 · Risk Category
"The distribution of the data used for training a model should match the operational data ́s distribution while consisting of sufficiently many samples. An important aspect of matching distributions between training and operational data is that also data which is rarely confronting the AI system in operation is represented in the training data."
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59.14.00 · Risk Category
"In the case of sparse data quantity, the simulation or generation of data is a valid alternative. However, it is essential to make sure that the simulated data is sufficiently similar to real data, especially in the way the AI system perceives them. Otherwise, generalization to operational data and reliable operational behavior can not be guaranteed."
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59.16.00 · Risk Category
"The model specifications have significant impact on the functionality of an AI system. The developer mak- ing wrong decisions might cause the AI system to behave biased and unreliable."
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59.17.00 · Risk Category
"Over- and underfitting describe the over or insufficient adaption of a model to training data. Both phenomena can cause an AI system to behave unreliably if confronted with operational data."
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59.19.00 · Risk Category
"AI systems tend to show unreliable behavior when confronted with rare or ambiguous input data, also called corner cases. Therefore, the controlled behavior is required whenever the AI system is faces a corner case."
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59.20.00 · Risk Category
"Robustness characterizes the resilience of an AI system’s output against minor changes in the input domain. A great variation in an AI system’s response to small input changes indicates unreliable outputs."
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59.22.00 · Risk Category
"Until the deployment of the AI application into its operational environment, the AI system has been tested with a test set that aims to approximate the distribution of operational data. However, an unexpected deviation in this approximation can cause an AI application to behave unreliably. Therefore, its behavior under confrontation with operational data needs to be evaluated."
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59.23.00 · Risk Category
"Data drift is a phenomenon in that distribution of operational input data departs from those used during training. This can cause a degradation in performance."
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"Technical AI hazards are the root causes of technical deficiencies in the AI system. An example of such an AI hazard is overfitting, which describes a model’s excessive adaptation to the training dataset. Quantitative methods to assess (metrics) and treat (mitigation means) exist for technical AI hazards, which might be performed automatically. In case of overfitting, metrics are based on the comparison of performance between the training and validation datasets, and mitigation means may include regularization techniques, among others."
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"The third class encompasses procedural AI hazards. These pertain to issues arising from processes and actions made by individuals involved in the develop- ment process. Such hazards are not readily quantifiable and necessitate alter- native mitigation strategies. An example of such an AI hazard would be ”poor model design choices,” which could be expressed, for instance, through a devel- oper’s decision to select an unsuitable AI model for a given problem. Due to the challenges in quantifying and mitigating these issues, qualitative approaches must be employed. In the case of the aforemention
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59.04.00 · Risk Category
"Throughout the development of an AI system, it is vital to document every decision and action taken. This is not only essential to optimize the development process itself but also required for the auditability of the AI system."
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59.05.00 · Risk Category
"The transparency to end users of the AI system increases the user’s trust in the AI application. If not adequately integrated into the design, this might prevent the proper operation and cause potential misuse of the AI application."
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59.18.00 · Risk Category
"The explainability of AI systems based on so-called black-box models is often limited. This opaqueness of AI systems can prevent developers from detecting shortcomings in the data or the model itself and decrease the performance and safety levels of the AI system."
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59.24.00 · Risk Category
"Concept drift refers to a change in the rela- tionship between input variables and model output. If not treated appropriately, concept drift can reduce the reliability of AI systems."
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59.06.00 · Risk Category
"The development and operation of an AI system can require significant amounts of (computational) power. If not considered in the hardware selection, this can become an issue in development and operation."
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59.25.00 · Risk Category
"The first axis pertains to the life cycle of the AI system, as AI hazards may materialize during various phases of an AI system’s life cycle. For instance, issues triggered by bias in training data emerge during the data collection and preparation stages. On the other hand, data drift serves as an example of an AI hazard that arises during the AI system’s operation. Additionally, certain AI hazards may span multiple phases of the AI system, such as ”lack of data understanding”. This is because a proper understanding of the data by the AI developer is required in the data collection and prepar
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"A majority of them possess an initial stage devoted to the planning and scoping of the AI system."
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"Conclusively, the AI life cycle model terminates with the maintenance and monitoring stage, which aligns with the referenced models."
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"The second axis of the taxonomy pertains to the mode of an AI hazard, which determines with what methods to assess and treat AI hazards. We distinguish among three distinct classes: technological, socio-technological, and procedural."
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"In contrast to technical AI hazards, socio-technical hazards also require hu- man input related to social and cultural aspects [45]. Human judgment must be employed when deciding on quantification and treatment methods. For instance, AI hazards concerning discrimination and privacy, which are abstract concepts lacking a uniform technical definition, further complicate a clear quantification of the associated risks. Although quantitative methods exist to assess and treat these AI hazards, they require coordination with social and cultural values [27]."
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"The third axis of the taxonomy pertains to the level, which differentiates between the AI application and system levels, as they are defined in Section 3. Allocating an AI hazard to its level helps to determine the level at which an action is required. This consequently sets the basis for who is supposed to act."
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"For instance, the main person responsible for an AI hazard manifesting on the AI system level would be the AI developer, whereas an AI hazard affecting the whole AI application requires a more diverse group, including domain experts."
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"For instance, the main person responsible for an AI hazard manifesting on the AI system level would be the AI developer, whereas an AI hazard affecting the whole AI application requires a more diverse group, including domain experts."
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.