MIT AI Risk Repository
Browse AI risks
13 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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59.01.00 · 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
Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.