MIT AI Risk Repository
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4 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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"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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