| CPC G06N 3/0895 (2023.01) | 20 Claims |
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1. A non-transitory computer-readable storage medium comprising instructions thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to:
receive, from a computing device, (1) a set of alphanumeric characters defining one or more operative boundaries of a set of expected model use cases configured to adhere to constraints of the set of alphanumeric characters and (2) a set of operational data containing one or more of: structured data or unstructured data, wherein the set of expected model use cases include a set of attributes common among each expected model use case in the set of expected model use cases;
transmit the set of attributes common among the set of expected model use cases into one or more nodes of an input layer of a first set of AI models to receive, from one or more nodes of an output layer of the first set of AI models, a set of observed model use cases from the set of operational data,
wherein each particular observed model use case of the set of observed model use cases includes a set of features of the particular observed model use case, and
wherein the set of features of the particular observed model use case includes two or more of: a text-based description of the particular observed model use case, an expected input of the particular observed model use case, an expected output of the particular observed model use case, one or more AI models configured to generate the expected output of the particular observed model use case using the expected input of the particular observed model use case, or data supporting the one or more AI models;
transmit each particular observed model use case of the set of observed model use cases into one or more nodes of an input layer of a second set of AI models trained to:
map the set of alphanumeric characters and the set of features of the particular observed model use case to a risk category defined within a set of vector representations of the set of alphanumeric characters, wherein the second set of AI models is configured to select the risk category from a plurality of risk categories defined within the set of vector representations of the set of alphanumeric characters in accordance with a level of risk associated with the set of features,
identify a set of criteria of the particular observed model use case within the set of alphanumeric characters by: (1) extracting a set of keywords from the set of alphanumeric characters, and (2) mapping the extracted set of keywords to the set of criteria within the set of alphanumeric characters associated with the mapped risk category, and
identify a set of gaps of the particular observed model use case by comparing the set of criteria of the particular observed model use case with the set of features of the particular observed model use case;
using the set of gaps, generate a set of actions to be performed related to the particular observed model use case configured to cause the set of features of the particular observed model use case to satisfy the set of criteria of the particular observed model use case;
presenting a representation including one or more of: a graphical user interface component or a set of text via the computing device, wherein the representation indicates at least one of: the set of gaps or the set of actions;
responsive to a user input received via the computing device, automatically execute the set of actions to modify the set of operational data; and
transmit each particular observed model use case of the set of observed model use cases into the one or more nodes of the input layer of the second set of AI models to validate satisfaction of the set of criteria of each observed model use case.
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