Citibank, N.a.

Sofia Rahman

James Myers

Daniel Lewandowski

Philip Davies

Payal Jain

Biraj Krushna Rath

Stuart Murray

Mariusz Saternus

Tariq Husayn Maonah

Chamindra Desilva

Vishal Mysore

Shardul Malviya

Samantha Cory

Deepak Jain

Wayne Liao

Prashant Praveen

Ramkumar Ayyadurai

Christopher Tucker

Miriam Silver

Prithvi Narayana Rao

William Franklin Cameron

Pramod Goyal

Manjit Rajaretnam

The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains a set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

Managing operational resilience of system assets using an artificial intelligence model

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output,? AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Identifying and remediating gaps in artificial intelligence use cases using a generative artificial intelligence model

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Systems and methods are disclosed comprising instructions to access audio data of a captured interaction involving one or more participating users of a digital conference tool, receive a first narrative summary of the captured interaction comprising a set of first component narratives, generate a second narrative summary comprising a set of second component narratives, identify at least one component narrative among the set of second component narratives corresponding to the participating users, display the second component narratives of the second narrative summary at a user interface of the participating users, receive user feedback data comprising an adjustment to the displayed text content of the at least one second component narrative, generate a third narrative summary using the user feedback data received from each participating user of the captured interaction, and display the third narrative summary for the captured interaction at the user interface of each participating user.

Multi-speaker speech signal to text signal validation

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Dynamic multi-model monitoring and validation for artificial intelligence models

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New York, NY, United States of America

Sofia Rahman