At&t Intellectual Property I, L.p.

Emily Dodwell

Balachander Krishnamurthy

Ritwik Mitra

Rajat Malik

Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.

Bias scoring of machine learning project data

Aspects of the disclosure include, for example, obtaining input data. Further embodiments include a determination of a fast path prediction for a first time period according to the input data based on a fast path model. Embodiments include providing instructions to deliver information to a user device according to the fast path prediction. Additional embodiments include obtaining additional input data. Embodiments include a determination of a slow path prediction for the first time period according to the input data and the additional input data based on a slow path model, retraining the fast path model according to the input data and the fast path prediction, and training the slow path model according to the slow path prediction. Embodiments include a determination of a fast path negative impact metric and determination of a slow path negative impact metric. Other embodiments are disclosed.

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Jersey City, NJ, United States of America

Emily Dodwell