The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 14, 2024

Filed:

Feb. 22, 2023
Applicant:

At&t Intellectual Property I, L.p., Atlanta, GA (US);

Inventors:

Emily Dodwell, Jersey City, NJ (US);

Balachander Krishnamurthy, New York, NY (US);

Rajat Malik, Metuchen, NJ (US);

Ritwik Mitra, Trenton, NJ (US);

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2023.01); G06F 16/28 (2019.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06F 16/285 (2019.01); G06N 20/00 (2019.01);
Abstract

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.


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