USPTO Patent Grant: Mitigating Bias in Machine Learning Decisioning
Summary
The USPTO has granted a patent (US12585956B2) to SAS INSTITUTE INC. for a system and method to mitigate bias in machine learning-based decisioning models. The invention focuses on detecting bias, performing counterfactual assessments, and generating explainability artifacts for user interfaces.
What changed
The United States Patent and Trademark Office (USPTO) has granted patent US12585956B2 to SAS INSTITUTE INC. for a novel system, method, and computer-program product designed to mitigate bias in machine learning-based decisioning models. The patented technology involves obtaining decisioning datasets, using machine learning algorithms to detect bias by comparing favorable and unfavorable decisioning records, executing counterfactual assessments, and generating explainability artifacts that detail the bias intensity. These artifacts are then displayed in a user interface.
This patent grant signifies a technological advancement in addressing algorithmic bias, particularly relevant for organizations developing or deploying AI-driven decisioning systems. While patents do not impose direct regulatory obligations, they represent proprietary technology that could influence future industry standards or product development. Companies utilizing machine learning for critical decisions, such as loan applications, hiring, or risk assessments, should be aware of this patented approach, as it may impact the competitive landscape and the availability of certain bias mitigation techniques.
Source document (simplified)
Systems, methods, and graphical user interfaces for mitigating bias in a machine learning-based decisioning model
Grant US12585956B2 Kind: B2 Mar 24, 2026
Assignee
SAS INSTITUTE INC.
Inventors
Luiz Henrique Outi Kauffmann, Aline Riquetti Campos Emídio
Abstract
A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.
CPC Classifications
G06N 5/045
Filing Date
2025-05-02
Application No.
19196841
Claims
30
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