Hitachi Deep Learning Bias Reduction Using Ensembles
Summary
USPTO granted Patent US12608595B2 to Hitachi, Ltd. on April 21, 2026, covering a method for reducing bias in deep learning classifiers through model ensembles. The patent describes training multiple ML models on different data subsets, validating each separately, pruning based on accuracy, and forming a reduced-bias ensemble. Inventors are Dipanjan Ghosh, Ahmed Farahat, and Chetan Gupta, with filing date January 23, 2023, and 13 claims.
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What changed
USPTO granted Patent US12608595B2 to Hitachi, Ltd. on April 21, 2026. The patent describes a method for generating model ensembles to reduce bias in deep learning classifiers by training multiple ML models on different data subsets, validating each on separate holdout data, pruning based on accuracy metrics, and combining the pruned subset into an ensemble. This approach addresses bias through ensemble diversity rather than single-model optimization.
Technology companies developing machine learning systems, AI researchers, and academic institutions working on bias mitigation in automated decision-making should note this patent's scope. The ensemble pruning methodology may be relevant to firms deploying ML in high-stakes applications such as hiring, lending, or healthcare where algorithmic bias is a compliance concern.
Archived snapshot
Apr 23, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Method for reducing bias in deep learning classifiers using ensembles
Grant US12608595B2 Kind: B2 Apr 21, 2026
Assignee
HITACHI, LTD.
Inventors
Dipanjan Ghosh, Ahmed Farahat, Chetan Gupta
Abstract
Example implementations described herein are directed to systems and methods for generating a model ensemble to reduce bias, the method involving training a plurality of machine learning models from data, each of the plurality of machine learning models trained from a first subset of the data and validated from a second subset of the data, each of the first subset and the second subset being different for each of the plurality of machine learning models; determining accuracy of each of the plurality of machine learning models based on validation against the second subset of the data; pruning the plurality of machine learning models based on the accuracy to generate a subset of the plurality of machine learning models; and forming the model ensemble from the subset of the plurality of machine learning models.
CPC Classifications
G06N 3/045 G06N 3/082
Filing Date
2023-01-23
Application No.
18100455
Claims
13
Mentioned entities
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