US 2024/0256853 A1 · SYSTEM AND METHOD FOR MANAGING LATENT BIAS IN CLUSTERING BASED INFERENCE MODELS

Abstract

Methods, systems, and devices for providing computer-implemented services are disclosed. To provide the computer-implemented services, inference models used by data processing systems may be managed to reduce latent bias. The inference models may be managed by establishing supervised models based on the results of unsupervised learning. The supervised models may then be subjected to training to reduce the levels of latent bias, and analysis to identify features contributing to the latent bias. The supervised learning may then be performed without consideration for the identified features.

Claims20

Dependent claims

A method for managing an inference model, the method comprising:

clustering a data set into first clusters to obtain first cluster assignments for records of the data set;
training a multiheaded inference model using the records and the first cluster assignments;
performing modified split training of the multiheaded inference model to obtain an updated multiheaded inference model;
obtaining an updated data set using the updated multiheaded inference model by removing at least one feature from the data set based on a predictive power level of the multiheaded inference model with respect to the first cluster assignments and a predictive power level for bias features;
clustering the updated data set into second clusters to obtain second cluster assignments for records of the updated data set;
and
providing computer implemented services using the second cluster assignments.

4 more elements

A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing an inference model, the operations comprising:

clustering a data set into first clusters to obtain first cluster assignments for records of the data set;
training a multiheaded inference model using the records and the first cluster assignments;
performing modified split training of the multiheaded inference model to obtain an updated multiheaded inference model;
obtaining an updated data set using the updated multiheaded inference model by removing at least one feature from the data set based on a predictive power level of the multiheaded inference model with respect to the first cluster assignments and a predictive power level for bias features;
clustering the updated data set into second clusters to obtain second cluster assignments for records of the updated data set;
and
providing computer implemented services using the second cluster assignments.

4 more elements

Dependencies are inferred from the claim text.

System and method for managing latent bias in clustering based inference models

Life of this patent

System and method for managing latent bias in clustering based inference models

Life of this patent

Abstract

Claims20