Biased based delegation in machine learning models

Patent metadata

Field	Value
Publication number	US-2022318685-A1
Application number	US-202117223158-A
Country	US
Kind code	A1
Filing date	Apr 6, 2021
Priority date	Apr 6, 2021
Publication date	Oct 6, 2022
Grant date	—

Abstract

An approach is provided in which the approach receives a job request, which includes biasing parameters, from an entity operating in a distributive cognitive system. The approach evaluates the biasing parameters against a set of machine learning model bias characteristics corresponding to a set of machine learning models and selects one of the machine learning models based on the evaluating. The approach assigns the selected machine learning model to the entity to perform the job request.

First claim

1 . A method comprising:
receiving a job request from an entity operating in a distributive cognitive system, wherein the job request comprises one or more biasing parameters; evaluating the one or more biasing parameters against a plurality of machine learning model bias characteristics corresponding to a plurality of machine learning models; selecting one of the plurality of machine learning models based on the evaluating; and assigning the job request to the selected machine learning model.

2 . The method of claim 1 further comprising:
receiving, from each of the plurality of machine learning models, their corresponding one of the plurality of bias characteristics prior to receiving the job request, wherein each of the plurality of bias characteristics comprise one or more bias features and one or more weightage factors.

3 . The method of claim 2 further comprising:
grouping the plurality of machine learning models into a set of machine learning model groups based on their corresponding one or more bias features and their one or more weightage factors.

4 . The method of claim 3 further comprising:
receiving a list of fake features from each of the plurality of machine learning models; and
grouping the plurality of machine learning models into the set of machine learning model groups based on their corresponding one or more bias features, their one or more weightage factors, and their corresponding list of fake features.

5 . The method of claim 4 further comprising:
identifying one of the set of machine learning model groups that comprises at least one fake feature that matches at least one of the one or more biasing parameters; and
omitting the identified machine learning model group from the evaluating.

6 . The method of claim 1 further comprising:
determining whether the distributed cognitive system is authenticated to use a bias-based service; and
dispatching the job request to the selected machine learning model in response to determining that the distributed cognitive system is authenticated to use the bias based service.

7 . The method of claim 1 wherein each one of the plurality of machine learning models is instantiated on an orchestration plane in the distributive cognitive system.

8 . An information handling system comprising:
one or more processors; a memory coupled to at least one of the processors; a set of computer program instructions stored in the memory and executed by at least one of the processors in order to perform actions of:
receiving a job request from an entity operating in a distributive cognitive system, wherein the job request comprises one or more biasing parameters;
evaluating the one or more biasing parameters against a plurality of machine learning model bias characteristics corresponding to a plurality of machine learning models;
selecting one of the plurality of machine learning models based on the evaluating; and
assigning the job request to the selected machine learning model.

9 . The information handling system of claim 8 wherein the processors perform additional actions comprising:
receiving, from each of the plurality of machine learning models, their corresponding one of the plurality of bias characteristics prior to receiving the job request, wherein each of the plurality of bias characteristics comprise one or more bias features and one or more weightage factors.

10 . The information handling system of claim 9 wherein the processors perform additional actions comprising:
grouping the plurality of machine learning models into a set of machine learning model groups based on their corresponding one or more bias features and their one or more weightage factors.

11 . The information handling system of claim 10 wherein the processors perform additional actions comprising:
receiving a list of fake features from each of the plurality of machine learning models; and
grouping the plurality of machine learning models into the set of machine learning model groups based on their corresponding one or more bias features, their one or more weightage factors, and their corresponding list of fake features.

12 . The information handling system of claim 11 wherein the processors perform additional actions comprising:
identifying one of the set of machine learning model groups that comprises at least one fake feature that matches at least one of the one or more biasing parameters; and
omitting the identified machine learning model group from the evaluating.

13 . The information handling system of claim 8 wherein the processors perform additional actions comprising:
determining whether the distributed cognitive system is authenticated to use a bias-based service; and
dispatching the job request to the selected machine learning model in response to determining that the distributed cognitive system is authenticated to use the bias based service.

14 . The information handling system of claim 8 wherein each one of the plurality of machine learning models is instantiated on an orchestration plane in the distributive cognitive system

15 . A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising:
receiving a job request from an entity operating in a distributive cognitive system, wherein the job request comprises one or more biasing parameters; evaluating the one or more biasing parameters against a plurality of machine learning model bias characteristics corresponding to a plurality of machine learning models; selecting one of the plurality of machine learning models based on the evaluating; and assigning the job request to the selected machine learning model.

16 . The computer program product of claim 15 wherein the information handling system performs further actions comprising:
receiving, from each of the plurality of machine learning models, their corresponding one of the plurality of bias characteristics prior to receiving the job request, wherein each of the plurality of bias characteristics comprise one or more bias features and one or more weightage factors.

17 . The computer program product of claim 16 wherein the information handling system performs further actions comprising:
grouping the plurality of machine learning models into a set of machine learning model groups based on their corresponding one or more bias features and their one or more weightage factors.

18 . The computer program product of claim 17 wherein the information handling system performs further actions comprising:
receiving a list of fake features from each of the plurality of machine learning models; and
grouping the plurality of machine learning models into the set of machine learning model groups based on their corresponding one or more bias features, their one or more weightage factors, and their corresponding list of fake features.

19 . The computer program product of claim 18 wherein the information handling system performs further actions comprising:
identifying one of the set of machine learning model groups that comprises at least one fake feature that matches at least one of the one or more biasing parameters; and
omitting the identified machine learning model group from the evaluating.

20 . The computer program product of claim 15 wherein the information handling system performs further actions comprising:
determining whether the distrib

Assignees

• IBM

Inventors

• Sivakumar Gandhi
• Kwok Lynn
• Patel Kushal S
• Patel Sarvesh S

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title