Automatic correction of indirect bias in machine learning models

Patent metadata

Field	Value
Publication number	US-11068797-B2
Application number	US-201816176570-A
Country	US
Kind code	B2
Filing date	Oct 31, 2018
Priority date	Oct 31, 2018
Publication date	Jul 20, 2021
Grant date	Jul 20, 2021

Abstract

Systems and methods for detecting indirect bias in machine learning models are provided. A computer-implemented method includes: receiving, by a computer device, a user request to detect transitive bias in a machine learning model; determining, by the computer device, correlations of attributes of neighboring data not included in a dataset of the machine learning model; ranking, by the computer device, the attributes based on the determined correlations; and returning, by the computer device, a list of the ranked attributes to a user that generated the user request.

First claim

What is claimed is:

1. A computer-implemented method comprising:
receiving, by a computer device, a user request to detect transitive bias in a machine learning model;
determining, by the computer device, correlations of attributes of neighboring data not included in a dataset of the machine learning model;
ranking, by the computer device, the attributes based on the determined correlations; and
returning, by the computer device, a list of the ranked attributes to a user that generated the user request.

2. The computer-implemented method of claim 1 , wherein:
the computer device is a server;
the server receives the user request from an enterprise device via a callback application program interface (API); and
the server returns the list of the ranked attributes to the enterprise device via the callback API.

3. The computer-implemented method of claim 1 , wherein the dataset of the machine learning model comprises training data and run time data.

4. The computer-implemented method of claim 3 , further comprising the computer device determining the neighboring data prior to the determining the correlations of the attributes.

5. The computer-implemented method of claim 1 , wherein the user request comprises:
an identification of the machine learning model; and
an identification of feature vectors of the machine learning model.

6. The computer-implemented method of claim 1 , wherein the user request comprises an indication to perform run time bias detection of the machine learning model.

7. The computer-implemented method of claim 1 , wherein the user request comprises an indication to perform design time bias detection of the machine learning model.

8. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computer device to:
receive a user request to detect hidden bias in a machine learning model;
classify data included in a dataset of the machine learning model based on the user request;
obtain auxiliary information based on the classifying;
determine correlations of attributes of the auxiliary information;
rank the attributes based on the determined correlations; and
return a list of the ranked attributes to a user that generated the user request.

9. The computer program product of claim 8 , wherein the computer device is a server that receives the user request from an enterprise device via a callback application program interface (API).

10. The computer program product of claim 9 , wherein the server returns the list of the ranked attributes to the enterprise device via the callback API.

11. The computer program product of claim 8 , wherein the dataset of the machine learning model comprises training data and run time data.

12. The computer program product of claim 8 , wherein the user request comprises an identification of the machine learning model.

13. The computer program product of claim 12 , wherein the user request comprises an indication to perform run time bias detection of the machine learning model.

14. The computer program product of claim 12 , wherein the user request comprises an indication to perform design time bias detection of the machine learning model.

15. A system comprising:
a processor, a computer readable memory, and a computer readable storage medium;
program instructions to receive a user request to detect hidden bias in a machine learning model;
program instructions to classify data included in a dataset of the machine learning model;
program instructions to obtain auxiliary information based on the classifying;
program instructions to determine correlations of attributes of the auxiliary information;
program instructions to rank the attributes based on the determined correlations; and
program instructions to return a list of the ranked attributes to a user that generated the user request,
wherein the program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory.

16. The system of claim 15 , wherein:
the processor is included in a server;
the server receives the user request from an enterprise device via a callback application program interface (API); and
the server returns the list of the ranked attributes to the enterprise device via the callback API.

17. The system of claim 16 , wherein the dataset of the machine learning model comprises training data and run time data.

18. The system of claim 17 , wherein the user request comprises an identification of the machine learning model.

19. The system of claim 18 , wherein the user request comprises an indication to perform run time bias detection of the machine learning model.

20. The system of claim 18 , wherein the user request comprises an indication to perform design time bias detection of the machine learning model.

Assignees

• IBM

Inventors

• Bhide Manish
• Puri Ruchir
• Chamarthy Ravi Chandra

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06N5/048 Primary

  Fuzzy inferencing · CPC title

• G06F9/547

  Remote procedure calls [RPC]; Web services · CPC title