US 2024/0193470 A1Published application

Machine learning evaluation for detecting feature bias

AssigneeOptum, Inc.

InventorsArun Tiwari, Amardeep Sharma, Rahul Pathak, Sudhanshu Sharma, Urvi Sharma, Radhika Agarwal

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Abstract

Various embodiments of the present disclosure disclose machine-learning based evaluation techniques for detecting feature bias. An evaluation framework is provided that utilizes new evaluation data structures for comprehensibly evaluating feature bias in machine learning models. The evaluation framework includes receiving evaluation dataset for a machine learning model that includes one or more different feature classes of an evaluation feature. The evaluation framework includes generating, using an evaluation function, at least two performance metrics for the machine learning model and generating a unitless dissimilarity metric for the evaluation feature based at least in part on the first performance metric, the second performance metric, and an average between the first and second performance metrics. In this way, the unitless dissimilarity metric is based on a variability associated with the evaluation feature.

Claims20

Dependent claims

A computer-implemented method for evaluating potential bias in machine learning models, the computer-implemented method comprising:

receiving, by one or more processors, an evaluation dataset for a machine learning model, wherein the evaluation dataset comprises a plurality of evaluation data objects associated with one or more different feature classes of an evaluation feature;
identifying, by the one or more processors, at least one evaluation function for evaluating the machine learning model based at least in part on the evaluation dataset and the evaluation feature;
generating, by the one or more processors using the at least one evaluation function, at least two performance metrics for the machine learning model based at least in part on the plurality of evaluation data objects, wherein the at least two performance metrics comprise a first performance metric associated with a first feature class of the evaluation feature and a second performance metric associated with a second feature class of the evaluation feature;
generating, by the one or more processors, a unitless dissimilarity metric for the evaluation feature based at least in part on the first performance metric and the second performance metric;
and
providing, by the one or more processors, a machine learning verification output for the machine learning model based at least in part on the unitless dissimilarity metric.

3 more elements

A computing apparatus for evaluating potential bias in machine learning models comprising:

a processor;
and
a memory storing instructions that, when executed by the processor, configure the computing apparatus to:
receive an evaluation dataset for a machine learning model, wherein the evaluation dataset comprises a plurality of evaluation data objects associated with one or more different feature classes of an evaluation feature;
identify at least one evaluation function for evaluating the machine learning model based at least in part on the evaluation dataset and the evaluation feature;
generate, using the at least one evaluation function, at least two performance metrics for the machine learning model based at least in part on the plurality of evaluation data objects, wherein the at least two performance metrics comprise a first performance metric associated with a first feature class of the evaluation feature and a second performance metric associated with a second feature class of the evaluation feature;
generate a unitless dissimilarity metric for the evaluation feature based at least in part on the first performance metric and the second performance metric;
and
provide a machine learning verification output for the machine learning model based at least in part on the unitless dissimilarity metric.

6 more elements

Dependencies are inferred from the claim text.