BACKGROUND

The present invention relates to the field of artificial intelligence, and specifically to machine learning models used in artificial intelligence. Still more particularly, the present invention relates to optimizing the performance of machine learning models used in artificial intelligence.

SUMMARY

In an embodiment of the present invention, a method identifies and removes bias from a machine learning model. A user/computer inputs a plurality of input training data into a machine learning system to generate an output of labeled output data. The user/computer evaluates the labeled output data according to a consistency metric in order to associate the labeled output data with a corresponding consistency assessment. The user/computer selects each labeled output data having a consistency assessment indicating a consistency assessment that is greater than a predetermined threshold to form a labeled output data subset. The user/computer creates additional labeling for the labeled output data subset in order to create additional labeling for the labeled output data subset. The user/computer utilizes the additional labeling in order to distinguish each labeled training data from labeled output data subset as being mislabeled and biased, and then adjusts the learning machine based on the labeled output data subset being mislabeled and biased.

In one or more embodiments, the method(s) described herein are performed by an execution of a computer program product and/or a computer system.

