System and method for selectively managing latent bias in inference models

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

• obtaining inference goals for label inferences and bias feature inferences for a multiheaded inference model of the inference models;
• obtaining learning rates for label prediction heads and bias feature heads of the multiheaded inference model using the inference goals;
• training the multiheaded inference model based on the learning rate to obtain a trained multiheaded inference model;
• identifying, for the trained multiheaded inference model, predictive power levels for labels and predictive power labels for bias features;
• making a determination, based on the predictive power levels for the labels and the predictive power labels for the bias features, regarding whether the trained multiheaded inference model is acceptable;
• and
• in an instance of the determination where the multiheaded inference model is acceptable:
• providing computer implemented services using the trained multiheaded inference model.

A data processing system, comprising:

• a processor;
• and
• a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing inference models, the operations comprising:
• obtaining inference goals for label inferences and bias feature inferences for a multiheaded inference model of the inference models;
• obtaining learning rates for label prediction heads and bias feature heads of the multiheaded inference model using the inference goals;
• training the multiheaded inference model based on the learning rate to obtain a trained multiheaded inference model;
• identifying, for the trained multiheaded inference model, predictive power levels for labels and predictive power labels for bias features;
• making a determination, based on the predictive power levels for the labels and the predictive power labels for the bias features, regarding whether the trained multiheaded inference model is acceptable;
• and
• in an instance of the determination where the multiheaded inference model is acceptable:
• providing computer implemented services using the trained multiheaded inference model.