Disparity mitigation in machine learning-based predictions for distinct classes of data using derived indiscernibility constraints during neural network training

A method of encouraging inferential fairness of an artificial neural network, the method comprising:

• computing a disparity divergence based on an approximate distribution of a non-disparity affected class of data samples and an approximate distribution of a disparity affected class of data samples;
• generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between artificial neural network inferences for the non-disparity affected class of data samples and the disparity affected class of data samples during a training of an artificial neural network;
• constructing a disparity-constrained loss function based on integrating the distribution-matching terms with a loss function;
• and
• training the artificial neural network using a training corpus of labeled data samples, wherein the training includes performing backpropagation using the disparity-constrained loss function.

A method comprising:

• computing a disparity divergence based on an estimated distribution of a disparity affected class of data samples and an estimated distribution of a control class of data samples;
• generating a distribution-matching term based on the disparity divergence, wherein the distribution-matching term mitigates an inferential disparity between the control class of data samples and the disparity affected class of data samples during a training of an unconstrained artificial neural network;
• constructing a disparity-constrained loss function based on including the distribution-matching term with a target loss function;
• and
• transforming the unconstrained artificial neural network to a disparity-constrained artificial neural network based on a training of the unconstrained artificial neural network using the disparity-constrained loss function.