Fairness-as-a-service, Inc.

Kareem Saleh

Mark Eberstein

John Wickens-Lamb Merrill

A system and method includes obtaining an incumbent model and a candidate model, generating a plurality of synthetic model input datasets, computing, for each synthetic model input dataset, a model performance efficacy metric and a model fairness efficacy metric for the incumbent model based on assessing model output data of the incumbent model that corresponds to each respective synthetic model input dataset of the plurality of synthetic model input datasets, computing, for each synthetic model input dataset, a model performance efficacy metric and a model fairness efficacy metric for the candidate model based on assessing model output data of the candidate model that corresponds to each respective synthetic model input dataset of the plurality of synthetic model input datasets, computing, for the candidate model, a disparity-mitigating model viability score, and displaying, via a graphical user interface, a representation of the candidate model in association with the disparity-mitigating model viability score.

Systems and methods for intelligent generation and assessment of candidate less discriminatory alternative machine learning models

A system and method includes generating approximate distributions for distinct classes of data samples; computing a first partial Jensen-Shannon (JS) divergence and a second partial JS divergence based on the approximate distribution of the disparity affected class of data samples with reference to the approximate distribution of the control class of data samples; computing a disparity divergence based on the first partial JS divergence and the second partial JS divergence; 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 augmenting a target loss function with the distribution-matching term; and transforming the unconstrained ANN to a disparity-constrained ANN based on a training of the unconstrained ANN using the disparity-constrained loss function.

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

Growing community of inventors

Marina Del Rey, CA, United States of America

Kareem Saleh