Representative & Fair Synthetic Data

TL;DR

This paper introduces a framework for generating synthetic data that is both representative and fair, aiming to reduce societal biases in AI training data while preserving data utility.

Contribution

It proposes a novel method to incorporate fairness constraints into self-supervised generative models, enabling the creation of unbiased synthetic datasets.

Findings

Successfully generated fair synthetic data for the UCI Adult census dataset.

Biases in gender and race are controlled while maintaining data relationships.

Downstream models trained on synthetic data show reduced bias compared to original data.

Abstract

Algorithms learn rules and associations based on the training data that they are exposed to. Yet, the very same data that teaches machines to understand and predict the world, contains societal and historic biases, resulting in biased algorithms with the risk of further amplifying these once put into use for decision support. Synthetic data, on the other hand, emerges with the promise to provide an unlimited amount of representative, realistic training samples, that can be shared further without disclosing the privacy of individual subjects. We present a framework to incorporate fairness constraints into the self-supervised learning process, that allows to then simulate an unlimited amount of representative as well as fair synthetic data. This framework provides a handle to govern and control for privacy as well as for bias within AI at its very source: the training data. We demonstrate the proposed approach by amending an existing generative model architecture and generating a representative as well as fair version of the UCI Adult census data set. While the relationships between attributes are faithfully retained, the gender and racial biases inherent in the original data are controlled for. This is further validated by comparing propensity scores of downstream predictive models that are trained on the original data versus the fair synthetic data. We consider representative & fair synthetic data a promising future building block to teach algorithms not on historic worlds, but rather on the worlds that we strive to live in.