Understanding and mitigating the impact of race with adversarial autoencoders.

ABSTRACT:

Background

Artificial intelligence carries the risk of exacerbating some of our most challenging societal problems, but it also has the potential of mitigating and addressing these problems. The confounding effects of race on machine learning is an ongoing subject of research. This study aims to mitigate the impact of race on data-derived models, using an adversarial variational autoencoder (AVAE). In this study, race is a self-reported feature. Race is often excluded as an input variable, however, due to the high correlation between race and several other variables, race is still implicitly encoded in the data.

Methods

We propose building a model that (1) learns a low dimensionality latent spaces, (2) employs an adversarial training procedure that ensure its latent space do

SUBMITTER: Sarullo K 

PROVIDER: S-EPMC11496710 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature