Understanding and mitigating the impact of race with adversarial autoencoders

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. We propose building a model that (1) learns a low dimensionality latent spaces, (2) employs an adversarial training procedure that ensure its latent space does not encode race, and (3) contains necessary information for reconstructing the data. We train the autoencoder to ensure the latent space does not indirectly encode race. In this study, AVAE successfully removes information about race from the latent space (AUC ROC = 0.5). In contrast, latent spaces constructed using other approaches still allow the reconstruction of race with high fidelity. The AVAE’s latent space does not encode race but conveys important information required to reconstruct the dataset. Furthermore, the AVAE’s latent space does not predict variables related to race (R2 = 0.003), while a model that includes race does (R2 = 0.08). Though we constructed a race-independent latent space, any variable could be similarly controlled. We expect AVAEs are one of many approaches that will be required to effectively manage and understand bias in ML. Computer models used in healthcare can sometimes be biased based on race, leading to unfair outcomes. Our study focuses on understanding and reducing the impact of self-reported race in computer models that learn from data. We use a model called an Adversarial Variational Autoencoder (AVAE), which helps ensure that the models don’t accidentally use race in their calculations. The AVAE technique creates a simplified version of the data, called a latent space, that leaves out race information but keeps other important details needed for accurate predictions. Our results show that this approach successfully removes race information from the models while still allowing them to work well. This method is one of many steps needed to address bias in computer learning and ensure fairer outcomes. Our findings highlight the importance of developing tools that can manage and understand bias, contributing to more equitable and trustworthy technology. Sarullo and Swamidass use an adversarial variational autoencoder (AVAE) to remove race information from computer models while retaining essential data for accurate predictions, effectively reducing bias. This approach highlights the importance of developing tools to manage bias, ensuring fairer and more trustworthy technology.