Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure.

Recent research has highlighted the vulnerabilities of modern machine learning based systems to bias, especially towards segments of society that are under-represented in training data. In this work, we develop a novel, tunable algorithm for mitigating the hidden, and potentially unknown, biases within training data. Our algorithm fuses the original learning task with a variational autoencoder to learn the latent structure within the dataset and then adaptively uses the learned latent distributions to re-weight the importance of certain data points while training. While our method is generalizable across various data modalities and learning tasks, in this work we use our algorithm to address the issue of racial and gender bias in facial detection systems. We evaluate our algorithm on the Pilot Parliaments Benchmark (PPB), a dataset specifically designed to evaluate biases in computer vision systems, and demonstrate increased overall performance as well as decreased categorical bias with our debiasing approach.