Mitigating Algorithmic Bias on Facial Expression Recognition
Glauco Amigo Pablo Rivas Perea Robert J. Marks
Biased datasets are ubiquitous and present a challenge for machine learning.
For a number of categories on a dataset that are equally important but some are
sparse and others are common, the learning algorithms will favor the ones with
more presence. The problem of biased datasets is especially sensitive when
dealing with minority people groups. How can we, from biased data, generate
algorithms that treat every person equally? This work explores one way to
mitigate bias using a debiasing variational autoencoder with experiments on
facial expression recognition.