  Deep learning (DL) models are widely used to provide a more convenient and
smarter life. However, biased algorithms will negatively influence us. For
instance, groups targeted by biased algorithms will feel unfairly treated and
even fearful of negative consequences of these biases. This work targets biased
generative models' behaviors, identifying the cause of the biases and
eliminating them. We can (as expected) conclude that biased data causes biased
predictions of face frontalization models. Varying the proportions of male and
female faces in the training data can have a substantial effect on behavior on
the test data: we found that the seemingly obvious choice of 50:50 proportions
was not the best for this dataset to reduce biased behavior on female faces,
which was 71% unbiased as compared to our top unbiased rate of 84%. Failure in
generation and generating incorrect gender faces are two behaviors of these
models. In addition, only some layers in face frontalization models are
vulnerable to biased datasets. Optimizing the skip-connections of the generator
in face frontalization models can make models less biased. We conclude that it
is likely to be impossible to eliminate all training bias without an unlimited
size dataset, and our experiments show that the bias can be reduced and
quantified. We believe the next best to a perfect unbiased predictor is one
that has minimized the remaining known bias.
