  Equal Opportunity and Fairness are receiving increasing attention in
artificial intelligence. Stereotyping is another source of discrimination,
which yet has been unstudied in literature. GAN-made faces would be exposed to
such discrimination, if they are classified by human perception. It is possible
to eliminate the human impact on fictitious faces classification task by the
use of statistical approaches. We present a novel approach through penalized
regression to label stereotype-free GAN-generated synthetic unlabeled images.
The proposed approach aids labeling new data (fictitious output images) by
minimizing a penalized version of the least squares cost function between
realistic pictures and target pictures.
