  Recent advancements in GANs and diffusion models have enabled the creation of
high-resolution, hyper-realistic images. However, these models may misrepresent
certain social groups and present bias. Understanding bias in these models
remains an important research question, especially for tasks that support
critical decision-making and could affect minorities. The contribution of this
work is a novel analysis covering architectures and embedding spaces for
fine-grained understanding of bias over three approaches: generators, attribute
modifier, and post-processing bias mitigators. This work shows that generators
suffer from bias across all social groups with attribute preferences such as
between 75%-85% for whiteness and 60%-80% for the female gender (for all
trained CelebA models) and low probabilities of generating children and older
men. Modifier and mitigators work as post-processor and change the generator
performance. For instance, attribute channel perturbation strategies modify the
embedding spaces. We quantify the influence of this change on group fairness by
measuring the impact on image quality and group features. Specifically, we use
the Fr\'echet Inception Distance (FID), the Face Matching Error and the
Self-Similarity score. For Interfacegan, we analyze one and two attribute
channel perturbations and examine the effect on the fairness distribution and
the quality of the image. Finally, we analyzed the post-processing bias
mitigators, which are the fastest and most computationally efficient way to
mitigate bias. We find that these mitigation techniques show similar results on
KL divergence and FID score, however, self-similarity scores show a different
feature concentration on the new groups of the data distribution. The
weaknesses and ongoing challenges described in this work must be considered in
the pursuit of creating fair and unbiased face generation models.
