Generative AI models have recently achieved astonishing results in quality
and are consequently employed in a fast-growing number of applications.
However, since they are highly data-driven, relying on billion-sized datasets
randomly scraped from the internet, they also suffer from degenerated and
biased human behavior, as we demonstrate. In fact, they may even reinforce such
biases. To not only uncover but also combat these undesired effects, we present
a novel strategy, called Fair Diffusion, to attenuate biases after the
deployment of generative text-to-image models. Specifically, we demonstrate
shifting a bias, based on human instructions, in any direction yielding
arbitrarily new proportions for, e.g., identity groups. As our empirical
evaluation demonstrates, this introduced control enables instructing generative
image models on fairness, with no data filtering and additional training
required.

通过一项称为公平扩散的新策略，无需数据筛选和额外培训，即可在没有任何偏见的情况下指导生成模型的公平性和防止其加重偏见。