  The fairness of AI decision-making has garnered increasing attention, leading
to the proposal of numerous fairness algorithms. In this paper, we aim not to
address this issue by directly introducing fair learning algorithms, but rather
by generating entirely new, fair synthetic data from biased datasets for use in
any downstream tasks. Additionally, the distribution of test data may differ
from that of the training set, potentially impacting the performance of the
generated synthetic data in downstream tasks. To address these two challenges,
we propose a diffusion model-based framework, FADM: Fairness-Aware Diffusion
with Meta-training. FADM introduces two types of gradient induction during the
sampling phase of the diffusion model: one to ensure that the generated samples
belong to the desired target categories, and another to make the sensitive
attributes of the generated samples difficult to classify into any specific
sensitive attribute category. To overcome data distribution shifts in the test
environment, we train the diffusion model and the two classifiers used for
induction within a meta-learning framework. Compared to other baselines, FADM
allows for flexible control over the categories of the generated samples and
exhibits superior generalization capability. Experiments on real datasets
demonstrate that FADM achieves better accuracy and optimal fairness in
downstream tasks.
