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.

通过使用扩散模型为基础的框架 FADM，本文提出了一种从存在偏见的数据集中生成全新的、公平的合成数据，而不是直接引入公平学习算法来解决人工智能决策公正性问题。实验证明，FADM 在下游任务中表现出更好的准确性和最佳的公平性，同时允许对生成样本的类别进行灵活控制。

基于评分的扩散模型的公平数据生成

Fair Data Generation via Score-based Diffusion Model

As Artificial Intelligence (AI) is used in more applications, the need to
consider and mitigate biases from the learned models has followed. Most works
in developing fair learning algorithms focus on the offline setting. However,
in many real-world applications data comes in an online fashion and needs to be
processed on the fly. Moreover, in practical application, there is a trade-off
between accuracy and fairness that needs to be accounted for, but current
methods often have multiple hyperparameters with non-trivial interaction to
achieve fairness. In this paper, we propose a flexible ensemble algorithm for
fair decision-making in the more challenging context of evolving online
settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is
based on using online component classifiers and updating them according to the
current distribution, that also accounts for fairness and a single
hyperparameters that alters fairness-accuracy balance. Experiments on
real-world discriminated data streams demonstrate the utility of FARF.

该研究提出了一种灵活的基于集成学习的算法 FARF，在演化的在线设置中实现公平决策。