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

AI is anticipated to enhance human decision-making in high-stakes domains
like aviation, but adoption is often hindered by challenges such as
inappropriate reliance and poor alignment with users' decision-making. Recent
research suggests that a core underlying issue is the recommendation-centric
design of many AI systems, i.e., they give end-to-end recommendations and
ignore the rest of the decision-making process. Alternative support paradigms
are rare, and it remains unclear how the few that do exist compare to
recommendation-centric support. In this work, we aimed to empirically compare
recommendation-centric support to an alternative paradigm, continuous support,
in the context of diversions in aviation. We conducted a mixed-methods study
with 32 professional pilots in a realistic setting. To ensure the quality of
our study scenarios, we conducted a focus group with four additional pilots
prior to the study. We found that continuous support can support pilots'
decision-making in a forward direction, allowing them to think more beyond the
limits of the system and make faster decisions when combined with
recommendations, though the forward support can be disrupted. Participants'
statements further suggest a shift in design goal away from providing
recommendations, to supporting quick information gathering. Our results show
ways to design more helpful and effective AI decision support that goes beyond
end-to-end recommendations.

AI 决策支持在航空等高风险领域有望提高人类决策能力，但由于过度依赖和与用户决策不一致等挑战，其应用常受限制。本研究旨在比较推荐为中心的支持与连续支持这两种不同范式在航空改航决策中的效果，考察结果表明连续支持结合推荐有助于飞行员向前思考超出系统限制的更多可能性，提供更快速的决策，而前向支持可能会受到干扰。参与者的陈述进一步建议将设计目标转移到支持快速信息获取，为超越全流程推荐的更有帮助和有效的 AI 决策支持提供了设计思路。