Machine learning (ML) systems have achieved remarkable performance across a wide area of applications. However, they frequently exhibit unfair behaviors in sensitive application domains, raising severe fairness concerns. To evaluate and test fairness, engineers often generate individual discriminatory instances to expose unfair behaviors before model deployment. However, existing baselines ignore the naturalness of generation and produce instances that deviate from the real data distribution, which may fail to reveal the actual model fairness since these unnatural discriminatory instances are unlikely to appear in practice. To address the problem, this paper proposes a framework named Latent Imitator (LIMI) to generate more natural individual discriminatory instances with the help of a generative adversarial network (GAN), where we imitate the decision boundary of the target model in the semantic latent space of GAN and further samples latent instances on it. Specifically, we first derive a surrogate linear boundary to coarsely approximate the decision boundary of the target model, which reflects the nature of the original data distribution. Subsequently, to obtain more natural instances, we manipulate random latent vectors to the surrogate boundary with a one-step movement, and further conduct vector calculation to probe two potential discriminatory candidates that may be more closely located in the real decision boundary. Extensive experiments on various datasets demonstrate that our LIMI outperforms other baselines largely in effectiveness ($\times$9.42 instances), efficiency ($\times$8.71 speeds), and naturalness (+19.65%) on average. In addition, we empirically demonstrate that retraining on test samples generated by our approach can lead to improvements in both individual fairness (45.67% on $IF_r$ and 32.81% on $IF_o$) and group fairness (9.86% on $SPD$ and 28.38% on $AOD$}).

翻译：机器学习系统在广泛的应用领域取得了卓越的性能，然而在敏感应用场景中频繁展现出不公平行为，引发了严重的公平性担忧。为评估和测试公平性，工程师通常在模型部署前生成个体歧视实例以暴露不公平行为。然而现有基线方法忽略生成实例的自然性，生成的实例偏离真实数据分布，由于这些非自然歧视实例在实践中几乎不会出现，因此可能无法揭示实际模型公平性。针对该问题，本文提出名为"潜在模仿者"（LIMI）的框架，借助生成对抗网络（GAN）生成更自然的个体歧视实例：我们在GAN的语义潜在空间中模仿目标模型的决策边界，并在此基础上采样潜在实例。具体而言，首先推导出替代线性边界以粗略逼近目标模型的决策边界，该边界反映了原始数据分布的本质。随后为获取更自然的实例，采用单步移动将随机潜在向量操控至替代边界，并通过向量计算探测两个可能更贴近真实决策边界的潜在歧视候选实例。多数据集上的大量实验表明，LIMI在有效性（平均×9.42个实例）、效率（平均×8.71倍速度）和自然性（平均+19.65%）方面远超其他基线。此外，实证证明采用本方法生成的测试样本进行再训练可同时提升个体公平性（$IF_r$提升45.67%，$IF_o$提升32.81%）和群体公平性（$SPD$提升9.86%，$AOD$提升28.38%）。