一句话：提出框架 EIDIG，用全局种子生成加梯度引导的局部搜索，高效发现深度学习模型的个体歧视实例。
深度学习系统广泛用于自动决策，公平性是其重要属性。为评估与提升模型的个体公平性，需在输入空间中系统生成测试用例以识别个体歧视样本。本文提出白盒测试框架 EIDIG：全局生成阶段快速产生一组多样化的歧视种子，局部生成阶段则在模型输出梯度引导下围绕这些种子生成尽可能多的个体歧视实例；两个阶段均充分利用迭代中的先验信息，以加速优化收敛或减少梯度计算。实验表明，与当前最优方法相比，EIDIG 平均多生成 19.11% 的个体歧视实例，速度提升 121.49%；重训练后个体歧视减少 80.03%，且精度损失有限。
Deep learning (DL) systems are increasingly deployed for autonomous decision-making in a wide range of applications. Apart from the robustness and safety, fairness is also an important property that a well-designed DL system should have. To evaluate and improve individual fairness of a model, systematic test case generation for identifying individual discriminatory instances in the input space is essential. In this paper, we propose a framework EIDIG for efficiently discovering individual fairness violation. Our technique combines a global generation phase for rapidly generating a set of diverse discriminatory seeds with a local generation phase for generating as many individual discriminatory instances as possible around these seeds under the guidance of the gradient of the model output. In each phase, prior information at successive iterations is fully exploited to accelerate convergence of iterative optimization or reduce frequency of gradient calculation. Our experimental results show that, on average, our approach EIDIG generates 19.11% more individual discriminatory instances with a speedup of 121.49% when compared with the state-of-the-art method and mitigates individual discrimination by 80.03% with a limited accuracy loss after retraining.