Fair representation learning encodes user data to ensure fairness and utility, regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the generative latent space. This allows learning individually fair representations where similar individuals are mapped close together, by using adversarial training to minimize the distance between their representations. Finally, we employ randomized smoothing to provably map similar individuals close together, in turn ensuring that local robustness verification of the downstream application results in end-to-end fairness certification. Our experimental evaluation on challenging real-world image data demonstrates that our method increases certified individual fairness by up to 60%, without significantly affecting task utility.
翻译:公平代表性学习将用户数据编码,以确保公平性和实用性,而不论下游应用程序如何。然而,在计算机愿景等高维环境中,学习个人公平代表性,即保证类似个人得到同等待遇,仍然具有挑战性。在这项工作中,我们引入了LASSI,这是证明高维数据个人公平性的第一个代表学习方法。我们的关键见解是利用基因化潜质空间中基因化模型的最新进展,捕捉相似个人组。这可以学习相近个人相近的个体公平代表性,方法是利用对抗性培训,尽量减少其代表性之间的距离。最后,我们随机地平稳地绘制类似个人相近的分布图,进而确保下游应用的当地稳健性核查在端至端公平认证中的结果。我们对挑战真实世界图像数据的实验性评估表明,我们的方法将个人公平性提高高达60%,但不会显著影响任务效用。