Fair representation learning transforms user data into a representation that ensures 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 enables us to learn individually fair representations that map similar individuals 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 90% without significantly affecting task utility.
翻译:公平代表性学习将用户数据转换成一种代表形式,确保公平性和实用性,而不管下游应用如何。然而,学习个人公平代表性,即保证类似个人得到同等待遇,在计算机愿景等高维环境中,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。我们的关键洞察力是利用基因化潜质空间中基因化模型的最新进展,以捕捉相近个人的一组。这使我们能够学习个人公平代表性,通过使用对抗性培训,将相似的个人相近相近,将彼此之间的距离缩小到最小。最后,我们采用随机性平滑来随机性地绘制相近的个人相近的分布图,进而确保下游应用的当地稳健性核查结果在端对端公平认证中产生结果。我们对挑战性真实世界图像数据的实验性评估表明,我们的方法在不显著影响任务效用的情况下,将个人公平性提升到90%。