  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.
