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Fair representation learning transforms user data into a representation thatensures fairness and utility regardless of the downstream application. However,learning individually fair representations, i.e., guaranteeing that similarindividuals are treated similarly, remains challenging in high-dimensionalsettings such as computer vision. In this work, we introduce LASSI, the firstrepresentation learning method for certifying individual fairness ofhigh-dimensional data. Our key insight is to leverage recent advances ingenerative modeling to capture the set of similar individuals in the generativelatent space. This enables us to learn individually fair representations thatmap similar individuals close together by using adversarial training tominimize the distance between their representations. Finally, we employrandomized smoothing to provably map similar individuals close together, inturn ensuring that local robustness verification of the downstream applicationresults in end-to-end fairness certification. Our experimental evaluation onchallenging real-world image data demonstrates that our method increasescertified individual fairness by up to 90% without significantly affecting taskutility.

Latent Space Smoothing for Individually Fair Representations