  We develop an algorithm to train individually fair learning-to-rank (LTR)
models. The proposed approach ensures items from minority groups appear
alongside similar items from majority groups. This notion of fair ranking is
based on the definition of individual fairness from supervised learning and is
more nuanced than prior fair LTR approaches that simply ensure the ranking
model provides underrepresented items with a basic level of exposure. The crux
of our method is an optimal transport-based regularizer that enforces
individual fairness and an efficient algorithm for optimizing the regularizer.
We show that our approach leads to certifiably individually fair LTR models and
demonstrate the efficacy of our method on ranking tasks subject to demographic
biases.
