Policy Learning for Fairness in Ranking
It addresses fairness issues in ranking applications such as online marketplaces and job placement, which is an incremental improvement over conventional LTR methods.
The paper tackles the problem of fairness in Learning-to-Rank (LTR) by proposing a framework that optimizes utility metrics like NDCG while satisfying fairness constraints for items, with empirical results on simulated and real-world datasets showing its effectiveness.
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job placement, admissions). To address this need, we propose a general LTR framework that can optimize a wide range of utility metrics (e.g. NDCG) while satisfying fairness of exposure constraints with respect to the items. This framework expands the class of learnable ranking functions to stochastic ranking policies, which provides a language for rigorously expressing fairness specifications. Furthermore, we provide a new LTR algorithm called Fair-PG-Rank for directly searching the space of fair ranking policies via a policy-gradient approach. Beyond the theoretical evidence in deriving the framework and the algorithm, we provide empirical results on simulated and real-world datasets verifying the effectiveness of the approach in individual and group-fairness settings.