Policy Learning for Fairness in Ranking
release_gc7tdm5mtjhgfhtu6hc6hg7l6y
by
Ashudeep Singh, Thorsten Joachims
2019
Abstract
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.
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