The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some notion of fairness among specified item groups. While fairness has recently been considered in the context of learning-to-rank systems, current methods cannot provide guarantees on the fairness of the predicted rankings. This paper addresses this gap and introduces Smart Predict and Optimize for Fair Ranking (SPOFR), an integrated optimization and learning framework for fairness-constrained learning to rank. The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints, while allowing for fine control of the fairness-utility tradeoff. SPOFR is shown to significantly improve on current state-of-the-art fair learning-to-rank systems with respect to established performance metrics.
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Policy Learning for Fairness in Ranking
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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- Award ID(s):
- 1901168
- NSF-PAR ID:
- 10170903
- Date Published:
- Journal Name:
- Conference on Neural Information Processing Systems (NeurIPS 2019)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
