Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and
  Ex-Post Fairness
Sruthi Gorantla Eshaan Bhansali Amit Deshpande Anand Louis
In learning-to-rank (LTR), optimizing only the relevance (or the expected
ranking utility) can cause representational harm to certain categories of
items. Moreover, if there is implicit bias in the relevance scores, LTR models
may fail to optimize for true relevance. Previous works have proposed efficient
algorithms to train stochastic ranking models that achieve fairness of exposure
to the groups ex-ante (or, in expectation), which may not guarantee
representation fairness to the groups ex-post, that is, after realizing a
ranking from the stochastic ranking model. Typically, ex-post fairness is
achieved by post-processing, but previous work does not train stochastic
ranking models that are aware of this post-processing.
  In this paper, we propose a novel objective that maximizes expected relevance
only over those rankings that satisfy given representation constraints to
ensure ex-post fairness. Building upon recent work on an efficient sampler for
ex-post group-fair rankings, we propose a group-fair Plackett-Luce model and
show that it can be efficiently optimized for our objective in the LTR
framework.
  Experiments on three real-world datasets show that our group-fair algorithm
guarantees fairness alongside usually having better relevance compared to the
LTR baselines. In addition, our algorithm also achieves better relevance than
post-processing baselines, which also ensures ex-post fairness. Further, when
implicit bias is injected into the training data, our algorithm typically
outperforms existing LTR baselines in relevance.