Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness

Sruthi Gorantla, Eshaan Bhansali, Amit Deshpande, Anand Louis

Keywords: ex-post fairness, ranking, Plackett-Luce, in-processing

TL;DR: We propose an algorithm to efficiently estimate gradients of our Group-Fair Plackett-Luce model that samples ex-post fair rankings.

Abstract:

In learning-to-rank (LTR), optimizing only the relevance (or the expected ranking utility) can cause representational harm to certain categories of items. 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 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 ensure ex-post fairness. Further, when implicit bias is injected into the training data, our algorithm typically outperforms existing LTR baselines in relevance.

Submission Number: 72