Policy-Gradient Training of Fair and Unbiased Ranking Functions

TL;DR

This paper introduces FULTR, a policy-gradient method that learns fair and unbiased ranking functions from biased implicit feedback by enforcing customizable fairness constraints and utilizing counterfactual estimators.

Contribution

It presents the first approach to address both presentation bias and fairness of exposure simultaneously using a flexible, counterfactual policy-gradient algorithm.

Findings

Successfully learns fair ranking policies from biased data.

Demonstrates effectiveness of counterfactual estimators in fairness constraints.

Achieves accurate and fair rankings in empirical evaluations.

Abstract

While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogenous and endogenous reasons. Exogenous reasons typically manifest themselves as biases in the training data, which then get reflected in the learned ranking policy and often lead to rich-get-richer dynamics. Moreover, even after the correction of such biases, reasons endogenous to the design of the learning algorithm can still lead to ranking policies that do not allocate exposure among items in a fair way. To address both exogenous and endogenous sources of unfairness, we present the first learning-to-rank approach that addresses both presentation bias and merit-based fairness of exposure simultaneously. Specifically, we define a class of amortized fairness-of-exposure constraints that can be chosen based on the needs of an application, and we show how these fairness criteria can be enforced despite the selection biases in implicit feedback data. The key result is an efficient and flexible policy-gradient algorithm, called FULTR, which is the first to enable the use of counterfactual estimators for both utility estimation and fairness constraints. Beyond the theoretical justification of the framework, we show empirically that the proposed algorithm can learn accurate and fair ranking policies from biased and noisy feedback.