Controlling Fairness and Bias in Dynamic Learning-to-Rank

TL;DR

This paper introduces a dynamic learning-to-rank algorithm that enforces fairness among item groups while optimizing utility, balancing fairness and relevance in online ranking systems.

Contribution

It proposes a novel, theoretically grounded algorithm that ensures group fairness in rankings and adapts dynamically based on implicit feedback.

Findings

The algorithm guarantees fairness and utility convergence.

Empirical results show robustness and practicality.

The method outperforms existing ranking approaches in fairness metrics.

Abstract

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.