Fairness of Exposure in Rankings

TL;DR

This paper introduces a framework for ensuring fairness in ranking systems by applying exposure-based fairness constraints, with algorithms that balance utility and fairness, demonstrated through empirical experiments.

Contribution

It proposes a flexible framework for fairness constraints in rankings and develops efficient algorithms to optimize utility while satisfying these fairness criteria.

Findings

Framework can implement various fairness constraints including demographic parity and disparate impact.

Algorithms efficiently find rankings that balance utility and fairness.

Empirical results demonstrate the effectiveness of the proposed approach.

Abstract

Rankings are ubiquitous in the online world today. As we have transitioned from finding books in libraries to ranking products, jobs, job applicants, opinions and potential romantic partners, there is a substantial precedent that ranking systems have a responsibility not only to their users but also to the items being ranked. To address these often conflicting responsibilities, we propose a conceptual and computational framework that allows the formulation of fairness constraints on rankings in terms of exposure allocation. As part of this framework, we develop efficient algorithms for finding rankings that maximize the utility for the user while provably satisfying a specifiable notion of fairness. Since fairness goals can be application specific, we show how a broad range of fairness constraints can be implemented using our framework, including forms of demographic parity, disparate treatment, and disparate impact constraints. We illustrate the effect of these constraints by providing empirical results on two ranking problems.