Fairness in Rankings and Recommendations: An Overview

TL;DR

This paper provides a comprehensive overview of fairness in ranking and recommendation systems, including definitions, models, methods, challenges, and future research directions in this rapidly evolving domain.

Contribution

It offers a structured framework and synthesis of current methods for ensuring fairness in ranking and recommendation systems, highlighting open challenges.

Findings

Provides a toolkit of definitions, models, and methods for fairness

Highlights open challenges and future research directions

Synthesizes current approaches in a rapidly evolving field

Abstract

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this work, we aim at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are three-fold: (a) to provide a solid framework on a novel, quickly evolving, and impactful domain, (b) to present related methods and put them into perspective, and (c) to highlight open challenges and research paths for future work.