Exploring User Opinions of Fairness in Recommender Systems

TL;DR

This paper investigates user perceptions of fairness in recommender systems, aiming to understand diverse opinions to guide the development of more equitable and transparent algorithms.

Contribution

It provides an initial exploration of user opinions on fairness in recommendation systems, highlighting factors influencing perceptions and potential design considerations.

Findings

Users have varied ideas of fairness in recommendations.

Discrepancies in fairness perceptions can impact system acceptance.

Understanding user views can inform fairer algorithm design.

Abstract

Algorithmic fairness for artificial intelligence has become increasingly relevant as these systems become more pervasive in society. One realm of AI, recommender systems, presents unique challenges for fairness due to trade offs between optimizing accuracy for users and fairness to providers. But what is fair in the context of recommendation--particularly when there are multiple stakeholders? In an initial exploration of this problem, we ask users what their ideas of fair treatment in recommendation might be, and why. We analyze what might cause discrepancies or changes between user's opinions towards fairness to eventually help inform the design of fairer and more transparent recommendation algorithms.