Dimensions of Diversity in Human Perceptions of Algorithmic Fairness

TL;DR

This study investigates how sociodemographic factors and personal experiences influence perceptions of algorithmic fairness, emphasizing the importance of diversity considerations in oversight and regulation.

Contribution

It provides empirical insights into how individual differences affect fairness perceptions, highlighting the need for diverse stakeholder engagement in algorithmic oversight.

Findings

Political views significantly influence fairness perceptions.

Personal experience with algorithms affects feature fairness judgments.

Diversity in oversight bodies can improve algorithmic governance.

Abstract

A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people's lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of how individual factors like sociodemographics or direct experience with a decision-making scenario may affect their ethical views. We take a step toward filling this gap by exploring how people's perceptions of one aspect of procedural algorithmic fairness (the fairness of using particular features in an algorithmic decision) relate to their (i) demographics (age, education, gender, race, political views) and (ii) personal experiences with the algorithmic decision-making scenario. We find that political views and personal experience with the algorithmic decision context significantly influence perceptions about the fairness of using different features for bail decision-making. Drawing on our results, we discuss the implications for stakeholder engagement and algorithmic oversight including the need to consider multiple dimensions of diversity in composing oversight and regulatory bodies.