Understanding Fairness in Recommender Systems: A Healthcare Perspective

TL;DR

This study assesses public understanding of fairness metrics in healthcare recommender systems, revealing widespread misconceptions and emphasizing the need for better education and context-aware fairness approaches.

Contribution

It provides empirical insights into public perceptions of fairness in healthcare AI, highlighting gaps in understanding and the importance of context-sensitive fairness designs.

Findings

Low public understanding of fairness metrics

Need for improved education on algorithmic fairness

Context-sensitive fairness approaches are crucial

Abstract

Fairness in AI-driven decision-making systems has become a critical concern, especially when these systems directly affect human lives. This paper explores the public's comprehension of fairness in healthcare recommendations. We conducted a survey where participants selected from four fairness metrics -- Demographic Parity, Equal Accuracy, Equalized Odds, and Positive Predictive Value -- across different healthcare scenarios to assess their understanding of these concepts. Our findings reveal that fairness is a complex and often misunderstood concept, with a generally low level of public understanding regarding fairness metrics in recommender systems. This study highlights the need for enhanced information and education on algorithmic fairness to support informed decision-making in using these systems. Furthermore, the results suggest that a one-size-fits-all approach to fairness may be insufficient, pointing to the importance of context-sensitive designs in developing equitable AI systems.