Understanding Fairness in Recommender Systems: A Healthcare Perspective
Veronica Kecki, Alan Said
TL;DR
This study investigates how the public understands fairness in healthcare recommender systems by presenting participants with two health-related scenarios and a choice among four fairness metrics: Demographic Parity, Equal Accuracy, Equalized Odds, and Positive Predictive Value. Using an online survey with 131 responses, the authors reveal that public understanding of fairness is limited and strongly influenced by context, with participants favoring accuracy over equality in both high-stakes and low-stakes settings. The findings underscore the challenge of selecting a universal fairness criterion and highlight the need for transparent communication, education, and context-sensitive or adaptive fairness designs. Practically, the work suggests engaging users and designing flexible fairness frameworks to support informed decision-making and trust in AI-driven healthcare recommendations.
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.