Understanding Fairness in Recommender Systems: A Healthcare Perspective
This addresses the problem of public awareness and education on algorithmic fairness for users of healthcare AI systems, but it is incremental as it focuses on comprehension rather than proposing new methods.
The paper investigated public understanding of fairness metrics in healthcare recommender systems through a survey, finding that fairness is complex and often misunderstood, with generally low comprehension levels.
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.