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.

本研究探讨了公众对医疗推荐中公平性的理解，填补了现有研究空白。通过调查四种公平性指标的认知，揭示公众在算法公平性方面的理解普遍偏低，强调了对公平性教育和信息传递的必要性。研究结果表明，一刀切的公平性方法可能不够有效，提出需要根据具体情境设计公平的AI系统。

理解推荐系统中的公平性：医疗健康视角