Understanding Fairness in Recommender Systems: A Healthcare Perspective

• URL: http://arxiv.org/abs/2409.03893v2
• Date: Mon, 9 Sep 2024 07:47:58 GMT
• Title: Understanding Fairness in Recommender Systems: A Healthcare Perspective
• Authors: Veronica Kecki, Alan Said,
• Abstract summary: This paper explores the public's comprehension of fairness in healthcare recommendations. We conducted a survey where participants selected from four fairness metrics. Results suggest that a one-size-fits-all approach to fairness may be insufficient.
• Score: 0.18416014644193066
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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