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Understanding Fairness in Recommender Systems: A Healthcare Perspective

Published: 08 October 2024 Publication History

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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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 08 October 2024

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Author Tags

  1. algorithmic fairness
  2. decision-making
  3. demographic parity
  4. equal accuracy
  5. equalized odds
  6. ethical artificial intelligence
  7. fairness
  8. healthcare
  9. positive predicted value
  10. understanding

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