Authors: Mukund Telukunta, Venkata Sriram Siddhardh Nadendla, Morgan Stuart, Casey Canfield
Year: 2025
Published in: ArXiv
Institution: Missouri University of Science and Technology, United Network for Organ Sharing
Research Area: Algorithmic Fairness, Healthcare AI, Decision Making
Discipline: Artificial Intelligence
The study investigates fairness in regression-based predictive models for kidney transplantation, introducing three group fairness notions and identifying social preferences for fairness criteria, revealing biases against age groups but fairness towards gender and race groups.
Methods: Three novel fairness notions (independence, separation, sufficiency) were introduced alongside crowd feedback analysis through a Mixed-Logit discrete choice model.
Key Findings: Fairness in regression-based predictive analytics regarding group fairness criteria across social dimensions such as age, gender, and race.
Sample Size: 85
