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Area of Science:

  • Computer Science
  • Statistics
  • Machine Learning

Background:

  • Survival analysis is crucial for predicting event occurrence over time, especially with censored data.
  • Algorithmic fairness has advanced significantly, yet fairness in survival analysis remains under-explored.
  • Existing methods lack robust approaches for ensuring fairness in time-to-event predictions.

Purpose of the Study:

  • To propose a novel framework for achieving demographic parity in survival analysis.
  • To minimize the mutual information between predicted time-to-event and sensitive attributes.
  • To develop new disparity assessment metrics for survival predictions.

Main Methods:

  • Developed a framework to minimize mutual information between survival predictions and sensitive attributes.
  • Implemented techniques to ensure statistical independence of time-to-event predictions.
  • Proposed four novel disparity assessment metrics tailored for survival analysis.

Main Results:

  • The proposed method effectively minimizes mutual information, promoting fairness.
  • Experiments demonstrate systematic improvement in the fairness of survival predictions.
  • The approach is robust and performs well even with censored data across benchmark datasets.

Conclusions:

  • The introduced framework successfully enhances fairness in survival analysis models.
  • Minimizing dependence on sensitive attributes leads to more equitable time-to-event predictions.
  • This work provides a valuable tool for developing fair and reliable survival analysis systems.