@inproceedings{
zhou2024conformal,
title={Conformal Classification with Equalized Coverage for Adaptively Selected Groups},
author={Yanfei Zhou and Matteo Sesia},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3pWHKxK1sC}
}
This paper introduces a conformal inference method to evaluate uncertainty in classification by generating prediction sets with valid coverage conditional on adaptively chosen features. These features are carefully selected to reflect potential model limitations or biases. This can be useful to find a practical compromise between efficiency---by providing informative predictions---and algorithmic fairness---by ensuring equalized coverage for the most sensitive groups. We demonstrate the validity and effectiveness of this method on simulated and real data sets.
Conformal Classification with Equalized Coverage for Adaptively Selected Groups
Yanfei Zhou Matteo Sesia