Event Date
SPEAKER: Bradley Rava, PhD Candidate, University of Southern California
TITLE: “A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification”
ABSTRACT: We study fairness in classification, where one wishes to make automated decisions for people from different protected groups. When individuals are classified, the decision errors can be unfairly concentrated in certain protected groups. We develop a fairness-adjusted selective inference (FASI) framework and data-driven algorithms that achieve statistical parity in the sense that the false selection rate (FSR) is controlled and equalized among protected groups. The FASI algorithm operates by converting the outputs from black-box classifiers to R-values, which are intuitively appealing and easy to compute. Selection rules based on R-values are provably valid for FSR control, and avoid disparate impacts on protected groups. The effectiveness of FASI is demonstrated through both simulated and real data.
Link: https://arxiv.org/abs/2110.05720
This seminar will be delivered remotely via Zoom. To access the Zoom meeting for this seminar, please contact Pete Scully ([email protected]) for the meeting ID and password, stating your affiliation.