On conditional parity as a notion of non-discrimination in machine learning

Ruofei Zhao; Ya'acov Ritov; Yuekai Sun

arxiv: 1706.08519 · v1 · pith:BLJIUWTNnew · submitted 2017-06-26 · 📊 stat.ML · cs.CY· cs.LG

On conditional parity as a notion of non-discrimination in machine learning

Ya'acov Ritov , Yuekai Sun , Ruofei Zhao This is my paper

classification 📊 stat.ML cs.CYcs.LG

keywords conditionalparitynon-discriminationgenerallearningmachinenotionnotions

0 comments

read the original abstract

We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying randomization as a general mechanism for achieving conditional parity and a kernel-based test of conditional parity.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning
cs.LG 2026-05 unverdicted novelty 6.0

Introduces a fairness layer for deep learning models that guarantees output parity and an online primal-dual algorithm for aggregate fairness guarantees in streaming predictions with small batch sizes.