Hsiang Hsu

To Split or Not to Split: The Impact of Disparate Treatment in Classification

2020 · arXiv (Cornell University)

Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute (e.g., age, sex). In domains where prediction accuracy is paramount, it could potentially be acceptable to fit …

To Split or not to Split: The Impact of Disparate Treatment in Classification

2021 · IEEE Transactions on Information Theory

Disparate treatment occurs when a machine learning model produces different decisions for individuals based on a legally protected or sensitive attribute (e.g., age, sex). In domains where prediction accuracy is paramount, it could potentially be …

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

2023

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Gaussian noise to clipped …