We used reliable public data from 1970 to 2020 to curate attributes like prior criminal counts and recidivism outcomes. The dataset contains large number of samples from five racial groups, in addition to information like sex and age (at judgment and first offense).

As the state-of-the-art framework, i.e., RecEraser, naturally achieves full unlearning completeness,

Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.