Overview
Syllabus
Quantifying bias in machine decisions
Summary
Pretrial detention A detailed case study
Key assumptions
From features to decisions
Risk distributions
From risk to decisions Threshold rules
A double standard
Fairness of a single threshold
Popular mathematical definitions of fairness
Discrimination with calibrated scores
Classification parity
False positive rate parity
Error rate disparities in Broward County
Calculating false positive rates
Infra-marginality
The problem with false positive rates
Are the data biased?
Biased labels
Biased predictors
Taught by
Simons Institute