Overview
Syllabus
Intro
How do we identify bias in algorithmic decisions?
Case study: Pre-trial decision making
Problems with the benchmark test
The outcome test in Broward County
Risk distributions
The problem with the outcome test
The problem of infra-marginality
Identifying bias in human decisions
Making decisions with algorithms
Evidence from Broward County
Potential fairness concerns
Redlining
Why is calibration insufficient?
Sample bias
Label bias
Subgroup validity
Use of protected characteristics
Statistical parity as a measure of fairness
Where do these disparities come from?
The optimal rule is a single threshold
The fairness/fairness trade-off
Analogies to tests for discrimination
The problem with false positive rates
Making fair decisions with algorithms
Limitations
Taught by
Simons Institute