Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness
Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness
Abstract As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular “fairness measures” are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a …