Normative Principles for Evaluating Fairness in Machine Learning

There are many incompatible ways to measure fair outcomes for machine learning algorithms. The goal of this paper is to characterize rates of success and error across protected groups (race, gender, sexual orientation) as a distribution problem, and describe the possible solutions to this problem according to different normative principles from moral and political philosophy. These normative principles are based on various competing attributes within a distribution problem: intentions, compensation, desert, consent, and consequences. Each principle will be applied to a sample risk-assessment classifier to demonstrate the philosophical arguments underlying different sets of fairness metrics.