The Representative Individuals Approach to Fair Machine Learning

Michele Loi

Abstract: This paper develops a philosophical account of probabilistic fairness claims in machine learning. It argues that fairness metrics can be interpreted through “representative individuals”: hypothetical persons standing for socially relevant roles. The framework explains why group-level statistical measures can matter morally while preserving attention to individual complaints and principled choices about relevant social categories.