Title: On Formalizing Fairness in Prediction with Machine Learning,
Abstract: Machine learning algorithms for prediction are increasingly being used in
critical decisions affecting human lives. Various fairness formalizations, with
no firm consensus yet, are employed to prevent such algorithms from
systematically discriminating against people based on certain attributes
protected by law. The aim of this article is to survey how fairness is
formalized in the machine learning literature for the task of prediction and
present these formalizations with their corresponding notions of distributive
justice from the social sciences literature. We provide theoretical as well as
empirical critiques of these notions from the social sciences literature and
explain how these critiques limit the suitability of the corresponding fairness
formalizations to certain domains. We also suggest two notions of distributive
justice which address some of these critiques and discuss avenues for
prospective fairness formalizations.