The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default by Brent Mittelstadt, Sandra Wachter, Chris Russell :: SSRN

In recent years fairness in machine learning (ML), artificial intelligence (AI), and algorithmic decision-making systems has emerged as a highly active area of research and development. To date, the majority of measures and methods to mitigate bias and improve fairness in algorithmic systems have been built in isolation from policy and civil societal contexts and lack serious engagement with philosophical, political, legal, and economic theories of equality and distributive justice. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. This oversimplification of equality through fairness measures is troubling. Many current fairness measures suffer from both fairness and performance degradation, or "levelling down," where fairness is achieved by making every group worse off, or by bringing better performing groups down to the level of the worst off.