What Is New, and What Is Old, in Fairness and Machine Learning

About This Event

The aim of this talk will be to explore the question of normative distinctiveness in machine learning decision systems alongside Barocas, Hardt, and Narayanan’s landmark book Fairness and Machine Learning. What, if anything, is different this time, with the rise of machine learning-based aids to bureaucratic decision-making? I want to show how a focus on normative distinctiveness can obscure from view a much more significant upshot of machine learning: that the mere existence of feasible alternatives presses new justificatory demands not just on the design of new technical systems but on the prevailing human-centered decision regime. Prevailing discussions, in particular those that aim to critically scrutinize machine learning decision systems, have a tendency to depoliticize conventional bureaucratic structures of decision-making. This leads to a missed opportunity for a broader normative reevaluation of what we owe to each other in a world of expanded practical possibility.

Featured Guests

Professor Lily Hu, Department of Philosophy, Yale University.