Abstract

While much of politics is about making decisions, machine learning is about making predictions. Despite this diverging focus, machine learning is increasingly deployed in politically sensitive fields. This chapter focuses on the tension arising from this deployment. We specifically address the question of whether machine learning-based political decision making can be justified and how this question of justification interrelates with considerations about epistemological aspects of machine learning. To that end, we evaluate the epistemic state that individual decision makers can reach when relying on machine learning systems and assess when this state can be considered justified. We then broaden this first-person perspective and turn to political justification. By employing the so-called public justification principle, we investigate whether epistemic justification transmits to political justification in applications of machine learning. We argue that in light of the epistemological considerations, a series of problems arise concerning the political justification of deploying these models.