Epistemology and Politics of AI
Chapter 8
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Author / Producer
Date
2025-07-03
Publication Type
Book Chapter, Book Chapter
ETH Bibliography
yes
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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.
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Publication status
published
Book title
A Companion to Applied Philosophy of AI
Journal / series
Volume
Pages / Article No.
104 - 117
Publisher
Wiley
Event
Edition / version
First edition
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09614 - Vayena, Eftychia / Vayena, Eftychia
Notes
OB has been supported by an ETH Zurich Postdoctoral Fellowship.