When LLMs Threaten Democratic Autonomy
TL;DR
It is argued that many of the ways in which computer scientists, deliberative democratic practitioners, and others are using LLMs to do this threatens the non-instrumental value of a collective's ability to determine its own future.
Abstract
An increasingly large number of projects seek to use Large Language Models (LLMs) to enhance or support democracy. I argue that many of the ways in which computer scientists, deliberative democratic practitioners, and others are using LLMs to do this threatens the non-instrumental value of a collective’s ability to determine its own future. In particular, I present a novel worry that projects aimed at algorithmically facilitating deliberation and representing people’s interests in political processes threaten what has been called democratic autonomy. I begin with some conceptual groundwork concerning collective self-determination (and specifically democratic autonomy) to motivate its non-instrumental value. Next, I offer a few necessary conditions for democratic autonomy from the literature, such as the possession of a joint intention and said joint intention being realized in policy at least some significant portion of the time. I then show when two kinds of projects that use LLMs to enhance democracy – facilitative and representative LLMs – threaten these necessary conditions and thus democratic autonomy. I conclude by outlining some practical upshots and recommendations for projects that aim to use LLMs to enhance or support democracy.