Knowing oneself with and through AI: From self-tracking to chatbots

TL;DR

This paper explores how AI technologies like self-tracking, digital memories, and chatbots influence self-knowledge, highlighting benefits, risks, and the complex ways AI shapes personal understanding.

Contribution

It offers a comprehensive analysis of AI's role in self-understanding across multiple domains, integrating frameworks from distributed cognition to reveal new insights.

Findings

Self-tracking promotes self-optimization but may limit other self-understanding forms.

Digital autobiographical memories aid reflection but pose manipulation risks.

LLMs support self-exploration yet risk detachment from reality.

Abstract

This chapter examines how algorithms and artificial intelligence are transforming our practices of self-knowledge, self-understanding, and self-narration. Drawing on frameworks from distributed cognition, I analyse three key domains where AI shapes how and what we come to know about ourselves: self-tracking applications, technologically-distributed autobiographical memories, and narrative co-construction with Large Language Models (LLMs). While self-tracking devices promise enhanced self-knowledge through quantified data, they also impose particular frameworks that can crowd out other forms of self-understanding and promote self-optimization. Digital technologies increasingly serve as repositories for our autobiographical memories and self-narratives, offering benefits such as detailed record-keeping and scaffolding during difficult periods, but also creating vulnerabilities to algorithmic manipulation. Finally, conversational AI introduces new possibilities for interactive narrative construction that mimics interpersonal dialogue. While LLMs can provide valuable support for self-exploration, they also present risks of narrative deference and the construction of self-narratives that are detached from reality.