From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents

TL;DR

This paper develops a detailed taxonomy of psychological risks from AI conversational agents based on lived experiences, highlighting complex interactions and offering design recommendations for safer AI development.

Contribution

It introduces a novel psychological risk taxonomy derived from lived experiences and a multi-path vignette framework to understand AI's psychological impacts.

Findings

Identified 19 AI behaviors linked to psychological risks

Mapped 21 negative psychological impacts and 15 user contexts

Provided design recommendations for safer AI agents

Abstract

Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences of individuals. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through the lived experiences of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 people with lived mental health experience and workshops involving experts with lived experience to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette-based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers.