User Privacy Harms and Risks in Conversational AI: A Proposed Framework

TL;DR

This paper develops a comprehensive framework based on Solove's taxonomy to identify and analyze privacy harms and risks in text-based AI chatbot interactions, aiding responsible AI deployment.

Contribution

It extends Solove's privacy taxonomy specifically for conversational AI, providing a practical tool for developers and policymakers to address privacy concerns.

Findings

Identified 9 privacy harms in chatbot interactions

Identified 9 privacy risks associated with chatbot use

Framework covers multiple interaction stages for privacy assessment

Abstract

This study presents a unique framework that applies and extends Solove (2006)'s taxonomy to address privacy concerns in interactions with text-based AI chatbots. As chatbot prevalence grows, concerns about user privacy have heightened. While existing literature highlights design elements compromising privacy, a comprehensive framework is lacking. Through semi-structured interviews with 13 participants interacting with two AI chatbots, this study identifies 9 privacy harms and 9 privacy risks in text-based interactions. Using a grounded theory approach for interview and chatlog analysis, the framework examines privacy implications at various interaction stages. The aim is to offer developers, policymakers, and researchers a tool for responsible and secure implementation of conversational AI, filling the existing gap in addressing privacy issues associated with text-based AI chatbots.