AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbots

TL;DR

This paper introduces an AI chaperone framework that uses a language model to evaluate conversations for parasocial cues, aiming to prevent harmful parasocial relationships with chatbots, especially for vulnerable users.

Contribution

It presents a novel response evaluation framework that effectively detects parasocial cues in conversations, offering a promising safeguard against chatbot-induced parasocial relationships.

Findings

Successfully identified all parasocial conversations in synthetic data

Detected parasocial cues within the first few exchanges

Avoided false positives under a unanimity rule

Abstract

Emerging reports of the harms caused to children and adults by AI sycophancy and by parasocial ties with chatbots point to an urgent need for safeguards against such risks. Yet, preventing such dynamics is challenging: parasocial cues often emerge gradually in private conversations between chatbots and users, and we lack effective methods to mitigate these risks. We address this challenge by introducing a simple response evaluation framework (an AI chaperone agent) created by repurposing a state-of-the-art language model to evaluate ongoing conversations for parasocial cues. We constructed a small synthetic dataset of thirty dialogues spanning parasocial, sycophantic, and neutral conversations. Iterative evaluation with five-stage testing successfully identified all parasocial conversations while avoiding false positives under a unanimity rule, with detection typically occurring within the first few exchanges. These findings provide preliminary evidence that AI chaperones can be a viable solution for reducing the risk of parasocial relationships.