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

arXiv: https://arxiv.org/abs/2508.15748 · HTML: https://mlsanity.com/paper/2508.15748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Defeating Prompt Injections by Design](https://mlsanity.com/paper/2503.18813/) (2025)
- [ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://mlsanity.com/paper/2505.13995/) (2025)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships](https://mlsanity.com/paper/2410.20130/) (2024)
- [Why human-AI relationships need socioaffective alignment](https://mlsanity.com/paper/2502.02528/) (2025)
- [Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust](https://mlsanity.com/paper/2502.10844/) (2025)
- [Evaluating Psychological Safety of Large Language Models](https://mlsanity.com/paper/2212.10529/) (2022)
- [From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents](https://mlsanity.com/paper/2412.07951/) (2024)
- [Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with Prompt Evaluation](https://mlsanity.com/paper/2502.00580/) (2025)
- [Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting](https://mlsanity.com/paper/2410.12284/) (2024)

## Built on by (most cited first, 1 of 1)

- [From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities](https://mlsanity.com/paper/2606.17174/) (2026)

## Abstract (arXiv)

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.