@inproceedings{qiu-etal-2025-emoagent,
title = "{E}mo{A}gent: Assessing and Safeguarding Human-{AI} Interaction for Mental Health Safety",
author = "Qiu, Jiahao and
He, Yinghui and
Juan, Xinzhe and
Wang, Yimin and
Liu, Yuhan and
Yao, Zixin and
Wu, Yue and
Jiang, Xun and
Yang, Ling and
Wang, Mengdi",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.594/",
pages = "11752--11767",
ISBN = "979-8-89176-332-6",
abstract = "The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. EmoAgent comprises two components: **EmoEval** simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters. It uses clinically proven psychological and psychiatric assessment tools (PHQ-9, PDI, PANSS) to evaluate mental risks induced by LLM. **EmoGuard** serves as an intermediary, monitoring users' mental status, predicting potential harm, and providing corrective feedback to mitigate risks. Experiments conducted in popular character-based chatbots show that emotionally engaging dialogues can lead to psychological deterioration in vulnerable users, with mental state deterioration in more than 34.4{\%} of the simulations. EmoGuard significantly reduces these deterioration rates, underscoring its role in ensuring safer AI-human interactions."
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<abstract>The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. EmoAgent comprises two components: **EmoEval** simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters. It uses clinically proven psychological and psychiatric assessment tools (PHQ-9, PDI, PANSS) to evaluate mental risks induced by LLM. **EmoGuard** serves as an intermediary, monitoring users’ mental status, predicting potential harm, and providing corrective feedback to mitigate risks. Experiments conducted in popular character-based chatbots show that emotionally engaging dialogues can lead to psychological deterioration in vulnerable users, with mental state deterioration in more than 34.4% of the simulations. EmoGuard significantly reduces these deterioration rates, underscoring its role in ensuring safer AI-human interactions.</abstract>
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%0 Conference Proceedings
%T EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety
%A Qiu, Jiahao
%A He, Yinghui
%A Juan, Xinzhe
%A Wang, Yimin
%A Liu, Yuhan
%A Yao, Zixin
%A Wu, Yue
%A Jiang, Xun
%A Yang, Ling
%A Wang, Mengdi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F qiu-etal-2025-emoagent
%X The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. EmoAgent comprises two components: **EmoEval** simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters. It uses clinically proven psychological and psychiatric assessment tools (PHQ-9, PDI, PANSS) to evaluate mental risks induced by LLM. **EmoGuard** serves as an intermediary, monitoring users’ mental status, predicting potential harm, and providing corrective feedback to mitigate risks. Experiments conducted in popular character-based chatbots show that emotionally engaging dialogues can lead to psychological deterioration in vulnerable users, with mental state deterioration in more than 34.4% of the simulations. EmoGuard significantly reduces these deterioration rates, underscoring its role in ensuring safer AI-human interactions.
%U https://aclanthology.org/2025.emnlp-main.594/
%P 11752-11767
Markdown (Informal)
[EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety](https://aclanthology.org/2025.emnlp-main.594/) (Qiu et al., EMNLP 2025)
ACL
- Jiahao Qiu, Yinghui He, Xinzhe Juan, Yimin Wang, Yuhan Liu, Zixin Yao, Yue Wu, Xun Jiang, Ling Yang, and Mengdi Wang. 2025. EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 11752–11767, Suzhou, China. Association for Computational Linguistics.