AI Content Moderation in Therapy Conversations
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Abstract (arXiv)
Large language models (LLMs) are increasingly being used for emotional support. They are also being developed for formal therapy purposes. However, LLMs like ChaptGPT or Llama are often developed with content moderation guardrails that prevent them from discussing sensitive subjects with users for both liability and safety purposes, and this inability to broach these subjects may affect their capacity as therapists. In this study, we perform an algorithm audit on three state-of-the-art moderation systems (OpenAI's moderation endpoint, Meta's Llama Guard, and Google's Shield Gemma) to investigate the extent to which these systems flag the content of real-life therapy sessions as undesirable. Our results raise implications for the limitations that users and organizations may encounter when designing LLMs to play the part of a therapist.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 25 May 2026.
Builds on
11 of 30 references.
References · 11
- 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 356
- 2023Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuringcited by 53
- 2024SLM-Mod: Small Language Models Surpass LLMs at Content Moderationcited by 27
- 2024LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devicescited by 21
- 2025CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answeringcited by 21
- 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depressioncited by 13
- 2025MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governancecited by 13
- 2025Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregiverscited by 13
- 2026RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactionscited by 10
- 2025Exploring the Effects of Chatbot Anthropomorphism and Human Empathy on Human Prosocial Behavior Toward Chatbotscited by 3
- 2024Practicing Stress Relief for the Everyday: Designing Social Simulation Using VR, AR, and LLMscited by 1
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- 2026TherapyGym: Evaluating and Aligning Clinical Fidelity and Safety in Therapy Chatbots
- 2025AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing
- 2025From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?
- 2026Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming
- 2024Script-Based Dialog Policy Planning for LLM-Powered Conversational Agents: A Basic Architecture for an "AI Therapist"
- 2026Assessing the Effectiveness of LLMs in Delivering Cognitive Behavioral Therapy
- 2024Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health
- 2024Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models