mlsanityData to 7 Oct 2026

A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots

arXiv 2601.15412 ↗First posted 21 Jan 2026cs.HC cs.AI cs.CYcited by 1

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Abstract (arXiv)

Mental health concerns are rising globally, prompting increased reliance on technology to address the demand-supply gap in mental health services. In particular, mental health chatbots are emerging as a promising solution, but these remain largely untested, raising concerns about safety and potential harms. In this paper, we dive into the literature to identify critical gaps in the design and implementation of mental health chatbots. We contribute an operational checklist to help guide the development and design of more trustworthy, safe, and user-friendly chatbots. The checklist serves as both a developmental framework and an auditing tool to ensure ethical and effective chatbot design. We discuss how this checklist is a step towards supporting more responsible design practices and supporting new standards for sociotechnically sound digital mental health tools.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 55 references that are in the map. First posted on arXiv 21 Jan 2026.

Builds on

17 of 55 references.

References · 17

  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  3. 2024"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trustcited by 67
  4. 2025Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providerscited by 67
  5. 2024A Computational Framework for Behavioral Assessment of LLM Therapistscited by 61
  6. 2024Design Principles for Generative AI Applicationscited by 57
  7. 2023Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuringcited by 53
  8. 2023MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journalingcited by 50
  9. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  10. 2023From task structures to world models: What do LLMs know?cited by 46
  11. 2023Benefits and Harms of Large Language Models in Digital Mental Healthcited by 39
  12. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
5 more
  1. 2023Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AIcited by 33
  2. 2023Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebookcited by 21
  3. 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depressioncited by 13
  4. 2025Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregiverscited by 13
  5. 2024AI on My Shoulder: Supporting Emotional Labor in Front-Office Roles with an LLM-based Empathetic Coworkercited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The Epistemic Politics of AI Anthropomorphismcited by 0