mlsanityData to 14 Oct 2026

AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression

arXiv 2504.18932 ↗First posted 26 Apr 2025cs.HC cs.AIcited by 13

Not on the map. Why?

Abstract (arXiv)

Recent advancements in LLMs enable chatbots to interact with individuals on a range of queries, including sensitive mental health contexts. Despite uncertainties about their effectiveness and reliability, the development of LLMs in these areas is growing, potentially leading to harms. To better identify and mitigate these harms, it is critical to understand how the values of people with lived experiences relate to the harms. In this study, we developed a technology probe, a GPT-4o based chatbot called Zenny, enabling participants to engage with depression self-management scenarios informed by previous research. We used Zenny to interview 17 individuals with lived experiences of depression. Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management. This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots, aiming to enhance self-management support while minimizing risks.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 23 of its 133 references that are in the map. First posted on arXiv 26 Apr 2025.

Builds on

23 of 133 references.

References · 23

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2020Artificial Intelligence, Values and Alignmentcited by 448
  3. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  4. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 94
  5. 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
  6. 2023Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Makingcited by 67
  7. 2023Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creationcited by 64
  8. 2024Design Principles for Generative AI Applicationscited by 57
  9. 2023Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuringcited by 53
  10. 2023MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journalingcited by 50
  11. 2022How Different Groups Prioritize Ethical Values for Responsible AIcited by 49
  12. 2023Benefits and Harms of Large Language Models in Digital Mental Healthcited by 40
11 more
  1. 2021Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AIcited by 38
  2. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  3. 2022Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theorycited by 32
  4. 2023Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Riskscited by 31
  5. 2024Designing for Human-Agent Alignment: Understanding what humans want from their agentscited by 18
  6. 2022Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in Chinacited by 17
  7. 2023Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholderscited by 13
  8. 2024Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkitscited by 10
  9. 2024The Future of HCI-Policy Collaborationcited by 8
  10. 2024Towards Understanding Emotions for Engaged Mental Health Conversationscited by 3
  11. 2023Reconciling Governmental Use of Online Targeting With Democracycited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 6.

13 papers.

Sort:

By area:

  1. 2025Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregiverscited by 13
  2. 2025Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narrativescited by 12
  3. 2026RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactionscited by 10
  4. 2025Mental Health Impacts of AI Companions: Triangulating Social Media Quasi-Experiments, User Perspectives, and Relational Theorycited by 9
  5. 2025Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregiverscited by 7
  6. 2026PAIR-SAFE: A Paired-Agent Approach for Runtime Auditing and Refining AI-Mediated Mental Health Supportcited by 7
  7. 2026AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?cited by 5
  8. 2025Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberationcited by 4
  9. 2025Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiencycited by 1
  10. 2026Designing KRIYA: An AI Companion for Wellbeing Self-Reflectioncited by 1
  11. 2026A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbotscited by 1
  12. 2025"I Said Things I Needed to Hear Myself": Peer Support as an Emotional, Organisational, and Sociotechnical Practice in Singaporecited by 0
1 more
  1. 2026AI Content Moderation in Therapy Conversationscited by 0