# Towards Explainable and Safe Conversational Agents for Mental Health: A Survey

arXiv: https://arxiv.org/abs/2304.13191 · HTML: https://mlsanity.com/paper/2304.13191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 88 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 27 of 27)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Recipes for Safety in Open-domain Chatbots](https://mlsanity.com/paper/2010.07079/) (2020)
- [Challenges in Detoxifying Language Models](https://mlsanity.com/paper/2109.07445/) (2021)
- [Lessons from Natural Language Inference in the Clinical Domain](https://mlsanity.com/paper/1808.06752/) (2018)
- [Improved Image Captioning via Policy Gradient optimization of SPIDEr](https://mlsanity.com/paper/1612.00370/) (2016)
- [Ethical Challenges in Data-Driven Dialogue Systems](https://mlsanity.com/paper/1711.09050/) (2017)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [NUBIA: NeUral Based Interchangeability Assessor for Text Generation](https://mlsanity.com/paper/2004.14667/) (2020)
- [A Study of Automatic Metrics for the Evaluation of Natural Language Explanations](https://mlsanity.com/paper/2103.08545/) (2021)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media](https://mlsanity.com/paper/2012.09123/) (2020)
- [Deep Learning Mental Health Dialogue System](https://mlsanity.com/paper/2301.09412/) (2023)
- [Process Knowledge-infused Learning for Suicidality Assessment on Social Media](https://mlsanity.com/paper/2204.12560/) (2022)
- [A Conversational Interface to Improve Medication Adherence: Towards AI Support in Patient's Treatment](https://mlsanity.com/paper/1803.09844/) (2018)
- [Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2210.10227/) (2022)
- [Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women](https://mlsanity.com/paper/2107.10410/) (2021)
- [Towards Detecting Need for Empathetic Response in Motivational Interviewing](https://mlsanity.com/paper/2105.09649/) (2021)
- [Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes](https://mlsanity.com/paper/2206.06141/) (2022)
- [The Duality of Data and Knowledge Across the Three Waves of AI](https://mlsanity.com/paper/2103.13520/) (2021)

## Built on by (most cited first, 2 of 2)

- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)

## Abstract (arXiv)

Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role of VMHAs is to provide emotional support through information, focusing less on developing a reflective conversation with the patient. A more comprehensive, safe and explainable approach is required to build responsible VMHAs to ask follow-up questions or provide a well-informed response. This survey offers a systematic critical review of the existing conversational agents in mental health, followed by new insights into the improvements of VMHAs with contextual knowledge, datasets, and their emerging role in clinical decision support. We also provide new directions toward enriching the user experience of VMHAs with explainability, safety, and wholesome trustworthiness. Finally, we provide evaluation metrics and practical considerations for VMHAs beyond the current literature to build trust between VMHAs and patients in active communications.