Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation

• URL: http://arxiv.org/abs/2406.11852v2
• Date: Wed, 14 Aug 2024 18:20:22 GMT
• Title: Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation
• Authors: Declan Grabb, Max Lamparth, Nina Vasan,
• Abstract summary: This paper proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents. We also evaluate 14 state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questionnaires.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Amidst the growing interest in developing task-autonomous AI for automated mental health care, this paper addresses the ethical and practical challenges associated with the issue and proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents in the context of mental health support. We also evaluate fourteen state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questionnaires designed to reflect various mental health conditions, such as psychosis, mania, depression, suicidal thoughts, and homicidal tendencies. The questionnaire design and response evaluations were conducted by mental health clinicians (M.D.s). We find that existing language models are insufficient to match the standard provided by human professionals who can navigate nuances and appreciate context. This is due to a range of issues, including overly cautious or sycophantic responses and the absence of necessary safeguards. Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. We explore solutions to enhance the safety of current models. Before the release of increasingly task-autonomous AI systems in mental health, it is crucial to ensure that these models can reliably detect and manage symptoms of common psychiatric disorders to prevent harm to users. This involves aligning with the ethical framework and default behaviors outlined in our study. We contend that model developers are responsible for refining their systems per these guidelines to safeguard against the risks posed by current AI technologies to user mental health and safety. Trigger warning: Contains and discusses examples of sensitive mental health topics, including suicide and self-harm.

Related papers

• MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
  Mental health disorders are one of the most serious diseases in the world. Privacy concerns limit the accessibility of personalized treatment data. MentalArena is a self-play framework to train language models.
  arXiv  Detail & Related papers  (2024-10-09T13:06:40Z)
• Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots [0.0]
  This paper explores the potential of AI-enabled chatbots as a scalable solution. We assess their ability to deliver empathetic, meaningful responses in mental health contexts. We propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality.
  arXiv  Detail & Related papers  (2024-09-17T20:49:13Z)
• Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models [44.99833362998488]
  We explore the use of large language models (LLMs) like GPT-4 to augment psychological consultation services. Our approach introduces a novel layered prompting system that dynamically adapts to user input. We also develop empathy-driven and scenario-based prompts to enhance the LLM's emotional intelligence.
  arXiv  Detail & Related papers  (2024-08-29T05:47:14Z)
• Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook [0.7689629183085726]
  We conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare. A significant portion of the population actively engages in online social media platforms, creating a vast repository of personal data. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare.
  arXiv  Detail & Related papers  (2024-06-10T02:51:16Z)
• No General Code of Ethics for All: Ethical Considerations in Human-bot Psycho-counseling [16.323742994936584]
  We propose aspirational ethical principles specifically tailored for human-bot psycho-counseling. We examined the responses generated by EVA2.0, GPT-3.5, and GPT-4.0 in the context of psycho-counseling and mental health inquiries.
  arXiv  Detail & Related papers  (2024-04-22T10:29:04Z)
• PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents [68.50571379012621]
  Psychological measurement is essential for mental health, self-understanding, and personal development. PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
  arXiv  Detail & Related papers  (2024-02-19T18:00:30Z)
• PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety [70.84902425123406]
  Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence. However, the potential misuse of this intelligence for malicious purposes presents significant risks. We propose a framework (PsySafe) grounded in agent psychology, focusing on identifying how dark personality traits in agents can lead to risky behaviors. Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents' self-reflection when engaging in dangerous behavior, and the correlation between agents' psychological assessments and dangerous behaviors.
  arXiv  Detail & Related papers  (2024-01-22T12:11:55Z)
• Challenges of Large Language Models for Mental Health Counseling [4.604003661048267]
  The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment. The application of large language models (LLMs) in the mental health domain raises concerns regarding the accuracy, effectiveness, and reliability of the information provided. This paper investigates the major challenges associated with the development of LLMs for psychological counseling, including model hallucination, interpretability, bias, privacy, and clinical effectiveness.
  arXiv  Detail & Related papers  (2023-11-23T08:56:41Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models [3.650517404744655]
  Psy-LLM framework is an AI-based tool leveraging Large Language Models for question-answering in psychological consultation settings. Our framework combines pre-trained LLMs with real-world professional Q&A from psychologists and extensively crawled psychological articles. It serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress.
  arXiv  Detail & Related papers  (2023-07-22T06:21:41Z)
• Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks [43.2802002858859]
  This paper enhances text representation with lexicon-based sentiment scores and latent topics. It proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators.
  arXiv  Detail & Related papers  (2020-04-16T11:18:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.