Artificial Empathy: AI based Mental Health
Many people suffer from mental health problems but not everyone seeks professional help or has access to mental health care. AI chatbots have increasingly become a go-to for individuals who either have mental disorders or simply want someone to talk to. This paper presents a study on participants who have previously used chatbots and a scenario-based testing of large language model (LLM) chatbots. Our findings indicate that AI chatbots were primarily utilized as a "Five minute therapist" or as a non-judgmental companion. Participants appreciated the anonymity and lack of judgment from chatbots. However, there were concerns about privacy and the security of sensitive information. The scenario-based testing of LLM chatbots highlighted additional issues. Some chatbots were consistently reassuring, used emojis and names to add a personal touch, and were quick to suggest seeking professional help. However, there were limitations such as inconsistent tone, occasional inappropriate responses (e.g., casual or romantic), and a lack of crisis sensitivity, particularly in recognizing red flag language and escalating responses appropriately. These findings can inform both the technology and mental health care industries on how to better utilize AI chatbots to support individuals during challenging emotional periods.
Code (0)
등록된 구현이 없습니다.
Tasks
Large Language ModelSimilar Papers 제목 키워드 기반
Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective
Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trendi…
Emotion RecognitionA Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
Empathy is critical to successful mental health support. Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. Because millions …
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and…
Deep Reinforcement LearningDialogue GenerationLanguage Modellingreinforcement-learning+4Empathy-R1: A Chain-of-Empathy and Reinforcement Learning Framework for Long-Form Mental Health Support
Empathy is critical for effective mental health support, especially when addressing Long Counseling Texts (LCTs). However, existing Large Language Models (LLMs) often generate replies that are semantically fluent but lac…
Reinforcement LearningHuman-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text. However, such Human-AI colla…
Scheduling