[论文解读] Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

Ashish Sharma, Inna Wanyin Lin+3 more|arXiv (Cornell University)|Mar 28, 2022

Digital Mental Health Interventions被引用 22

一句话总结

这项研究推出 Hailey，一个 AI-in-the-loop 系统，向 TalkLife 的同行支持者提供即时的同理心写作反馈，在在线同伴对同伴心理健康对话中显著提升表达的同理心。

ABSTRACT

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 collaboration poses challenges for more complex, creative tasks, such as carrying out empathic conversations, due to difficulties of AI systems in understanding complex human emotions and the open-ended nature of these tasks. Here, we focus on peer-to-peer mental health support, a setting in which empathy is critical for success, and examine how AI can collaborate with humans to facilitate peer empathy during textual, online supportive conversations. We develop Hailey, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers). We evaluate Hailey in a non-clinical randomized controlled trial with real-world peer supporters on TalkLife (N=300), a large online peer-to-peer support platform. We show that our Human-AI collaboration approach leads to a 19.60% increase in conversational empathy between peers overall. Furthermore, we find a larger 38.88% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support. We systematically analyze the Human-AI collaboration patterns and find that peer supporters are able to use the AI feedback both directly and indirectly without becoming overly reliant on AI while reporting improved self-efficacy post-feedback. Our findings demonstrate the potential of feedback-driven, AI-in-the-loop writing systems to empower humans in open-ended, social, creative tasks such as empathic conversations.

研究动机与目标

• 在在线同伴对同伴心理健康平台中，激发并让未受训练的大多数支持者能够提供具有同理心的、开放式的支持。
• 开发并评估一个 AI-in-the-loop 反馈代理，能够向同伴支持者提供可执行、即时的同理心指导。
• 评估在人机协作下，是否比单纯传统培训在非临床随机试验中提高表达的同理心。

提出的方法

• 设计 Hailey，使其基于求助帖文及当前支持者的回应，提供带有 Insert 和 Replace 选项的即时反馈。
• 在 TalkLife 上进行一项非临床随机对照试验，N=300 名参与者，比较 Human+AI(治疗组) 与 Human Only(对照组)。
• 使用人工评价和自动同理心评分来评估结果，并通过事后安全与伦理措施确保安全性。

实验结果

研究问题

• RQ1 即时的人机反馈是否比没有反馈时能提升同行支持者的同理回应？
• RQ2 在人机协作下，写作同理回应有困难的支持者与没有此类困难的支持者相比，效果如何？
• RQ3 人机协作的模式（咨询频率与使用情况）及其与同理心提升的关系是怎样的？

主要发现

• 人机反馈比仅人工达到更高的同理心分数，提升 19.60%（0–6 量表中为 1.77 对 1.48，p<1e-5）。
• 独立的人类评估显示 Human+AI 的回应在同理性方面占比更高，为 46.87%，而 Human Only 为 37.40%（p<0.01）。
• 在报告写作挑战的参与者中，同理心提升在 Human+AI 为 38.88%，而 Human Only 为 11.87%（p<1e-5）。
• 更频繁咨询 AI 的参与者表现出更高的表达同理心，频繁使用 AI 与更高的同理心分数相关。
• 63.31% 认为反馈有帮助，60.43% 认为可执行，77.70% 希望在如 TalkLife 等平台上部署。
• 69.78% 表示在研究结束后提供支持更有信心。