论文标题
部分可观测时空混沌系统的无模型预测
Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
论文作者
论文摘要
人工智能（AI）的进步正在为增强和与人合作的系统提供了能力，以执行简单的机械任务，例如调度会议和语法检查文本。但是，由于人工智能系统在理解复杂的人类情绪和这些任务的开放性质上，这种人为合作对更复杂，创造性的任务（例如进行移情对话）构成了挑战。在这里，我们专注于点对点的心理健康支持，在这种情况下，同理心对成功至关重要，并研究AI如何与人类合作，以促进文本，在线支持性对话中的同时同情。我们开发了Hailey，这是一位循环代理商，提供了即时的反馈，以帮助提供支持（同伴支持者）的参与者对寻求帮助的人（支持寻求者）的反应更加同情。我们在一项非临床随机对照试验中评估了Hailey，与真实世界的同伴支持者（n = 300），这是一个大型的在线在线点对点支持平台（n = 300）。我们表明，我们的人类协作方法导致同龄人之间的对话同理心增加19.60％。此外，我们发现在同伴支持者的子样本中，同情的同情增加了38.88％，这些支持者自我识别会遇到难以提供支持。我们系统地分析了人类协作模式，并发现同伴支持者能够直接和间接地使用AI反馈，而不会过分依赖AI，同时报告后反馈后改善了自我效能感。我们的发现证明了反馈驱动的，AI-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-IN-AN-IN式介绍性。
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.
