嘉　宾：李顾杰  PhD. Candidate 马里兰大学

主持人：刘佳璐  助理教授  开云网页登录 安泰经济与管理学院

时　间：2024年11月15日（周五）9:30-11:00

地　点：开云网页登录 徐汇校区安泰楼A305

内容简介:

Mental healthcare has become a concern globally, and the challenges of providing and accessing appropriate care are only magnified by a dearth of available providers. At the same time, Large Language Models (LLMs) are anticipated to hold transformative potential for numerous industries. LLMs demonstrate superior performance in medical expertise, communication skill, as well as emotional and social intelligence, and therefore such technologies are expected to alleviate the problem of workforce undersupply in mental healthcare. However, the question of how human-provided mental healthcare will evolve in response to the emergence of LLMs remains critical but unanswered. We focus on an online mental health forum wherein mental healthcare providers offer support to seekers through question-and-answer (Q&A). In early 2023, an LLM-powered bot account was integrated into the forum to automatically leave responses to seekers. Leveraging the (unanticipated) introduction of the chatbot as a natural experiment, we investigate changes in the engagement and the nature of support from human providers—both of which are essential for mental well-being and counseling success of seekers. We observe human providers‘ disengagement and their downgrading support in the forum. Concerningly, the disengagement became more pronounced in tougher cases, such as seekers expressing suicidal ideation. Overall, our study provides important implications for advanced AI integration in mental healthcare, offering actionable insights for multiple stakeholders.

演讲人简介：

Gujie Li is a Ph.D. Candidate in Information Systems at Robert H. Smith School of Business, University of Maryland. His research interests center around understanding how platform design choices including artificial intelligence and multimodal communication reshape user behavior and experience, specifically within the contexts of education, healthcare and marketing. In addition, he cares about critical social topics such as gender/racial bias, as well as the challenges faced by underrepresented groups. On a broader scale, the overarching goal is to explore and advance technological solutions for fostering a more educated, healthier, and equitable society. He leverages quasi-experiment opportunities in the real world, and combines econometrics with NLP, audio and video analytics to derive business insights from large-scale user-generated content.

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