{"683105":{"#nid":"683105","#data":{"type":"event","title":"PhD Proposal by Jiawei Zhou ","body":[{"value":"\u003Cp\u003ETitle: Generative Artificial Intelligence in Public Health Context: Assessing and Communicating Risks in Information Ecosystems\u003C\/p\u003E\u003Cp\u003EDate: Friday, July 18, 2025\u003Cbr\u003ETime: 10 AM \u2014 12 PM ET\u003Cbr\u003ELocation: Virtual - Zoom link\u003C\/p\u003E\u003Cp\u003EJiawei Zhou\u003Cbr\u003EPh.D. student in Human-Centered Computing\u003Cbr\u003ESchool of Interactive Computing\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003ECommittee:\u003Cbr\u003EDr. Munmun De Choudhury (Advisor) \u2014 School of Interactive Computing, Georgia Institute of Technology\u003Cbr\u003EDr. Andrea G. Parker \u2014 School of Interactive Computing, Georgia Institute of Technology\u003Cbr\u003EDr. Srijan Kumar \u2014 School of Computational Science and Engineering, Georgia Institute of Technology\u003Cbr\u003EDr. Nick Diakopoulos \u2014 School of Communication, Northwestern University\u003Cbr\u003EDr. Q. Vera Liao \u2014 FATE Group, Microsoft Research Montreal \u0026amp; Computer Science and Engineering, University of Michigan\u003C\/p\u003E\u003Cp\u003EAbstract:\u003Cbr\u003ETechnology is increasingly shaping the way we interact with information, where both the quality of that information and the affordances of information technologies shape people\u0027s attitudes and decision-making. Generative Artificial Intelligence (AI), such as large language models, differs fundamentally from prior information and communication technologies that were primarily task-centric, as it can produce new content in a probabilistic manner and at scale. This generative nature is both its power and its pitfall. On one hand, documented evidence indicates that it creates low-quality information, from hallucinated outputs to oversimplified answers. On the other hand, it is rapidly adopted as standalone applications or as features embedded into existing systems, often without users\u0027 full awareness of AI\u2019s role in content generation. This imbalance between adoption speed and public understanding raises pressing questions about the influences of generative AI on information ecosystems.\u003C\/p\u003E\u003Cp\u003EI posit that understanding and mitigating the risks of AI-generated low-quality information requires contextualized assessment alongside traditional information sources, as well as adequate communication about its risks and distinctions. Situated within the context of a variety of public health crises and challenges, this dissertation adopts computational and qualitative methods alongside deep-seated domain collaborations with experts in the fields of public health, informatics, mass communication, and natural language processing.\u003C\/p\u003E\u003Cp\u003EIn my completed work, I have shown that low-quality information is not only a harmful endpoint but also a source for further harm, and demonstrated how AI-generated outputs amplify these issues through high persuasiveness and detection difficulty. I have also synthesized stakeholder views on generative AI\u0027s ecological risks that extend beyond factual accuracy to broader impacts, and identified patterns and gaps in public discourse on how these distinctions and risks of generative AI are communicated. These insights together underscore the need to help the public navigate emerging AI technologies responsibly. To this end, my proposed work will explore ways to assist mindful and meaningful AI use for informational needs. This experimental study will examine the effects of risk communication on people\u0027s trust in using generative AI for a set of health informational needs, ranging from healthy lifestyle guidance to therapeutic mental health support. Collectively, this dissertation aims to advance a deeper understanding of generative AI in information environments and to inform more responsible adoption, communication, and governance.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGenerative Artificial Intelligence in Public Health Context: Assessing and Communicating Risks in Information Ecosystems\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Generative Artificial Intelligence in Public Health Context: Assessing and Communicating Risks in Information Ecosystems"}],"uid":"27707","created_gmt":"2025-07-11 16:29:46","changed_gmt":"2025-07-11 16:29:46","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-07-18T10:00:00-04:00","event_time_end":"2025-07-18T12:00:00-04:00","event_time_end_last":"2025-07-18T12:00:00-04:00","gmt_time_start":"2025-07-18 14:00:00","gmt_time_end":"2025-07-18 16:00:00","gmt_time_end_last":"2025-07-18 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual - Zoom link","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}