Habert S. Gregory Distinguished Lecturer Series


11:45 am-12:35 pm
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Fay Cobb Payton, Ph.D.
Professor of Information Technology/Analytics
North Carolina State University

Abstract: Research Mashup: From Health Care IT to AI Bias

This presentation will review my research trajectory starting from earlier publications to works in progress. My earlier research used interorganizational theory focused on information sharing and the impacts on health organizations and patients given the increased needs to share clinical information. I also explored the use of health information networks supporting homecare systems for persons living with HIV and caregivers of Alzheimer’s disease patients. This work was informed by Roger’s theory of diffusion to address social isolation associated with stigmatized health conditions. I have continued this research with the expansion into social media, human computer interaction, content design as well as health disparities via comorbid conditions (including breast cancer, mental health, HIV) and data modeling. A central thread of my research is leveraging, creating and using information to assess society (community) needs and implications, and I have used adaptative structuration theory as a framework. Content design and information seeking can offer critical insight into health dissemination, particularly among Generation Z, and Black millenials who often fail to identify with existing images and messages depicted by existing online narratives. Hence, @myhealthimpact was born and informed the need to examine sources of bias regardless of the research methodology (e.g., focus groups text/data mining, survey, action research, statistical modeling) executed and to do so with convergent teams and mindsets. I have examined these notions relative to computing education, talent management and others. I will discuss how my research continues to examine disparities within and across the stove pipes of disciplinary silos, and what equitable AI (if achieved) “looks” like.

Tune in via Zoom:

Meeting ID: 936 9569 8478
Passcode: 2i402x


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Department of Industrial & Systems Engineering