Coding, Coded & Counting: A Bias Continuum

Seminar / Lecture
Open to the Public

Geometric abstract image of brain

Part of the Special ECE Seminar Series 

Modern Artificial Intelligence


Coding, Coded & Counting: A Bias Continuum


Fay Cobb Payton


This presentation will review my research trajectory starting from earlier publications to works in progress.  My earlier research examined information sharing and the impacts on health organizations and patients given the increased needs to share clinical information.  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 theme of my research is leveraging, creating and using data to assess society (community) needs and the intersection of disparities which exist as along an “implications” continuum.  While much of the data created and used is a direct result of embedded notions of “systems” informing a direct outcome the coding, coded and counting (Web of Cs) does not exist in a vacuum.  Rather, the coding and coded influence a counting of decisions used that impact lived experiences.  For Black and Brown communities, this web of Cs informed algorithmic bias while overlooking factors including context, place and space.



Faye CobbDr. Fay Cobb Payton is a Full Professor (with Tenure) of Information Technology/Analytics at North Carolina State University and was named a University Faculty Scholar for her leadership in turning research into solutions to society’s most pressing issues. She completed the American Council on Education Fellows (leadership) program.  Her research examines AI fairness/bias, data curation, artifact design, healthcare disparities, tech leadership inclusion/exclusion, innovation & entrepreneurship.  She is currently on assignment as a Program Director at the National Science Foundation.

Prior to joining academe, she worked in corporate IT, systems engineering and consulting at IBM, EY and Time, Inc. She received the PhD Project Hall of Fame, North Carolina Technology Association Tech Educator of the Year, and SAS Teaching Fellow Awards.  She is a full member of Sigma Xi, Association of Computing Machinery (ACM) Education Advisory Board & chairs its Diversity and Inclusion Committee; Member of the Institute of Industrial & Systems Engineers (IISE) and Member of its Health Systems & Diversity-Equity-Inclusion Committees. Dr. Payton serves on the American Council on Education’s Leadership Council of Fellows Board. She has been featured in Essence Magazine, Forbes AI 50 Start-ups, Thomson Reuters Foundation and AFI Sliver Theatre on her work on the impacts of AI bias and fairness. Dr. Payton is the author of over 100 peer-reviewed journal articles, conference publications and book chapters.  She is the author of Leveraging Intersectionality: Seeing and Not Seeing (Richer Press), an anthology of her research on STEM education and experiences in both academe and corporate environments. She earned her BS in Industrial & Systems Engineering from Georgia Institute of Technology and BA in Accounting (with a minor in Mathematics) from Clark Atlanta University.  She earned an MBA in Decision Sciences from Clark Atlanta University and a Ph.D. in Information & Decision Systems from Case Western Reserve University.