Bachelor of Arts, University of Central Florida, 2010
Master of Arts, American University, 2013
Artificial Neural Networks in Public Policy: A Framework for Effective Implementation
Thursday, January 17, 2019, 2:00pm-4:00pm
720 Founders Hall, Arlington Campus
All are invited to attend.
Laurie Schintler, Chair
This dissertation will generate an analytical framework for public policy scholars and practitioners to utilize when implementing artificial neural networks (ANNs). It will explore how issues such as explanatory power, bias, accountability, privacy, security, and maintainability intersect with ANNs utilized in public policy settings. While ANNs may provide unprecedented levels of predictive accuracy compared to previous machine learning techniques, we still don’t understand precisely why ANNs work. To determine these intersections and generate the initial analytical framework, this research will incorporate both quantitative and qualitative methods including case study analysis, experimental research, and content mining. The analytical framework will then be further refined through expert interviews and peer review.
A copy of this doctoral dissertation proposal is available for examination from Shannon Williams, Schar School of Policy and Government.