APPLYING MACHINE LEARNING IN THE U.S. POLITICAL LANDSCAPE: FORECASTING, POLLING, AND THE DOMESTIC SUPPLY CHAIN

Abstract

There are areas of the current U.S. political system that are fit for the implementation of machine learning capabilities. Some of these areas include election forecasting, polling, and vote-by-mail services. The introduction of machine learning tools to the components of the U.S. political system can result in increases in efficiency and accessibility throughout the sector. By doing so, there would be an assumed decrease in necessary labor. This research analyzed the specified areas of U.S. politics by establishing a four-step framework based on a previous exploratory case study into the application of machine learning to operations management and adjusted said framework to fit the needs of the nature of U.S. politics. This framework was utilized to determine the objectives to which machine learning would be applied within this sector. As well, it serves to guide the technology or strategies necessary to achieve those objectives, illustrate the effect on performance and on stakeholders. This analysis introduces valuable insights for any decision makers as well as providing an accessible and informative review of some of the potential applications of machine learning in the existing U.S. political system.