Meghan Cook
AI Modeling and Visualizations for Anomaly Detection in NYS’s Voter Registration Data: Informing NYS Board of Elections Next Generation Enterprise Investments
Voter registration has been recognized across the country as one key source of cybersecurity vulnerability in election processes.
Aware of the advertised potential of artificial intelligence (AI) driven data analytics, NYS Board of Elections asked the Center for Technology in Government to explore AI techniques to monitor voter registration data; specifically going beyond current efforts to detect errors in the data to identify patterns and anomalies in NYS’s voter registration database.
CTG UAlbany led an interdisciplinary team of social and computer science researchers, faculty, and students from CTG UAlbany, the College of Engineering and Applied Sciences, and the College of Homeland Security and Emergency Preparedness and Cybersecurity in stakeholder needs assessment, data forensics, statistical and AI modeling, and development of visualizations of voter registrations data.
Producing prototypes designed to build a shared understanding among state and county election leaders, the UAlbany team helped inform NYSBOE’s future investments in AI solutions.