Improved Support for Machine-assisted Ballot-level Audits
Eric S. Kim, Nicholas Carlini, Andrew Chang, George Yiu, Kai Wang, David Wagner · 2013
This paper studies how to provide support for ballot-level post-election audits. Informed by our work supporting pilots of these audits in several California counties, we identify gaps in current technology in tools for this task: we need better ways to count voted ballots (from scanned images) without access to scans of blank, unmarked ballots; and we need improvements to existing techniques that help them scale better to large, complex elections. We show how to meet these needs and use our system to successfully process ballots from 11 California counties, in support of the pilot audit program. Our new techniques yield order-of-magnitude speedups compared to the previous system, and enable us to successfully process some elections that would not have reasonably feasible without these techniques. 1.