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Journal of Science Policy & Governance | Volume 17, Issue 01 | September 30, 2020

Policy Memo: ​Securing Election Infrastructure with Hand-Marked Paper Ballots

Varun Gupta, Joel Hypolite, Stephen Mell, Hersh Sanghvi
  1. ​University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA
  2. University of Pennsylvania, Penn Science Policy and Diplomacy Group, Philadelphia, PA
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https://doi.org/10.38126/JSPG170106
Keywords: voting machines; election security; critical infrastructure; Congress; democracy
Executive Summary: American democracy is critically threatened by the use of insecure voting systems. Many existing electronic voting machines have malfunctioned during recent elections, and many are also vulnerable to hacking (Appel et al. 2019, Blaze et al. 2019, Blaze 2020). Most states have switched to secure, hand-marked paper ballots, but roughly 30% of Americans will continue to vote using vulnerable voting machines in 2020 (Cordova et al. 2019, Bajak 2020). While Congress allocated $380 million in 2018 and $425 million in 2020 to improve election security, these funds were neither targeted at nor sufficient for replacing all electronic voting machines. We propose that Congress (1) allocate $110 million exclusively for transitioning away from electronic voting machines and (2) prohibit the use of federal funds for purchasing voting systems that do not primarily use hand-marked paper ballots. This one-time transition cost is much smaller than even annual expenditures on other critical infrastructure (Copeland 2010; Halderman 2019). Replacing electronic voting machines with hand-marked paper ballots is the most affordable and secure option.

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Varun Gupta is a Ph.D. student in Computer Science at the University of Pennsylvania. He is interested in machine learning and technology policy.
 
Joel Hypolite is a Ph.D. candidate in Computer Science at the University of Pennsylvania. He received his BS in Computer Science from the University of Notre Dame. His research interests are broadly in the areas of networking, security, and artificial intelligence.
 
Stephen Mell is a Ph.D. student in Computer Science at the University of Pennsylvania. He received his Bachelor of Arts in Mathematics at Washington University in St. Louis. His research interests lie at intersection between computational learning and formal logic.
 
Hersh Sanghvi is a Ph.D. student in Computer Science at the University of Pennsylvania. He received his B.S in Electrical and Computer Engineering from the University of California, Berkeley in 2019. His research interests lie in the areas of robotic perception and control.
DISCLAIMER: The findings and conclusions published herein are solely attributed to the author and not necessarily endorsed or adopted by the Journal of Science Policy and Governance. Articles are distributed in compliance with copyright and trademark agreements.

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