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1. UnclearBallot: Automated Ballot Image Manipulation

   Matthew Bernhard, Kartikeya Kandula ( October 2021 , Proc. 4th International Joint Conference on Electronic Voting (E-Vote-ID ’19))

   null (Ed.)

   As paper ballots and post-election audits gain increased adoption in the United States, election technology vendors are offering products that allow jurisdictions to review ballot images—digital scans produced by optical-scan voting machines—in their post-election audit procedures. Jurisdictions including the state of Maryland rely on such image audits as an alternative to inspecting the physical paper ballots. We show that image audits can be reliably defeated by an attacker who can run malicious code on the voting machines or election management system. Using computer vision techniques, we develop an algorithm that automatically and seamlessly manipulates ballot images, moving voters’ marks so that they appear to be votes for the attacker’s preferred candidate. Our implementation is compatible with many widely used ballot styles, and we show that it is effective using a large corpus of ballot images from a real election. We also show that the attack can be delivered in the form of a malicious Windows scanner driver, which we test with a scanner that has been certified for use in vote tabulation by the U.S. Election Assistance Commission. These results demonstrate that post-election audits must inspect physical ballots, not merely ballot images, if they are to strongly defend against computer-based attacks on widely used voting systems. 

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2. Ballot Length in Instant Runoff Voting

   https://doi.org/10.1609/aaai.v37i5.25724  

   Tomlinson, Kiran ; Ugander, Johan ; Kleinberg, Jon ( June 2023 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Instant runoff voting (IRV) is an increasingly-popular alternative to traditional plurality voting in which voters submit rankings over the candidates rather than single votes. In practice, elections using IRV often restrict the ballot length, the number of candidates a voter is allowed to rank on their ballot. We theoretically and empirically analyze how ballot length can influence the outcome of an election, given fixed voter preferences. We show that there exist preference profiles over k candidates such that up to k-1 different candidates win at different ballot lengths. We derive exact lower bounds on the number of voters required for such profiles and provide a construction matching the lower bound for unrestricted voter preferences. Additionally, we characterize which sequences of winners are possible over ballot lengths and provide explicit profile constructions achieving any feasible winner sequence. We also examine how classic preference restrictions influence our results—for instance, single-peakedness makes k-1 different winners impossible but still allows at least Ω(√k). Finally, we analyze a collection of 168 real-world elections, where we truncate rankings to simulate shorter ballots. We find that shorter ballots could have changed the outcome in one quarter of these elections. Our results highlight ballot length as a consequential degree of freedom in the design of IRV elections. 

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3. Boardroom Voting: Verifiable Voting with Ballot Privacy Using Low-Tech Cryptography in a Single Room

   Blanchard, Nikola K. ; Selker, Ted ; Sherman, Alan T. ( October 2020 , IEEE Transactions on Systems, Man and Cybernetics: Systems, submitted)

   A boardroom election is an election that takes place in a single room — the boardroom — in which all voters can see and hear each other. We present an initial exploration of boardroom elections with ballot privacy and voter verifiability that use only “low-tech cryptography” without using computers to mark or collect ballots. Specifically, we define the problem, introduce several building blocks, and propose a new protocol that combines these blocks in novel ways. Our new building blocks include “foldable ballots” that can be rotated to hide the alignment of ballot choices with voting marks, and “visual secrets” that are easy to remember and use but hard to describe. Although closely seated participants in a boardroom election have limited privacy, the protocol ensures that no one can determine how others voted. Moreover, each voter can verify that their ballot was correctly cast, collected, and counted, without being able to prove how they voted, providing assurance against undue influence. Low-tech cryptography is useful in situations where constituents do not trust computer technology, and it avoids the complex auditing requirements of end-to-end cryptographic voting systems such as Prêt-à-Voter. This paper’s building blocks and protocol are meant to be a proof of concept that might be tested for usability and improved. 

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4. More Style, Less Work: Card-style Data Decrease Risk-limiting Audit Sample Sizes

   https://doi.org/10.1145/3457907  

   Glazer, Amanda K. ; Spertus, Jacob V. ; Stark, Philip B. ( December 2021 , Digital Threats: Research and Practice)

   U.S. elections rely heavily on computers such as voter registration databases, electronic pollbooks, voting machines, scanners, tabulators, and results reporting websites. These introduce digital threats to election outcomes. Risk-limiting audits (RLAs) mitigate threats to some of these systems by manually inspecting random samples of ballot cards. RLAs have a large chance of correcting wrong outcomes (by conducting a full manual tabulation of a trustworthy record of the votes), but can save labor when reported outcomes are correct. This efficiency is eroded when sampling cannot be targeted to ballot cards that contain the contest(s) under audit. If the sample is drawn from all cast cards, then RLA sample sizes scale like the reciprocal of the fraction of ballot cards that contain the contest(s) under audit. That fraction shrinks as the number of cards per ballot grows (i.e., when elections contain more contests) and as the fraction of ballots that contain the contest decreases (i.e., when a smaller percentage of voters are eligible to vote in the contest). States that conduct RLAs of contests on multi-card ballots or RLAs of small contests can dramatically reduce sample sizes by using information about which ballot cards contain which contests—by keeping track of card-style data (CSD). For instance, CSD reduce the expected number of draws needed to audit a single countywide contest on a 4-card ballot by 75%. Similarly, CSD reduce the expected number of draws by 95% or more for an audit of two contests with the same margin on a 4-card ballot if one contest is on every ballot and the other is on 10% of ballots. In realistic examples, the savings can be several orders of magnitude. 

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5. Modeling Voters in Multi-Winner Approval Voting

   Scheuerman, Jaelle ; Harman, Jason ; Mattei, Nicholas ; Venable, K. Brent ( April 2021 , Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021)

   In many real world situations, collective decisions are made using voting and, in scenarios such as committee or board elections, employing voting rules that return multiple winners. In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-k candidates receiving the most approvals. In many scenarios, an agent may manipulate the ballot they submit in order to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty using behavioral data obtained from Mechanical Turk. We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. There are a number of predictive models of agent behavior in the social choice and psychology literature that are based on cognitively plausible heuristic strategies. We show that the existing approaches do not adequately model our real-world data. We propose a novel model that takes into account the size of the winning set and human cognitive constraints; and demonstrate that this model is more effective at capturing real-world behaviors in multi-winner approval voting scenarios. 

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