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

Glazer, Amanda; Spertus, Jacob; Stark, Philip B.

doi:10.1145/3457907

Cited by 5 publications

(2 citation statements)

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“…The domain of assorter j is Dj, which could comprise all ballot cards cast in the election or a smaller set, provided Dj includes every card that contains the contest that the assorter Aj is relevant for. Targeting audit sampling using information about which ballot cards purport to contain which contests (card style data) can vastly improve audit efficiency while rigorously maintaining the risk limit even if the voting system misidentifies which cards contain which contests [6]. There are also techniques for dealing with missing ballot cards [4,19].…”

Section: Pseudo-algorithm For Ballot-level Comparison and Ballot-poll...mentioning

confidence: 99%

ALPHA: Audit that Learns from Previously Hand-Audited Ballots

Stark¹

2022

Preprint

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A risk-limiting election audit (RLA) offers a statistical guarantee: if the reported electoral outcome is incorrect, the audit has at most a known maximum chance (the risk limit) of not correcting it before it becomes final. BRAVO [10], based on Wald's sequential probability ratio test for the Bernoulli distribution, is the most widely tried method for RLAs. It has limitations. It cannot accommodate sampling without replacement or stratified sampling, which can improve efficiency and are sometimes required by law. It applies only to ballot-polling audits, which are less efficient than comparison audits. It applies to plurality, majority, super-majority, proportional representation, and ranked-choice voting contests, but not to many other social choice functions for which there are RLA methods, such as approval voting, STAR-voting, Borda count, and general scoring rules. And while BRAVO has the smallest expected sample size among sequentially valid ballot-polling-with-replacement methods when the reported vote shares are exactly correct, BRAVO can require arbitrarily large samples when the reported reported winner(s) really won but the reported vote shares are incorrect. ALPHA is a simple generalization of BRAVO that (i) works for sampling with and without replacement; (ii) can be used with stratified sampling; (iii) works not only for ballot-polling but also for ballot-level comparison, batch-polling, and batch-level comparison audits, sampling with or without replacement, uniformly or with weights proportional to a measure of size; (iv) works for all social choice functions covered by SHANGRLA [19], including approval voting, Borda count, and all scoring rules; and (v) in situations where both ALPHA and BRAVO apply, requires smaller samples than BRAVO when the reported vote shares are wrong but the outcome is correct-five orders of magnitude in some examples. AL-PHA includes the family of betting martingale tests in RiLACS [27], with a different betting strategy parametrized as an estimator of the population mean and the flexibility to accommodate sampling weights and population bounds that change with each draw. A Python implementation is provided.

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Section: Pseudo-algorithm For Ballot-level Comparison and Ballot-poll...mentioning

confidence: 99%

ALPHA: Audit that Learns from Previously Hand-Audited Ballots

Stark¹

2022

Preprint

Self Cite

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“…2.1 SHANGRLA assorters [14] shows that RLAs can be reduced to statistical tests of whether the averages of a collection of finite lists of nonnegative, bounded numbers are all greater than 1/2. Each list results from applying an "assorter" A to the votes in a contest for each validly cast card that contains the contest (for audits that use style data, as described in [5]) or for all validly cast cards. The number of assorters involved in auditing a contest depends on the social choice function for that contest, among other things.…”

Section: Assumptionsmentioning

confidence: 99%

Non(c)esuch Ballot-Level Risk-Limiting Audits for Precinct-Count Voting Systems

Stark¹

2022

Preprint

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Risk-limiting audits (RLAs) guarantee a high probability of correcting incorrect reported outcomes before the outcomes are certified. The most efficient RLAs use ballot-level comparison, comparing the voting system's interpretation of individual ballot cards sampled at random (cast-vote records, CVRs) from a trustworthy paper trail to a human interpretation of the same cards. Such comparisons require the voting system to create and export CVRs in a way that can be linked to the individual ballots the CVRs purport to represent. Such links can be created by keeping the ballots in the order in which they are scanned or by printing a unique serial number on each ballot. But for precinct-count systems (PCOS), these strategies may compromise vote anonymity: the order in which ballots are cast may identify the voters who cast them. Printing a unique pseudo-random number ("cryptographic nonce") on each ballot card after the voter last touches it could reduce such privacy risks. But what if the system does not in fact print a unique number on each ballot or does not accurately report the numbers it printed? This paper gives two ways to conduct an RLA so that even if the system does not print a genuine nonce on each ballot or misreports the nonces it used, the audit's risk limit is not compromised (however, the anonymity of votes might be compromised). One method allows untrusted technology to be used to imprint and to retrieve ballot cards. The method is adaptive: if the technology behaves properly, this protection does not increase the audit workload. But if the imprinting or retrieval system misbehaves, the sample size the RLA requires to confirm the reported results when the results are correct is generally larger than if the imprinting and retrieval were accurate. If the reported outcome is incorrect, the audit still limits the risk conservatively, whether the imprinting and retrieval technology misbehaved or not.

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Non(c)esuch Ballot-Level Comparison Risk-Limiting Audits

Stark

2023

Lecture Notes in Computer Science

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

Cited by 5 publications

References 5 publications

ALPHA: Audit that Learns from Previously Hand-Audited Ballots

ALPHA: Audit that Learns from Previously Hand-Audited Ballots

Non(c)esuch Ballot-Level Risk-Limiting Audits for Precinct-Count Voting Systems

Non(c)esuch Ballot-Level Comparison Risk-Limiting Audits

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