Papers › Overstatement-Net-Equivalent Risk-Limiting Audit: ONEAudit
Overstatement-Net-Equivalent Risk-Limiting Audit: ONEAudit
Philip B. Stark
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Card-level comparison risk-limiting audits (CLCAs) heretofore required a CVR for each cast card and a "link" identifying which CVR is for which card -- which many voting systems cannot provide. Every set of CVRs that produces the same aggregate results overstates contest margins by the same amount: they are overstatement-net-equivalent (ONE). CLCAs can therefore use CVRs from the voting system for any number of cards and ONE CVRs for the rest. Ballot-polling RLAs are equivalent to CLCAs using ONE CVRs. CLCAs can be based on batch-level results (e.g., precinct subtotals) by constructing ONE CVRs for each batch. In contrast to batch-level comparison audits (BLCAs), this avoids tabulating batches manually and works even when reporting batches do not correspond to physically identifiable batches of cards. If the voting system can export linked CVRs for only some ballot cards, auditors can still use CLCA by constructing ONE CVRs for the rest of the cards from contest results or batch subtotals. This obviates the need for "hybrid" audits. This works for every social choice function for which there is a known RLA method, including IRV. Sample sizes for BPA and ONEAudit using contest totals are comparable. ONEAudit using batch subtotals has smaller sample sizes than ballot-polling when batches are much more homogeneous than the election overall. Sample sizes can be much smaller than for BLCA: A CLCA of the 2022 presidential election in California at risk limit 5% using ONE CVRs for precinct-level results would sample ~70 ballots statewide, if the reported results are accurate, compared to about 26,700 for BLCA. The 2022 Georgia audit tabulated >231,000 cards versus ~1300 for ONEAudit. For data from a pilot hybrid RLA in Kalamazoo, MI, in 2018, ONEAudit gives a risk of ~2%, substantially lower than the 3.7% measured risk for SUITE, the method the pilot used.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections