Title:
Evidence-Based Elections
Evidence-Based Elections
dc.contributor.author | Stark, Philip B. | |
dc.contributor.corporatename | Georgia Institute of Technology. Institute for Information Security & Privacy | en_US |
dc.contributor.corporatename | University of California, Berkeley. Dept. of Statistics | en_US |
dc.date.accessioned | 2022-02-14T18:44:08Z | |
dc.date.available | 2022-02-14T18:44:08Z | |
dc.date.issued | 2022-02-04 | |
dc.description | Presented online via Bluejeans Events and in-person in the CODA building, 9th floor on February 4, 2022 at 12:30 p.m. | en_US |
dc.description | Philip B. Stark is a Distinguished Professor of Statistics at the University of California, Berkeley. His research centers on inference problems, primarily in physical and social sciences. He is especially interested in confidence procedures tailored for specific goals and in quantifying the uncertainty in inferences that rely on numerical models of complex systems | en_US |
dc.description | Runtime: 71:57 minutes | en_US |
dc.description.abstract | Elections rely on people, hardware, and software, all of which are fallible and subject to manipulation. Voting equipment is built by private vendors using foreign parts. Many states outsource election results reporting. Advanced persistent threats and insider threats are real. Uncertainty about the outcome of elections has been weaponized politically recently. We need to conduct elections in a way that provides affirmative evidence that the reported winners really won–despite malfunctions, errors, and malfeasance. Evidence-based elections require voter-verified (generally, hand-marked) paper ballots kept demonstrably secure throughout the canvass and manual audits of election results against the trustworthy paper trail. Compliance audits establish whether the paper trail is complete and trustworthy. Risk-limiting audits (RLAs) check the outcome by testing the hypothesis that one or more reported winners did not win. For a broad variety of social choice functions, including plurality, multi-winner plurality, supermajority, Borda count, approval voting, all scoring rules, instant-runoff voting (ranked-choice voting), and D'Hondt and Hamiltonian proportional representation, the hypothesis that at least one reported winner did not win can be reduced to the hypothesis that the mean of one or more lists of nonnegative numbers is not greater than 1/2. Martingale tests of these nonparametric hypotheses sequentially are especially practical. Methods to accommodate different sampling plans, equipment capability, logistical constraints, and laws and regulations have been developed and piloted in more than a dozen states in jurisdictions of all sizes, including roughly 10 audits of statewide contests. RLAs are in law in several states. | en_US |
dc.format.extent | 71:57 minutes | |
dc.identifier.uri | http://hdl.handle.net/1853/66285 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.relation.ispartofseries | Cybersecurity Lecture Series | |
dc.subject | Election integrity | en_US |
dc.subject | Martingale tests | en_US |
dc.subject | Risk-limiting election audits | en_US |
dc.subject | Statistics | en_US |
dc.title | Evidence-Based Elections | en_US |
dc.type | Moving Image | |
dc.type.genre | Lecture | |
dspace.entity.type | Publication | |
local.contributor.corporatename | School of Cybersecurity and Privacy | |
local.contributor.corporatename | College of Computing | |
local.relation.ispartofseries | Institute for Information Security & Privacy Cybersecurity Lecture Series | |
relation.isOrgUnitOfPublication | f6d1765b-8d68-42f4-97a7-fe5e2e2aefdf | |
relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
relation.isSeriesOfPublication | 2b4a3c7a-f972-4a82-aeaa-818747ae18a7 |
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