Usability of Paper Audit Trails in Electronic Voting Machines
Bibliographic record
Abstract
Electronic voting machines (EVM) can improve the efficiency of elections. However, due to the possibility of errors in electronic voting machines, user trust in them is an issue. To improve user trust, one mechanism used by EVMs is the inclusion of a paper audit trail, which shows users a paper receipt of their vote for 7-seconds and asks them to verify their vote. While paper audit trails can theoretically improve user trust, their usability has not yet been explored, which can affect user trust. In this paper, we evaluate the usability of paper audit trails by creating two UI prototypes and testing them with users through a user study. The design of the first prototype reflected existing audit trail systems, whereas the second prototype was created using HCI design principles. Results showed that the second prototype improved error recognition rates compared to the first prototype. Post-test interviews showed that the second prototype also reduced users’ stress and anxiety of the voting process. Our work highlights the importance of exploring the human aspect in the design of electronic voting machines, and their associated components such as paper audit trails. It also provides insights into users’ overall perceptions of electronic voting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".