Summative Usability Assessments of STAR-Vote: A Cryptographically Secure e2e Voting System That Has Been Empirically Proven to Be Easy to Use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: From the project's inception, STAR-Vote was intended to be one of the first usable, end-to-end (e2e) voting systems with sophisticated security. To realize STAR-Vote, computer security experts, statistical auditors, human factors (HF)/human-computer interaction (HCI) researchers, and election officials collaborated throughout the project and relied upon a user-centered, iterative design and development process, which included human factors research and usability testing, to make certain the system would be both usable and secure. OBJECTIVE: While best practices in HF/HCI methods for design were used and all apparent usability problems were identified and fixed, summative system usability assessments were conducted toward the end of the user-centered design process to determine whether STAR-Vote is in fact easy to use. METHOD AND RESULTS: After collecting efficiency, effectiveness, and satisfaction measurements per ISO 9241-11's system usability criteria, an analysis of the data revealed that there is evidence for STAR-Vote being the most usable, cryptographically secure voting system to date when compared with the previously tested e2e systems: Helios, Prêt à Voter, and Scantegrity. CONCLUSION AND APPLICATION: is a significant accomplishment, because tamper-resistant voting systems can be used in U.S. elections to ensure the integrity of the electoral process, while still ensuring that voter intent is accurately reflected in the cast ballots. Moreover, this research empirically shows that a complex, secure system can still be usable-meaning that implemented security is not an excuse for poor usability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it