4 papers
Sublinear Risk-Limiting Audits from Direct Ballot Selection and Statistical Ballot Manifests
Benjamin Fuller, Abigail Harrison, Alexander Russell
Risk-limiting audits (RLAs) rigorously guarantee a specified maximum probability that an incorrect electoral outcome will not be detected. Efficient RLA methods require a software-…
Analyzing Physical Adversarial Example Threats to Machine Learning in Election Systems
Khaleque Md Aashiq Kamal, Surya Eada, Aayushi Verma +4
Developments in the machine learning voting domain have shown both promising results and risks. Trained models perform well on ballot classification tasks (> 99% accuracy) but are…
Busting the Paper Ballot: Voting Meets Adversarial Machine Learning
Kaleel Mahmood, Caleb Manicke, Ethan Rathbun +5
We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is decidin…
The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording
Benjamin Fuller, Rashmi Pai, Alexander Russell
Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. While they provide rigorous guarantees of correctness, widespread adoption has been impede…