# RiLACS: Risk-Limiting Audits via Confidence Sequences

arXiv: https://arxiv.org/abs/2107.11323 · HTML: https://mlsanity.com/paper/2107.11323/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2021.

## Built on by (most cited first, 6 of 6)

- [Auditing Fairness by Betting](https://mlsanity.com/paper/2305.17570/) (2023)
- [Probably Approximately Correct Labels](https://mlsanity.com/paper/2506.10908/) (2025)
- [Risk-limiting Financial Audits via Weighted Sampling without Replacement](https://mlsanity.com/paper/2305.06884/) (2023)
- [Sequentially Auditing Differential Privacy](https://mlsanity.com/paper/2509.07055/) (2025)
- [Optimal Rates for Differentially Private Hypothesis Testing with E-values](https://mlsanity.com/paper/2605.28952/) (2026)
- [Sequential Audit Sampling for Finite Populations with Exact and Simulation-based Guarantee](https://mlsanity.com/paper/2604.06116/) (2026)

## Abstract (arXiv)

Accurately determining the outcome of an election is a complex task with many potential sources of error, ranging from software glitches in voting machines to procedural lapses to outright fraud. Risk-limiting audits (RLA) are statistically principled "incremental" hand counts that provide statistical assurance that reported outcomes accurately reflect the validly cast votes. We present a suite of tools for conducting RLAs using confidence sequences -- sequences of confidence sets which uniformly capture an electoral parameter of interest from the start of an audit to the point of an exhaustive recount with high probability. Adopting the SHANGRLA framework, we design nonnegative martingales which yield computationally and statistically efficient confidence sequences and RLAs for a wide variety of election types.