The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording

• URL: http://arxiv.org/abs/2402.06515v4
• Date: Mon, 17 Jun 2024 18:04:22 GMT
• Title: The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording
• Authors: Benjamin Fuller, Rashmi Pai, Alexander Russell,
• Abstract summary: Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. We define new families of audits that improve efficiency and offer advances in statistical power. New audits are enabled by revisiting the standard notion of a cast-vote record so that it can declare multiple possible mark interpretations.
• Score: 51.82772358241505
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. While they provide rigorous guarantees of correctness, widespread adoption has been impeded by both efficiency concerns and the fact they offer statistical, rather than absolute, conclusions. We attend to both of these difficulties, defining new families of audits that improve efficiency and offer qualitative advances in statistical power. Our new audits are enabled by revisiting the standard notion of a cast-vote record so that it can declare multiple possible mark interpretations rather than a single decision; this can reflect the presence of marginal marks, which appear regularly on hand-marked ballots. We show that this simple expedient can offer significant efficiency improvements with only minor changes to existing auditing infrastructure. We consider two ways of representing these marks, both yield risk-limiting comparison audits in the formal sense of Fuller, Harrison, and Russell (IEEE Security & Privacy 2023). We then define a new type of post-election audit we call a contested audit. These permit each candidate to provide a cast-vote record table advancing their own claim to victory. We prove that these audits offer remarkable sample efficiency, yielding control of risk with a constant number of samples (that is independent of margin). This is a first for an audit with provable soundness. These results are formulated in a game-based security model that specify quantitative soundness and completeness guarantees. These audits provide a means to handle contestation of election results affirmed by conventional RLAs.

Related papers

• Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes [50.37313459134418]
  We study the effects of inference error on auditing for bias in one prominent application: black-box audit of ad delivery using paired ads. We propose a way to mitigate the inference error when evaluating skew in ad delivery algorithms.
  arXiv  Detail & Related papers  (2024-10-30T18:57:03Z)
• Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections [57.67176250198289]
  AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates.
  arXiv  Detail & Related papers  (2024-02-18T10:13:01Z)
• A Brief Tutorial on Sample Size Calculations for Fairness Audits [6.66743248310448]
  This tutorial provides guidance on how to determine the required subgroup sample sizes for a fairness audit. Our findings are applicable to audits of binary classification models and multiple fairness metrics derived as summaries of the confusion matrix.
  arXiv  Detail & Related papers  (2023-12-07T22:59:12Z)
• TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance [8.924352407824566]
  Federated learning (FL) enables collaborative learning among multiple clients while ensuring individual data privacy. In this paper, we propose a hierarchical audit-based FL (HiAudit-FL) framework to enhance the reliability and security of the learning process. Our simulation results demonstrate that HiAudit-FL can effectively identify and handle potential malicious users accurately, with small system overhead.
  arXiv  Detail & Related papers  (2023-12-06T13:56:45Z)
• Equal Opportunity of Coverage in Fair Regression [50.76908018786335]
  We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. We propose Equal Opportunity of Coverage (EOC) that aims to achieve two properties: (1) coverage rates for different groups with similar outcomes are close, and (2) the coverage rate for the entire population remains at a predetermined level.
  arXiv  Detail & Related papers  (2023-11-03T21:19:59Z)
• Binary Classification with Confidence Difference [100.08818204756093]
  This paper delves into a novel weakly supervised binary classification problem called confidence-difference (ConfDiff) classification. We propose a risk-consistent approach to tackle this problem and show that the estimation error bound the optimal convergence rate. We also introduce a risk correction approach to mitigate overfitting problems, whose consistency and convergence rate are also proven.
  arXiv  Detail & Related papers  (2023-10-09T11:44:50Z)
• Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE [61.872917066847855]
  Methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. We develop an RLA method that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available.
  arXiv  Detail & Related papers  (2023-07-20T15:55:34Z)
• New Algorithms and Applications for Risk-Limiting Audits [4.375873233252245]
  Risk-limiting audits (RLAs) are a significant tool in increasing confidence in the accuracy of elections. This work suggests a new generic method, called Batchcomp", for converting classical (ballot-level) RLAs into ones that operate on batches. We present an adaptation of ALPHA, an existing RLA method, to a method which applies to censuses.
  arXiv  Detail & Related papers  (2023-05-06T13:34:39Z)
• Auditing Ranked Voting Elections with Dirichlet-Tree Models: First Steps [23.14629947453497]
  Ranked voting systems are used in many places around the world. There is no known risk-limiting audit (RLA) method for STV other than a full hand count. We present a new approach to auditing ranked systems that uses a statistical model, a Dirichlet-tree, that can cope with high-dimensional parameters in a computationally efficient manner.
  arXiv  Detail & Related papers  (2022-06-29T13:06:42Z)
• Individually Fair Learning with One-Sided Feedback [15.713330010191092]
  We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances. On each round, $k$ instances arrive and receive classification outcomes according to a randomized policy deployed by the learner. We then construct an efficient reduction from our problem of online learning with one-sided feedback and a panel reporting fairness violations to the contextual semi-bandit problem.
  arXiv  Detail & Related papers  (2022-06-09T12:59:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.