It Takes So Little to Change So Much: Investigating the Robustness of a Danish Voting Advice Algorithm

• URL: http://arxiv.org/abs/2603.03532v1
• Date: Tue, 03 Mar 2026 21:35:01 GMT
• Title: It Takes So Little to Change So Much: Investigating the Robustness of a Danish Voting Advice Algorithm
• Abstract summary: We get access to the inner workings of a popular Danish VAA called the Kandidattest.<n>VAAs are popular tools in European countries and in other countries with multi-party democratic systems.<n>We find that the algorithm is not robust enough for users to trust the agreement percentages in the output.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Voting Advice Applications (VAA) are tools designed to help voters compare political candidates on policy preferences prior to elections. VAAs are popular tools in European countries and in other countries with multi-party democratic systems. Through a freedom of information request we got access to the inner workings of a popular Danish VAA called the Kandidattest which is implemented by major Danish news outlet and has been used for general, municipal, and European elections. Users and politicians from every political party answer the same online questionnaire and get matched based on the agreement percentage stemming from their answers. VAAs play a significant role in elections with 45% of surveyed voters reporting they followed its recommendations in the past Danish general election, however, the inner workings of VAAs have not been thoroughly evaluated. We find that the algorithm is not robust enough for users to trust the agreement percentages in the output, as small changes to the algorithm can lead to different results, potentially affecting election results. We conduct an algorithmic audit of the Kandidattest's robustness, using simulated responses to investigate the tool's brittleness, with respect to minor adjustments of the algorithm's weight, and changes in the number of questions of the questionnaire.

Related papers

• Algorithms for Structured Elections under Thiele Voting Rules [50.74668888214481]
  We study the computational complexity of winner determination problems in approval-based committee elections under Thiele voting rules.<n>We first analyze the structure of optimal solutions based on the sets of voters who approve each candidate.<n>We show that every Thiele rule on VI is FPT with respect to a parameter for which the problem is NP-hard on general instances.
  arXiv  Detail & Related papers  (2026-07-30T17:37:14Z)
• Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications [18.95453617434051]
  Voting advice applications (VAAs) help millions of voters understand which political parties or candidates best align with their views.<n>This paper explores the potential risks these applications pose to the democratic process when targeted by adversarial entities.
  arXiv  Detail & Related papers  (2025-05-19T16:38:06Z)
• A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany [41.972629376586035]
  We examine the reliability of AI-based Voting Advice Applications (VAAs) and large language models (LLMs) in providing objective political information.<n>Our analysis is based upon a comparison with party responses to 38 statements of the Wahl-O-Mat.
  arXiv  Detail & Related papers  (2025-02-21T16:30:53Z)
• Improving the Computational Efficiency of Adaptive Audits of IRV Elections [54.427049258408424]
  AWAIRE can audit IRV contests with any number of candidates, but the original implementation incurred memory and computation costs that grew superexponentially with the number of candidates. This paper improves the algorithmic implementation of AWAIRE in three ways that make it practical to audit IRV contests with 55 candidates, compared to the previous 6 candidates.
  arXiv  Detail & Related papers  (2024-07-23T13:28:00Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Employing Universal Voting Schemes for Improved Visual Place Recognition Performance [16.242139178368923]
  This paper analyses several voting schemes to maximise the place detection accuracy of a VPR ensemble set up. We take inspiration from a variety of voting schemes that are widely employed in fields such as politics and sociology.
  arXiv  Detail & Related papers  (2024-03-08T14:19:14Z)
• 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)
• 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)
• Anomaly Detection and Automated Labeling for Voter Registration File Changes [0.0]
  Voter eligibility in United States elections is determined by a patchwork of state databases containing information about which citizens are eligible to vote. Monitoring changes to Voter Registration Files (VRFs) is crucial, given that a malicious actor wishing to disrupt the democratic process in the US would be well-advised to manipulate the contents of these files in order to achieve their goals. We present a set of methods that make use of machine learning to ease the burden on analysts and administrators in protecting voter rolls.
  arXiv  Detail & Related papers  (2021-06-16T21:48:31Z)
• The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines [68.8204255655161]
  We look at the text search results for "us elections", "donald trump", "joe biden" and "bernie sanders" queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents.
  arXiv  Detail & Related papers  (2021-05-03T11:18:19Z)
• Bribery as a Measure of Candidate Success: Complexity Results for Approval-Based Multiwinner Rules [58.8640284079665]
  We study the problem of bribery in multiwinner elections, for the case where the voters cast approval ballots (i.e., sets of candidates they approve) We consider a number of approval-based multiwinner rules (AV, SAV, GAV, RAV, approval-based Chamberlin--Courant, and PAV) In general, our problems tend to be easier when we limit out bribery actions on increasing the number of approvals of the candidate that we want to be in a winning committee.
  arXiv  Detail & Related papers  (2021-04-19T08:26:40Z)

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.