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.

翻译：投票建议应用（VAA）是旨在帮助选民在选举前根据政策偏好比对政治候选人的工具。VAA在欧洲国家及其他多党民主制国家中广受欢迎。通过信息公开请求，我们获取了丹麦主流新闻机构实施、并已用于全国大选、市政选举及欧洲议会选举的流行VAA工具"Kandidattest"的内部运作机制。各政党用户与政治家需回答相同的在线问卷，并依据答案匹配度百分比进行配对。VAA在选举中发挥着重要作用——调查显示45%的丹麦选民在上次大选中遵循了其建议，然而其内部算法尚未得到充分评估。本研究发现该算法鲁棒性不足，用户难以信赖输出中的匹配度百分比：算法的细微调整可能导致结果差异，进而潜在影响选举结果。我们通过对模拟响应的算法审计，考察了该工具在算法权重微调及问卷题目数量变化情境下的脆弱性。