{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:30:32Z","timestamp":1769841032386,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Voting advice applications (VAAs) help millions of voters understand which political parties or candidates best align with their views. This paper explores the potential risks these applications pose to the democratic process when targeted by adversarial entities. In particular, we expose 11 manipulation strategies and measure their impact using data from Switzerland\u2019s primary VAA, Smartvote, collected during the last two national elections. We find that altering application parameters, such as the matching method, can shift a party\u2019s recommendation frequency by up to 105%. Cherry-picking questionnaire items can increase party recommendation frequency by over 261%, while subtle changes to parties\u2019 or candidates\u2019 responses can lead to a 248% increase. To address these vulnerabilities, we propose adversarial robustness properties VAAs should satisfy, introduce empirical metrics for assessing the resilience of various matching methods, and suggest possible avenues for research toward mitigating the effect of manipulation. Our framework is key to ensuring secure and reliable AI-based VAAs poised to emerge in the near future.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1063","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9564-9572","source":"Crossref","is-referenced-by-count":2,"title":["Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications"],"prefix":"10.24963","author":[{"given":"Fr\u00e9d\u00e9ric","family":"Berdoz","sequence":"first","affiliation":[{"name":"ETH Zurich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dustin","family":"Brunner","sequence":"additional","affiliation":[{"name":"ETH Zurich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yann","family":"Vonlanthen","sequence":"additional","affiliation":[{"name":"ETH Zurich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Wattenhofer","sequence":"additional","affiliation":[{"name":"ETH Zurich"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:01Z","timestamp":1758627361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1063"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1063","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}