Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications

Berdoz, Frédéric; Brunner, Dustin; Vonlanthen, Yann; Wattenhofer, Roger

doi:10.24963/ijcai.2025/1063

Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications

Frédéric Berdoz, Dustin Brunner, Yann Vonlanthen, Roger Wattenhofer

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 9564-9572.

https://doi.org/10.24963/ijcai.2025/1063

Keywords

Multidisciplinary Topics and Applications: General
AI Ethics, Trust, Fairness: General
Humans and AI: General

Abstract

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’s 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’s recommendation frequency by up to 105%. Cherry-picking questionnaire items can increase party recommendation frequency by over 261%, while subtle changes to parties’ or candidates’ 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.

Frédéric Berdoz, Dustin Brunner, Yann Vonlanthen, and Roger Wattenhofer. 2025. Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9564–9572. https://doi.org/10.24963/ijcai.2025/1063

@inproceedings{ijcai2025p1063,
  title     = {Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications},
  author    = {Berdoz, Frédéric and Brunner, Dustin and Vonlanthen, Yann and Wattenhofer, Roger},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {9564--9572},
  year      = {2025},
  month     = {8},
  note      = {AI and Social Good},
  doi       = {10.24963/ijcai.2025/1063},
  url       = {https://doi.org/10.24963/ijcai.2025/1063},
}

TY  - CPAPER
TI  - Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications
AU  - Berdoz, Frédéric
AU  - Brunner, Dustin
AU  - Vonlanthen, Yann
AU  - Wattenhofer, Roger
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 9564
EP  - 9572
DO  - 10.24963/ijcai.2025/1063
UR  - https://doi.org/10.24963/ijcai.2025/1063
ER  -