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For instance, by analyzing individuals\u2019 social media data, AI algorithms may develop detailed user profiles that capture individuals\u2019 specific interests and susceptibilities. These profiles are leveraged to derive personalized propaganda, with the aim of influencing individuals toward specific political opinions. To address this challenge, the value of privacy can serve as a bridge, as having a sense of privacy can create space for people to reflect on their own political stance prior to making critical decisions, such as voting for an election. In this paper, we explore a novel approach by harnessing the potential of AI to enhance the privacy of social-media data. By leveraging adversarial machine learning, i.e., \u201cAI versus AI,\u201d we aim to fool AI-generated user profiles to help users hold a stake in resisting political profiling and preserve the deliberative nature of their political choices. More specifically, our approach probes the conceptual possibility of infusing people\u2019s social media data with minor alterations that can disturb user profiling, thereby reducing the efficacy of the personalized influences generated by political actors. Our study delineates the boundary of ethical and practical implications associated with this \u2018AI versus AI\u2019 approach, highlighting the factors for the AI and ethics community to consider in facilitating deliberative decision-making toward democratic elections.<\/jats:p>","DOI":"10.1007\/s43681-024-00588-2","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T09:01:35Z","timestamp":1729069295000},"page":"2801-2813","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AI versus AI for democracy: exploring the potential of adversarial machine learning to enhance privacy and deliberative decision-making in elections"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8634-3677","authenticated-orcid":false,"given":"Syafira Fitri","family":"Auliya","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5374-1687","authenticated-orcid":false,"given":"Olya","family":"Kudina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-031X","authenticated-orcid":false,"given":"Aaron Yi","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9553-5651","authenticated-orcid":false,"given":"Ibo","family":"Van de Poel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"588_CR1","doi-asserted-by":"crossref","unstructured":"Manheim, K., Kaplan, L.: Artificial intelligence: risks to privacy and democracy. 21, 83 (2019)","DOI":"10.3390\/bdcc3020021"},{"key":"588_CR2","doi-asserted-by":"publisher","first-page":"66","DOI":"10.21552\/delphi\/2019\/2\/4","volume":"2","author":"M Brkan","year":"2019","unstructured":"Brkan, M.: Artificial intelligence and democracy: delphi. 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