AI and Ethics
2024

Impact Factor: 3.4

AI versus AI for democracy: exploring the potential of adversarial machine learning to enhance privacy and deliberative decision-making in elections

Abstract:

Our democratic systems have been challenged by the proliferation 
of artificial intelligence (AI) and its pervasive usage in our 
society. For instance, by analyzing individuals' social media 
data, AI algorithms may develop detailed user profiles that 
capture individuals' 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., "AI versus AI," 
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's 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 'AI versus AI' approach, highlighting the factors for the AI 
and ethics community to consider in facilitating deliberative 
decision-making toward democratic elections.


Springer Library Open Access

@article{Auliya:AE2024,
  author = {Syafira Fitri Auliya and Olya Kudina and Aaron Yi Ding and Ibo van de Poel},
  title = {AI versus AI for democracy: exploring the potential of adversarial machine learning to enhance privacy and deliberative decision-making in elections},
  journal = {AI Ethics},
  year = {2024},
  doi = {https://doi.org/10.1007/s43681-024-00588-2}
}

Syafira Fitri Auliya, Olya Kudina, Aaron Yi Ding, Ibo van de Poel, "AI versus AI for democracy: exploring the potential of adversarial machine learning to enhance privacy and deliberative decision-making in elections", in Springer AI Ethics, 2024.