Paper Type
Complete
Abstract
The rise of disinformation through audiovisual AI-generated content (AVAIGC), such as deepfakes, challenges trust in media platforms, as users struggle to distinguish authentic from synthetic content. The increasing accessibility of AI tools exacerbates this issue, fostering skepticism, reducing user engagement, and accelerating declining trust in media platforms. To address these challenges, transparency measures such as AI-literacy initiatives are becoming prevelant due to regulatory pressure from the European Union. However, there is limited research on how AI-literacy measures on AVAIGC should be tailored to diverse users. This study examines age-tailored AI-literacy on AVAIGC by analyzing 12 focus group interviews with 60 social media users. Our findings reveal diverse preferences for AI-literacy measures, with older users favoring different approaches than younger users, and these measures improving their trust in media platforms. We contribute to trust frameworks in information systems research and provide practical guidance for media platforms, regulators, and consumer protection organizations.
Paper Number
1701
Recommended Citation
Nserat, Joseph Maximilian, "Countering Audiovisual AI-Generated Content: AI-Literacy to Enhance Trust in Media Platforms?" (2025). AMCIS 2025 Proceedings. 6.
https://aisel.aisnet.org/amcis2025/social_inclusion/social_inclusion/6
Countering Audiovisual AI-Generated Content: AI-Literacy to Enhance Trust in Media Platforms?
The rise of disinformation through audiovisual AI-generated content (AVAIGC), such as deepfakes, challenges trust in media platforms, as users struggle to distinguish authentic from synthetic content. The increasing accessibility of AI tools exacerbates this issue, fostering skepticism, reducing user engagement, and accelerating declining trust in media platforms. To address these challenges, transparency measures such as AI-literacy initiatives are becoming prevelant due to regulatory pressure from the European Union. However, there is limited research on how AI-literacy measures on AVAIGC should be tailored to diverse users. This study examines age-tailored AI-literacy on AVAIGC by analyzing 12 focus group interviews with 60 social media users. Our findings reveal diverse preferences for AI-literacy measures, with older users favoring different approaches than younger users, and these measures improving their trust in media platforms. We contribute to trust frameworks in information systems research and provide practical guidance for media platforms, regulators, and consumer protection organizations.
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