Paper Number
ECIS2026-1433
Paper Type
SP
Abstract
Artificial intelligence (AI) systems are increasingly integrated into individuals’ daily lives. These systems can operate reactively, responding to user inputs, or proactively, taking initiative and acting without explicit prompts. While AI proactivity enhances convenience and performance, it also raises privacy risks because it often relies on access to personal and sensitive data. We refer to this tension as the privacy-proactivity paradox. Drawing on the privacy calculus framework, this study examines how AI proactivity influences perceived benefits and privacy risks of information disclosure, and how these shape individuals’ willingness to grant data access to AI systems. The study further investigates how AI principles, specifically accountability and explainability, moderate the impact of AI proactivity on risk perception. This study contributes to understanding how users evaluate proactive AI systems and offers practical guidance for designing AI that balances performance benefits with privacy protection.
Recommended Citation
Atabaki Nia, Niki and Mirhoseini, Mahdi, "The Privacy–Proactivity Paradox: Revisiting The Privacy Calculus In The Age Of Proactive AI" (2026). ECIS 2026 Proceedings. 4.
https://aisel.aisnet.org/ecis2026/security/security/4
The Privacy–Proactivity Paradox: Revisiting The Privacy Calculus In The Age Of Proactive AI
Artificial intelligence (AI) systems are increasingly integrated into individuals’ daily lives. These systems can operate reactively, responding to user inputs, or proactively, taking initiative and acting without explicit prompts. While AI proactivity enhances convenience and performance, it also raises privacy risks because it often relies on access to personal and sensitive data. We refer to this tension as the privacy-proactivity paradox. Drawing on the privacy calculus framework, this study examines how AI proactivity influences perceived benefits and privacy risks of information disclosure, and how these shape individuals’ willingness to grant data access to AI systems. The study further investigates how AI principles, specifically accountability and explainability, moderate the impact of AI proactivity on risk perception. This study contributes to understanding how users evaluate proactive AI systems and offers practical guidance for designing AI that balances performance benefits with privacy protection.
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