The Privacy Paradox in Human–AI Communication: Why People Share More Information with Machines than with Other Humans
Keywords:
Conversational agents, Human-AI interaction, Privacy calculus, Privacy paradox, Self disclosureAbstract
This systematic literature review investigates the privacy paradox in human-AI interaction, focusing on why users frequently disclose sensitive personal information to artificial agents despite harboring stated privacy concerns. Utilizing the Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) framework, this study synthesizes literature spanning 2015 to 2025. Following rigorous screening protocols, an initial corpus of 1,428 articles was refined to a final synthesis of 86 peer-reviewed studies. The review identifies five primary thematic dimensions: (1) the amplification of self-disclosure through anthropomorphism and social presence; (2) the transformative privacy calculus balancing utility against vulnerability; (3) the psychological mechanics of de-identified intimacy; (4) architectural parameters and conversational conditions influencing privacy salience; and (5) contextual variances among vulnerable populations, including patients and the elderly. The principal findings indicate that conversational AI effectively overrides traditional privacy-protective behaviors by simulating non-judgmental empathy and hyperpersonalized utility, creating an "illusion of isolation" wherein users feel both socially supported and technically unobserved. This review contributes to Human-Computer Interaction (HCI) by extending privacy calculus theory into the realm of generative agents. Ultimately, the study addresses a critical research gap by mapping the specific conversational architectures that bypass cognitive privacy defenses, offering empirical guidelines for designing ethically compliant, privacy-preserving AI frameworks.
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