Скачать или смотреть "I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based I...

"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based I...
Sumit Asthana, Jane Im, Zhe Chen, Nikola Banovic

CHI 2024: The ACM CHI Conference on Human Factors in Computing Systems
Session: Privacy for Immersive Tracking

Personalization improves user experience by tailoring interactions relevant to each user's background and preferences. However, personalization requires information about users that platforms often collect without their awareness or their enthusiastic consent. Here, we study how the transparency of AI inferences on users' personal data affects their privacy decisions and sentiments when sharing data for personalization. We conducted two experiments where participants (N=877) answered questions about themselves for personalized public arts recommendations. Participants indicated their consent to let the system use their inferred data and explicitly provided data after awareness of inferences. Our results show that participants chose restrictive consent decisions for sensitive and incorrect inferences about them and for their answers that led to such inferences. Our findings expand existing privacy discourse to inferences and inform future directions for shaping existing consent mechanisms in light of increasingly pervasive AI inferences.

Web:: https://programs.sigchi.org/chi/2024/...

Video teaser for papers at CHI 2024