Sumit Asthan
Personalization improves user experience by tailoring interactions relevant to each user’s background and preferences. However, per sonalization 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 afects their privacy decisions and sentiments when sharing data for personalization. We conducted two experiments where participants (N=877) answered questions about themselves for per sonalized public arts recommendations. Participants indicated their consent to let the system use their inferred data and explicitly pro vided 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 fndings expand existing privacy discourse to inferences and inform future directions for shaping existing consent mechanisms in light of increasingly pervasive AI inferences. CCS CONCEPTS • Security and privacy → Social aspects of security and pri vacy; • Human-centered computing → Empirical studies in HCI.