arXiv:2603. 27117v2 Announce Type: replace-cross Abstract: This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems.
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arXiv:2607. 16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems.
The study investigates how users perceive the helpfulness and privacy-preservation of large language model (LLM) responses in privacy-sensitive scenarios. Using 94 participants and 90 PrivacyLens scenarios, researchers found that users’ evaluations of identical LLM outputs varied widely, whereas five proxy LLM judges showed high agreement but low correlation with user judgments. The results suggest that proxy LLMs cannot reliably estimate users’ diverse perceptions of utility and privacy, highlighting the need for more user-centered evaluation methods.
The article "We'll Fix It Later": Education, AI, and the Deferral of Privacy in EdTech examines how educational technology platforms collect sensitive student data but often postpone privacy considerations until later stages of product development. Through 12 interviews and a policy audit of 48 platforms, the study finds that privacy is acknowledged yet deferred in favor of functionality, growth, and funding, with responsibility frequently shifted to cloud providers or downstream institutions. The audit reveals that many platforms lack clear AI disclosures and provide limited governance details, indicating a gap between data collection practices and privacy accountability.
arXiv:2607. 14607v1 Announce Type: cross Abstract: Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees.
PEARL is a task-aware framework that evaluates differentially private synthetic educational data by checking validity, privacy protection, predictive usefulness, and suitability for the intended educational task. In a study of 96 settings, only 12 datasets passed all PEARL checks, with many failures due to missing outcome groups or distorted learning activity order. Even datasets that met privacy and predictive-usefulness criteria sometimes exhibited fairness issues and failed to support knowledge-tracing models, indicating that privacy alone does not guarantee practical usefulness.
Large language models (LLMs) are increasingly deployed in hiring workflows, yet most research on gender bias in LLM hiring decisions has focused on English-language, Western-format resumes. This study examines whether pro-female gender bias extends to a Japanese corporate context and evaluates two practical mitigation strategies.