From Framework to Practice: Youth Negotiations of Privacy with Smart Voice Assistants Through the PEA-AI Lens
Molly Campbell, Yulia Bobkova, Ajay Kumar Shrestha

TL;DR
This study investigates how Canadian youth perceive and negotiate privacy with smart voice assistants, revealing a privacy paradox and offering design principles to improve trust and privacy practices.
Contribution
It applies the PEA-AI framework to empirically analyze youth privacy perceptions and behaviors regarding SVAs, providing new insights into privacy negotiation and design implications.
Findings
Youth show moderate privacy concerns but also perceive benefits and engage in protective behaviors.
Transparency and trust in SVAs are rated lowest among youth.
Heavy SVA users perceive higher benefits and lower risks.
Abstract
Smart voice assistants (SVAs) have become embedded in the daily lives of youth, introducing complex privacy challenges due to always-on listening, shared device usage, and opaque data practices. This study applies the Privacy-Ethics Alignment in AI (PEA-AI) framework to examine how youth perceive and negotiate privacy within SVAs. Through a survey of 469 Canadian youth (aged 16-24), we measured five privacy constructs: Perceived Privacy Risk (PPR), Perceived Privacy Benefits (PPBf), Algorithmic Transparency and Trust (ATT), Privacy Self-Efficacy (PSE), and Privacy-Protective Behavior (PPB). Results reveal a persistent privacy paradox. While youth express moderate to high privacy concerns (PPR M = 3.61), perceived benefits (PPBf M = 3.00) and protective actions (PPB M = 3.03) remain moderate, with transparency and trust scoring lowest (ATT M = 2.52). Heavy SVA users report higher…
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Taxonomy
TopicsAI in Service Interactions · Digital Mental Health Interventions · Social Robot Interaction and HRI
