青少年对AI隐私问题担忧严重,尤其缺乏数据控制权和透明度。
Navigating AI to Unpack Youth Privacy Concerns: An In-Depth Exploration and Systematic Review
- 系统梳理108篇论文,分析青少年在社交、教育等场景中的隐私顾虑
- 超半数青少年因担心数据滥用而减少信息共享,信任依赖透明与可控性
- 适合政策制定者、教育工作者及设计可信赖AI系统的开发者参考
本系统综述研究了年轻数字公民对人工智能(AI)系统中隐私的感知、关切与期待,聚焦社交媒体、教育科技、游戏系统和推荐算法。通过严格方法筛选,初始2000篇文献经初筛降至552篇,最终确定108篇进行深入分析。重点提取隐私担忧、数据共享行为、隐私与效用的平衡、AI信任因素、透明度期望及用户数据控制策略。结果显示,青少年普遍存在隐私焦虑,包括对个人数据控制力不足、被AI滥用风险及数据泄露恐惧;这些情绪因数据收集不透明和AI应用缺乏说明而加剧。数据分享意愿与感知收益及数据保护承诺密切相关。研究还强调家长引导作用和数据隐私教育的重要性。个性化服务受青睐,但隐私风险仍令人警惕。信任显著受透明度、可靠性、行为可预测性和数据使用说明影响。提升用户控制力的策略包括数据访问与更正、清晰同意机制和强保护承诺。研究揭示了纵向研究、跨文化比较和伦理框架构建等未来方向,对政策与教育实践具重要启示。
原文摘要 · Abstract (English)
This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks. The findings have significant implications for policy development and educational initiatives
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