构建以人为本的隐私框架，融合技术与伦理，保障AI发展中的个体尊严。
A Human-Centered Privacy Approach (HCP) to AI
- 从数据采集到部署全周期识别隐私风险，提出融合技术与人文的防护思路。
- 整合联邦学习、差分隐私等技术，结合用户心理模型与治理机制提升隐私保护。
- 适合关注AI伦理、隐私设计及政策制定的研究者与从业者参考。
随着以人为本的人工智能（HCAI）范式日益重要，其社会价值伴随显著伦理挑战，尤其是个体隐私保护问题。本文全面梳理了人工智能生命周期中各阶段的隐私风险，涵盖数据收集、模型训练、部署与再利用，并强调隐私风险对系统整体的影响。文中提出一种以人为本的隐私（HCP）框架，整合技术、伦理与人因视角，引入联邦学习、差分隐私等隐私保护技术。后续章节从用户认知模型出发，结合不断演进的监管环境与伦理规范，探讨隐私治理路径。基于该框架，提出具体设计指南，并通过跨领域实际案例展示应用效果。最后，讨论持续存在的开放挑战与未来研究方向，强调需融合技术、设计、政策与伦理多学科能力，将隐私内嵌于HCAI核心，以确保技术发展尊重人类自主性、信任与尊严。
原文摘要 · Abstract (English)
As the paradigm of Human-Centered AI (HCAI) gains prominence, its benefits to society are accompanied by significant ethical concerns, one of which is the protection of individual privacy. This chapter provides a comprehensive overview of privacy within HCAI, proposing a human-centered privacy (HCP) framework, providing integrated solution from technology, ethics, and human factors perspectives. The chapter begins by mapping privacy risks across each stage of AI development lifecycle, from data collection to deployment and reuse, highlighting the impact of privacy risks on the entire system. The chapter then introduces privacy-preserving techniques such as federated learning and dif erential privacy. Subsequent chapters integrate the crucial user perspective by examining mental models, alongside the evolving regulatory and ethical landscapes as well as privacy governance. Next, advice on design guidelines is provided based on the human-centered privacy framework. After that, we introduce practical case studies across diverse fields. Finally, the chapter discusses persistent open challenges and future research directions, concluding that a multidisciplinary approach, merging technical, design, policy, and ethical expertise, is essential to successfully embed privacy into the core of HCAI, thereby ensuring these technologies advance in a manner that respects and ensures human autonomy, trust and dignity.
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