A Human-Centered Privacy Approach (HCP) to AI

2026-02-04

AI Summary

This study addresses the systemic threat posed by the rapid advancement of artificial intelligence to individual privacy and advocates for embedding privacy protection intrinsically within a human-centered AI framework. The paper proposes a Human-Centered Privacy (HCP) framework that, for the first time, deeply integrates user mental models, ethical norms, and privacy-enhancing technologies—such as federated learning and differential privacy—across the entire AI lifecycle. By unifying technical, ethical, and human factors and aligning with evolving regulatory landscapes and governance practices, the framework delivers actionable privacy-by-design guidelines and cross-domain application cases. This approach not only enhances user trust and autonomy but also shifts the paradigm of privacy protection from mere regulatory compliance toward a value-driven, human-centered orientation.

Technology Category

• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

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.

Problem

• Human-Centered AI
• privacy
• ethical concerns
• human autonomy
• privacy risks

Innovation

• Human-Centered Privacy
• Federated Learning
• Differential Privacy
• Privacy by Design
• AI Ethics