A Human-Centered Privacy Approach (HCP) to AI

TL;DR

This paper presents a human-centered privacy framework for AI that integrates technical, ethical, and human factors to address privacy risks throughout the AI development lifecycle.

Contribution

It introduces a comprehensive human-centered privacy framework, combining privacy-preserving techniques, user perspectives, and ethical considerations for AI.

Findings

Mapping privacy risks across AI lifecycle stages

Integration of federated learning and differential privacy

Emphasis on multidisciplinary approaches for privacy in HCAI

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.