AI Ethics in Smart Homes: Progress, User Requirements and Challenges
Liqian You, Jianlong Zhou, Zhiwei Li, Fang Chen
TL;DR
The paper tackles the ethical challenges of AI-driven detection in smart homes by applying the User Requirements Notation (URN) framework to systematically review literature from 1985 to 2024. It combines a technology-focused survey of smart-home architectures, detection sensors, and evolving AI methods with a user-centric ethical analysis to propose a URN-based design guideline set. The main contributions include a framework that links ethical goals to concrete design decisions (via GRL, UCM, and RDD), an examination of detection technologies through privacy, fairness, transparency, and accountability lenses, and practical guidelines for developers. The study highlights the importance of privacy-by-design, fairness in algorithmic decision-making, explainability, and human autonomy as foundational elements for trustworthy smart-home systems, and it calls for ongoing collaboration among technologists, ethicists, and policymakers to address emerging challenges such as BCIs, XR, and advanced AI interfaces.
Abstract
With the rise of Internet of Things (IoT) technologies in smart homes and the integration of artificial intelligence (AI), ethical concerns have become increasingly significant. This paper explores the ethical implications of AI-driven detection technologies in smart homes using the User Requirements Notation (URN) framework. In this paper, we thoroughly conduct thousands of related works from 1985 to 2024 to identify key trends in AI ethics, algorithm methods, and technological advancements. The study presents an overview of smart home and AI ethics, comparing traditional and AI-specific ethical issues, and provides guidelines for ethical design across areas like privacy, fairness, transparency, accountability, and user autonomy, offering insights for developers and researchers in smart homes.