"These cameras are just like the Eye of Sauron": A Sociotechnical Threat Model for AI-Driven Smart Home Devices as Perceived by UK-Based Domestic Workers

2026-02-09

AI Summary

This study investigates the privacy risks and surveillance challenges that AI-powered smart home devices pose to domestic workers, both in employers’ households and their own residences. Drawing on semi-structured interviews with 18 domestic workers in the UK and integrating Communication Privacy Management (CPM) theory with human-centered threat modeling, the research analyzes data exposure vulnerabilities across multi-household settings. It proposes the first cross-household sociotechnical threat model centered on domestic workers, reconceptualizing domestic work agencies as institutional adversaries—a departure from conventional models focused on single households and abstract attackers. The findings reveal how AI-driven analytics, residual data logs, and cross-household data flows intensify privacy violations, leading to actionable socio-technical recommendations to strengthen domestic workers’ privacy rights and autonomy.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Voting

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers'homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing how AI analytics, residual data logs, and cross-household data flows shaped the privacy risks faced by participants. In employer-controlled homes, AI-enabled features and opaque, agency-mediated employment arrangements intensified surveillance and constrained participants'ability to negotiate privacy boundaries. In their own homes, participants had greater control as device owners but still faced challenges, including gendered administrative roles, opaque AI functionalities, and uncertainty around data retention. We synthesize these insights into a sociotechnical threat model that identifies DW agencies as institutional adversaries and maps AI-driven privacy risks across interconnected households, and we outline social and practical implications for strengthening DW privacy and agency.

Problem

• privacy risks
• domestic workers
• AI-driven smart home devices
• sociotechnical threat
• data surveillance

Innovation

• sociotechnical threat model
• AI-driven smart home devices
• domestic workers
• cross-household data flows
• Communication Privacy Management