"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

TL;DR

This study investigates privacy threats faced by UK-based domestic workers (DWs) in AI-driven smart homes, focusing on both employer-controlled environments and DWs' own homes. Using semi-structured interviews with 18 DWs and a human-centered threat-modeling approach grounded in Communication Privacy Management (CPM), the authors derive a sociotechnical threat model that places DW agencies as institutional adversaries and accounts for cross-household data flows and AI analytics. Key findings show that AI-enabled cameras and speakers generate persistent data traces and algorithmic inferences that intensify surveillance, while agency-driven employment terms and vague contracts further constrain DWs' privacy boundaries. The work argues for coordinated interventions in device design, policy, and agency practices to strengthen DW privacy across interconnected contexts, and provides concrete design recommendations such as tangible indicators, guest modes, and post-employment data deletion to mitigate cross-context privacy harms.

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.