Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction

2026-07-07

AI Summary

This study addresses the ethical risks—such as diminished user autonomy, biased user modeling, manipulation, dehumanization, and privacy violations—that arise from personalized human-AI interaction, noting the absence of a systematic, context-sensitive analytical framework in current research. To bridge this gap, the paper proposes an embodied risk analysis framework that integrates the interaction lifecycle with contextual characteristics (e.g., short-term vs. long-term, open-domain vs. closed-domain). By uniquely aligning personalization stages with interaction dynamics, the framework leverages integrative literature synthesis, contextual categorization modeling, and ethical risk mapping to establish a structured evaluation system. It not only elucidates the mechanisms underlying risk evolution across contexts but also yields actionable design guidelines and delineates promising directions for future research.

Abstract

While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interaction contexts. This gap is particularly critical in HRI, where robots' embodiment and social presence can amplify and reshape such risks or generate new types of risks. We present a lifecycle-based and context-sensitive framework for personalised HRI, grounded in an embodiment-aware perspective. The framework combines stages of the personalisation process with interaction characteristics (short-term vs. long-term, open-domain vs. closed-domain), enabling systematic analysis of how risks arise and evolve. Building on this, we conduct an integrative analysis of key ethical risks, including autonomy erosion, biased user modelling, manipulation, dehumanisation, and privacy violations, and examine how they manifest across contexts. We translate these insights into actionable design recommendations and outline open research challenges. By structuring both the design space and risk landscape of personalised HRI, this work provides a foundation for more systematic, transparent, and ethically grounded approaches to personalised robot behaviour.

Problem

• responsible personalisation
• human-robot interaction
• ethical risks
• embodiment
• context-sensitive

Innovation

• responsible personalisation
• human-robot interaction
• embodiment-aware
• ethical risks
• context-sensitive framework