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

• URL: http://arxiv.org/abs/2607.06344v1
• Date: Tue, 07 Jul 2026 14:40:20 GMT
• Title: Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction
• Abstract summary: We present a lifecycle-based and context-sensitive framework for personalised HRI, grounded in an embodiment-aware perspective.<n>We conduct an integrative analysis of key ethical risks, including autonomy erosion, biased user modelling, manipulation, dehumanisation, and privacy violations.<n>This work provides a foundation for more systematic, transparent, and ethically grounded approaches to personalised robot behaviour.
• Score: 2.1875243525685946
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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