Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 213 references that are in the map. First posted on arXiv 7 Jul 2026.
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28 of 213 references.
References · 28
- 2023Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategiescited by 85
- 2024Stereotype or Personalization? User Identity Biases Chatbot Recommendationscited by 34
- 2023Robotic Vision for Human-Robot Interaction and Collaboration: A Survey and Systematic Reviewcited by 25
- 2025Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalizationcited by 24
- 2024LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actionscited by 22
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- 2021I am Definitely Manipulated, Even When I am Aware of it. It s Ridiculous! -- Dark Patterns from the End-User Perspectivecited by 8
- 2023Not Only WEIRD but "Uncanny"? A Systematic Review of Diversity in Human-Robot Interaction Researchcited by 8
16 more
- 2025The Sense of Agency in Assistive Robotics Using Shared Autonomycited by 8
- 2025Understanding Generative AI Risks for Youth: A Taxonomy Based on Empirical Datacited by 7
- 2021A Survey on End-User Robot Programmingcited by 6
- 2022Liability regimes in the age of AI: a use-case driven analysis of the burden of proofcited by 4
- 2017How to be Helpful? Supportive Behaviors and Personalization for Human-Robot Collaborationcited by 3
- 2022Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactionscited by 3
- 2022Understanding Factors that Shape Children's Long Term Engagement with an In-Home Learning Companion Robotcited by 3
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- 2024On the impact of robot personalization on human-robot interaction: A reviewcited by 2
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- 2024Robots in the Wild: Contextually-Adaptive Human-Robot Interactions in Urban Public Environmentscited by 1
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- 2025From Human Bias to Robot Choice: How Occupational Contexts and Racial Priming Shape Robot Selectioncited by 1
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