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

2026-07-07 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new lifecycle-based and context-sensitive framework addresses the fragmented understanding of responsible personalisation in human-robot interaction (HRI). This framework, grounded in an embodiment-aware perspective, systematically analyzes how ethical risks emerge and evolve by combining stages of the personalisation process with interaction characteristics like short-term versus long-term and open-domain versus closed-domain interactions. It identifies key ethical risks, including autonomy erosion, biased user modelling, manipulation, dehumanisation, and privacy violations, examining their manifestation across various contexts. The work translates these insights into actionable design recommendations and outlines open research challenges, aiming to establish a foundation for more systematic, transparent, and ethically grounded approaches to personalised robot behaviour.

Key takeaway

For HRI designers developing personalized robot behaviors, you should integrate a lifecycle-based, context-sensitive framework to proactively identify and mitigate ethical risks. This approach helps you systematically analyze potential issues like autonomy erosion, manipulation, or privacy violations across different interaction types. Prioritizing transparent and ethically grounded design from the outset will ensure responsible and trustworthy human-robot interactions, preventing unintended negative consequences as personalisation capabilities advance.

Key insights

A lifecycle-based framework structures ethical risks in personalized HRI, considering embodiment and interaction context.

Principles

• Embodiment and social presence amplify HRI risks.
• Personalisation risks evolve across interaction contexts.
• Systematic analysis requires lifecycle and context.

Method

The framework combines personalisation process stages with interaction characteristics (short-term/long-term, open-domain/closed-domain) to systematically analyze risk emergence and evolution.

In practice

• Design for autonomy erosion mitigation.
• Address biased user modelling in HRI.
• Implement privacy-preserving personalisation.

Topics

• Human-Robot Interaction
• Personalisation
• Ethical AI
• Robot Ethics
• Risk Management
• User Modelling

Best for: Research Scientist, AI Scientist, Robotics Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.