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

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

Summary

A new lifecycle-based and context-sensitive framework for personalized Human-Robot Interaction (HRI) is presented, addressing the fragmented understanding of ethical risks in this rapidly evolving field. The framework integrates stages of the personalization process with interaction characteristics, such as short-term versus long-term and open-domain versus closed-domain contexts. This systematic approach enables a detailed analysis of how risks like autonomy erosion, biased user modeling, manipulation, dehumanization, and privacy violations emerge and evolve in HRI. Grounded in an embodiment-aware perspective, the work translates these insights into actionable design recommendations and identifies open research challenges. This structured approach aims to provide a foundation for more transparent and ethically sound personalized robot behavior, acknowledging the unique amplification of risks due to robots' embodiment and social presence.

Key takeaway

For robotics engineers and AI ethicists designing personalized Human-Robot Interaction systems, you must adopt a structured, lifecycle-based approach to proactively identify and mitigate ethical risks. Your design process should explicitly consider how robot embodiment and interaction context (e.g., short-term vs. long-term) amplify issues like autonomy erosion, manipulation, and privacy violations. Implement the provided design recommendations to ensure your personalized robot behaviors are transparent and ethically grounded from conception.

Key insights

A lifecycle-based framework systematically analyzes ethical risks in personalized Human-Robot Interaction, guiding responsible design.

Principles

• Robot embodiment amplifies ethical risks.
• Personalization risks evolve across interaction contexts.
• Systematic frameworks improve ethical HRI design.

Method

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

Topics

• Human-Robot Interaction
• Personalization
• Ethical AI
• Robot Ethics
• Risk Management
• AI Frameworks

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 Artificial Intelligence.