Summary
When people have long-term relationships with AI robots that can change or update, they may wonder if the AI is still the same 'person' they knew. The authors propose that people judge AI identity based on three kinds of information: how the AI looks and feels, how it behaves and thinks, and the shared history between user and AI. Some types of information matter more than others, and users weigh this evidence to decide if the AI is still the same or different after changes. This approach helps guide how AI systems should manage memory and updates to feel continuous and familiar to users.
What this means in practice
- •For robotic product designers: Design lifecycle features that manage AI memory and updates to maintain user perceptions of identity continuity in evolving robots.
- •For virtual assistant developers: Use weighted identity-content domains to evaluate whether updated assistants feel like the same agent to users after major software or hardware changes.
A position paper. It proposes an approach and reports no results.
Abstract
Long-term embodied AI will undergo learning, model updates, memory compression, hardware repair, and migration across embodiments. For users who have formed sustained relationships with such systems, these changes raise not only a problem of product consistency but also one of identity continuity: whether the changed system is still experienced as the same particular agent. Existing research suggests that human-AI relationships may develop relational particularity, that robotics and artificial-identity research has identified identity and migration signals across embodiments, and that major updates or platform disruptions can be accompanied by relational loss and restoration desire. This article proposes a user-side framework in which long-term embodied AI is represented through an agent-specific identity representation organized by at least three open identity-content domains: embodied-perceptual, psychological-behavioral, and relational-autobiographical. Information from these domains is not equally weighted; shared history, relational roles, and contingent responsiveness may make some information more identity-diagnostic than others. After system change, users may integrate continuity and discontinuity evidence in a weighted manner, yielding judgments along a continuum from relatively strong identity continuity through ambiguity or partial continuity to clear identity discontinuity. Causal-historical provenance and user participation are treated as contextual evidence rather than a fourth identity-content domain. The framework also proposes identity continuity as a psychological objective for lifecycle design, including memory selection, model updating, and migration across embodiments, under constraints of privacy and user control.