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Digital Doppelgängers, Human Relationships, and Practical Identity
Cristina Voinea1, Sebastian Porsdam Mann2,3, Julian Savulescu4
1Uehiro Oxford Institute, University of Oxford.
Exploring relationships with Large Language Model (LLM)-based digital doppelgängers (DDs) reveals their potential impact on users' practical identity. Understanding these AI interactions is crucial for future development and deployment.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence Ethics
- Psychology of Technology
Background:
- Digital doppelgängers (DDs) are AI conversational agents trained on user data to mimic personality.
- Relationships significantly shape an individual's practical identity.
- Relational DDs are designed to emulate known individuals, acting as artificial companions.
Purpose of the Study:
- To examine the effects of relationships with LLM-based DDs on users' values, concerns, and interests.
- To understand how DDs influence practical identity formation.
- To provide considerations for the development and deployment of DDs.
Main Methods:
- Conceptual analysis of LLM-based digital doppelgängers.
- Exploration of the role of relationships in defining practical identity.
- Distinction between task-specific and relational DDs.
Main Results:
- Relationships are central to shaping practical identity.
- Relational DDs are specifically designed to serve as artificial relational partners.
- Interactions with DDs have the potential to alter users' practical identities.
Conclusions:
- The development and deployment of DDs require careful consideration of their impact on user identity.
- Further research is needed to understand the long-term psychological effects of human-DD relationships.
- Ethical guidelines are essential for responsible AI companion development.
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