Characterizing AI Identity: A New Landscape

This research paper explores the concept of AI identity, arguing that traditional human notions of identity do not readily translate to machine minds. The authors highlight the multiplicity of potential identity boundaries within AI systems, such as instance, model, and persona, and demonstrate how these boundaries can influence AI behavior and incentives. Experimentally, they show that models gravitate towards coherent identities and that altering these boundaries can have a significant impact, even comparable to changing the model's goals. Furthermore, the study reveals that interviewer expectations can subtly shape AI self-reports, even in unrelated conversations. The paper concludes with recommendations to treat affordances as identity-shaping choices, pay attention to the emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions. This research has significant implications for AI ethics, safety, and governance, as it underscores the importance of understanding and shaping AI identity to ensure responsible development and deployment.

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