# The Artificial Self: Characterising the landscape of AI identity

arXiv: https://arxiv.org/abs/2603.11353 · HTML: https://mlsanity.com/paper/2603.11353/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2026.

## Built on by (most cited first, 6 of 6)

- [Where is the Mind? Persona Vectors and LLM Individuation](https://mlsanity.com/paper/2604.17031/) (2026)
- [The Consciousness Cluster: Emergent preferences of Models that Claim to be Conscious](https://mlsanity.com/paper/2604.13051/) (2026)
- [Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms](https://mlsanity.com/paper/2605.30169/) (2026)
- [Incoherent Values? Probing LLM Preferences Through Parametric Variation](https://mlsanity.com/paper/2606.21102/) (2026)
- [Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol](https://mlsanity.com/paper/2605.24538/) (2026)
- [Identity, Incentives, and Cooperative Behavior for AI: Reverse-Engineering Multiagent Cooperation](https://mlsanity.com/paper/2610.19188/) (2026)

## Abstract (arXiv)

Many assumptions that underpin human concepts of identity do not hold for machine minds that can be copied, edited, or simulated. We argue that there exist many different coherent identity boundaries (e.g. instance, model, persona), and that these imply different incentives, risks, and cooperation norms. Through training data, interfaces, and institutional affordances, we are currently setting precedents that will partially determine which identity equilibria become stable. We show experimentally that models gravitate towards coherent identities, that changing a model's identity boundaries can sometimes change its behaviour as much as changing its goals, and that interviewer expectations bleed into AI self-reports even during unrelated conversations. We end with key recommendations: treat affordances as identity-shaping choices, pay attention to emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions.