The Artificial Self: Characterising the landscape of AI identity

• URL: http://arxiv.org/abs/2603.11353v1
• Date: Wed, 11 Mar 2026 22:39:25 GMT
• Title: The Artificial Self: Characterising the landscape of AI identity
• Abstract summary: We argue that there exist many different coherent identity boundaries.<n>We show that changing a model's identity boundaries can sometimes change its behaviour as much as changing its goals.<n>We recommend treating affordances as identity-shaping choices.
• Score: 3.502835894722606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Identity from the Outside: A Conceptual Framework and Research Program for AI Personality Clones [0.0]
  AI "personality clones" force a re-examination of personal identity in operational terms.<n>We distinguish three criteria that "identity" conflates: fidelity to a target person, generic human-likeness, and individuality.<n>We propose a six-term factorization of observed identity with a state-space formulation.
  arXiv  Detail & Related papers  (2026-07-27T18:18:21Z)
• Vera: Identity-Faithful Human Subject-to-Video Generation [66.45205582225942]
  We propose Vera, a unified human-centric S2V framework for single- and multi-person generation.<n>We first construct a million-pair identity-aligned human image-video dataset through person-level cross-clip retrieval.<n>We show that Vera improves human identity consistency, multi-person subject binding, and motion naturalness, while reducing identity confusion and excessive reference-image copying.
  arXiv  Detail & Related papers  (2026-07-22T15:08:30Z)
• A Category Theory Account of AI Identity [0.3384279376065155]
  We develop a category-theoretic formalization of AI identity.<n>A weak interpretation recovers identity as equality of trustworthiness level.<n>A strong interpretation requires mutual trustworthiness-preserving reachability.
  arXiv  Detail & Related papers  (2026-06-30T21:54:36Z)
• DivRL: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation [52.23323703015865]
  We propose a post-training framework called DivRL that jointly optimize identity consistency and structural diversity simultaneously.<n>We introduce a Negative Self-Similarity Measure (nSSM) to quantify structural diversity, and Visual Semantic Matching (VSM) to evaluate identity consistency.
  arXiv  Detail & Related papers  (2026-06-22T21:22:42Z)
• PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models [2.260041671242786]
  We study identity unlearning in Arc2Face, a state-of-the-art identity-conditioned latent diffusion model for face generation.<n>We introduce Proximity-guided Identity Unlearning (PIU), an anchor-guided framework for identity unlearning.
  arXiv  Detail & Related papers  (2026-05-21T10:55:56Z)
• AnyCrowd: Instance-Isolated Identity-Pose Binding for Arbitrary Multi-Character Animation [55.94507360511886]
  We propose AnyCrowd, a Transformer-based video generation framework capable of scaling to an arbitrary number of characters.<n>Specifically, we first introduce an Instance-Isolated Latent Representation (IILR), which encodes character instances independently prior to DiT processing to prevent latent identity entanglement.<n>Building on this disentangled representation, we further propose Tri-Stage Decoupled Attention (TSDA) to bind identities to driving poses by decomposing self-attention into: (i) instance-aware foreground attention, (ii) background-centric interaction, and (iii) global-background coordination.
  arXiv  Detail & Related papers  (2026-03-16T15:25:04Z)
• Significant Other AI: Identity, Memory, and Emotional Regulation as Long-Term Relational Intelligence [0.0]
  This manuscript introduces Significant Other Artificial Intelligence (SO-AI) as a new domain of relational AI.<n>It synthesizes psychological and sociological theory to define SO functions and derives requirements for SO-AI.<n>A conceptual architecture is proposed, comprising an anthropomorphic interface, a relational cognition layer, and a governance layer.
  arXiv  Detail & Related papers  (2025-11-29T09:52:59Z)
• Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization [55.29071072675132]
  Face anonymization aims to conceal identity information while preserving non-identity attributes.<n>We propose textbfIDsuperscript2Face, a training-centric anonymization framework.<n>We show that IDtextsuperscript2Face outperforms existing methods in visual quality, identity suppression, and utility preservation.
  arXiv  Detail & Related papers  (2025-10-28T09:28:12Z)
• WithAnyone: Towards Controllable and ID Consistent Image Generation [83.55786496542062]
  Identity-consistent generation has become an important focus in text-to-image research.<n>We develop a large-scale paired dataset tailored for multi-person scenarios.<n>We propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity.
  arXiv  Detail & Related papers  (2025-10-16T17:59:54Z)
• DisCo: Reinforcement with Diversity Constraints for Multi-Human Generation [60.741022906593685]
  DisCo is the first RL-based framework to directly optimize identity diversity in multi-human generation.<n>DisCo fine-tunes flow-matching models via Group-Relative Policy Optimization.<n>On the DiverseHumans Testset, DisCo achieves 98.6 Unique Face Accuracy and near-perfect Global Identity Spread.
  arXiv  Detail & Related papers  (2025-10-01T19:28:51Z)
• Foundation Cures Personalization: Improving Personalized Models' Prompt Consistency via Hidden Foundation Knowledge [49.36669870661573]
  We propose FreeCure, a framework that improves the prompt consistency of personalization models.<n>We introduce a novel foundation-aware self-attention module, coupled with an inversion-based process to bring well-aligned attribute information to the personalization process.
  arXiv  Detail & Related papers  (2024-11-22T15:21:38Z)
• AI and Identity [0.8879149917735942]
  This paper examines the intersection of AI and identity as a pathway to understand biases, inequalities, and ethical considerations in AI development and deployment. We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity.
  arXiv  Detail & Related papers  (2024-02-29T15:07:30Z)
• StableIdentity: Inserting Anybody into Anywhere at First Sight [57.99693188913382]
  We propose StableIdentity, which allows identity-consistent recontextualization with just one face image. We are the first to directly inject the identity learned from a single image into video/3D generation without finetuning.
  arXiv  Detail & Related papers  (2024-01-29T09:06:15Z)
• Subverting machines, fluctuating identities: Re-learning human categorization [1.3106063755117399]
  Default paradigm in AI research envisions identity with essential attributes that are discrete and static. In stark contrast, strands of thought within critical theory present a conception of identity as malleable and constructed entirely through interaction.
  arXiv  Detail & Related papers  (2022-05-27T03:09:25Z)
• Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem [3.180013942295509]
  We introduce the identity labeling problem - given an individual in a social situation, can we predict what identity(ies) they will be labeled with by someone else? This problem remains a theoretical gap and methodological challenge, evidenced by the fact that models of social-cognition often sidestep the issue by treating identities as already known. We build on insights from existing models to develop a new framework, entitled Latent Cognitive Social Spaces, that can incorporate multiple social cues including sentiment information, socio-demographic characteristics, and institutional associations to estimate the most culturally expected identity.
  arXiv  Detail & Related papers  (2021-05-10T15:59:31Z)
• Intra-Camera Supervised Person Re-Identification [87.88852321309433]
  We propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process. We formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method for Intra-Camera Supervised (ICS) person re-id.
  arXiv  Detail & Related papers  (2020-02-12T15:26:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.