Who Owns My AI Twin? Data Ownership in a New World of Simulated Identities

2026-01-14

AI Summary

This study addresses the legal and ethical challenges posed by AI twins—digital replicas generated from personal data—which disrupt established frameworks concerning data ownership, identity, and autonomy. Moving beyond conventional technology-centric paradigms, the work reconceptualizes AI twins as extensions of natural persons’ personality, asserting that they should belong to individuals rather than platforms. By integrating legal philosophy, data governance theory, and AI ethics, the research develops a human-centered governance model grounded in individual control and a default principle of data privacy. This framework establishes normative foundations for enhancing personal agency and equitable data stewardship, offering an innovative theoretical and policy-oriented approach to the legal regulation of AI twins.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Security and Privacy: Data transparency and provenance

Abstract

The emergence of AI twins, digital replicas that encapsulate an individual's knowledge, memories, psychological traits, and behavioral patterns, raises novel legal and ethical challenges for data governance and personal identity. Built from personal data, these systems require a rethinking of what it means to exercise dominion over one's data and to maintain personal autonomy in an AI-mediated environment. This article argues that natural persons should be recognized as the moral and legal owners of their AI twins, which function as intimate extensions of the self rather than as proprietary technological artifacts. It critiques prevailing legal frameworks that prioritize technological infrastructure and platform control over data and individual autonomy, exposing their structural limitations. In response, the article advances a human-centric model of data governance grounded in individual dominion and a private-by-default principle. This approach proposes a reimagined social contract for AI-driven identities that strengthens personal agency, promotes equitable data stewardship, and better aligns legal norms with the socio-technical realities of AI twins.

Problem

• AI twins
• data ownership
• personal identity
• autonomy
• data governance

Innovation

• AI twins
• data ownership
• human-centric governance
• private-by-default
• digital identity