Digital Twin Ethics and Governance Framework (DTEGF)

Abstract

Digital twins are evolving from models of physical assets into intelligent representations of identifiable people that can analyze, predict, communicate, and act on a person’s behalf in digital and physical environments. This shift raises governance questions that general AI ethics instruments do not fully operationalize: who may authorize a twin to act, how faithfully it must represent the person, how it should be governed as it drifts from the person over time, and what should happen on incapacity or death. This document proposes the Digital Twin Ethics and Governance Framework (DTEGF). DTEGF rests on nine ethical pillars and a three-tier governance architecture in which safeguards become harder to override as the consequences of overriding them increase, with non-overridable protections backed by technical or organizational controls the twin cannot remove. It classifies twins along two largely independent dimensions: four risk levels, from private representational twins to high-impact ones, and four embodiment classes, from software-only twins to those capable of hazardous physical action. The pillars are translated into 30 governance requirements and six embodiment requirements, linked through an applicability matrix and pillar traceability. Its distinctive contributions are person-twin separation, authority minimization, representation by explicit delegation, safeguards beyond the model, and lifecycle governance from creation to post-mortem. DTEGF is designed to be interoperable with the UNESCO Recommendation on the Ethics of Artificial Intelligence, the OECD AI Principles, the EU AI Act, and Saudi Arabia’s SDAIA AI Ethics Principles and Personal Data Protection Law.