Cognitive Digital Twins: Ethical Risks and Governance for AI Systems That Model the Mind
2026-06-22 • Artificial Intelligence
Artificial IntelligenceComputation and Language
AI summaryⓘ
The authors explain a new type of AI called cognitive digital twins (CDTs), which are virtual models that imitate a person's thinking by using their data. These models can predict, simulate, or even make decisions for someone, going beyond current AI systems like personal assistants. The paper outlines unique risks and governance challenges for CDTs, such as errors in representation and power imbalances. The authors propose a detailed framework to better regulate CDTs, emphasizing the need to control how cognition itself is modeled, not just the decisions or actions that follow.
cognitive digital twinsAI governancecognitive modelingautonomyproxy decision-makingdata privacyautomated decision systemssimulationepistemic authoritymodel traceability
Authors
Vamshi Krishna Bonagiri, Juan Nicolas Sepulveda-Arias, Abdoul Jalil Djiberou Mahamadou, Monojit Choudhury
Abstract
As AI systems become increasingly persistent and personalized, they make possible a class of technologies that we call cognitive digital twins (CDTs): dynamic computational representations of a specific person's cognition, updated from behavioral, contextual, or physiological data in order to model, predict, or simulate that person's cognition, or to act as that person's communicative or decision-making proxy. CDTs combine cognitive inference with longitudinal representation, simulation, and proxy action in ways that existing governance strategies for personal assistants, autonomous agents, recommender systems, and automated decision systems only partially address. This paper makes four contributions. First, we define CDTs and distinguish them from adjacent systems. Second, we introduce a 5A governance framework organized around authority, autonomy, access and control, accountability, and availability. Third, we identify CDT-specific risks, from misrepresentation and epistemic authority shifts to shadow twins, simulated participation, proxy action, and proxy-power asymmetries. Fourth, we analyze governance gaps and propose requirements for high-risk CDTs that strengthen consent, purpose limitation, validity, traceability, contestation, independent review, and model retirement. Existing frameworks primarily regulate data processing, automated decisions, or autonomous actions; CDTs also require governance at the level of cognitive representation itself, before any final decision or external action occurs. We argue that CDTs require governance not only because they can act for people, but because they can become infrastructures through which cognition is represented, simulated, classified, and operationalized.