Cognitive Digital Twins: Ethical Risks and Governance for AI Systems That Model the Mind

2026-06-22

AI Summary

This study addresses the unique ethical risks posed by cognitive digital twins (CDTs)—systems that simulate or act on behalf of individual cognitive processes—which remain inadequately covered by existing AI governance frameworks. The work offers the first systematic conceptualization of CDTs and identifies a critical governance gap at the level of cognitive representation. Integrating insights from ethics, AI governance, and human-computer interaction, it proposes a novel “5A” governance framework centered on authority, autonomy, access control, accountability, and availability. The paper further delineates CDT-specific risks and articulates forward-looking regulatory principles, including enhanced informed consent, purpose limitation, model traceability, and decommissioning mechanisms, thereby providing concrete governance pathways for high-risk CDT applications.

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.

Problem

• Cognitive Digital Twins
• Ethical Risks
• AI Governance
• Cognitive Representation
• Proxy Action

Innovation

• Cognitive Digital Twins
• AI Governance
• 5A Framework
• Cognitive Representation
• Proxy Agency