Ethics challenges of new technologies: Human digital twins
From The Embassy of Good Science
ThemesEthics challenges of new technologies: Human digital twins
What is this about?
A human digital twin is a highly detailed digital representation of a real person that uses data, artificial intelligence, and simulations to replicate aspects of the individual’s body, behavior, or decision-making processes. Unlike simple avatars or profiles, digital twins are dynamic systems continuously updated with real-world data such as health records, biometric signals (e.g., wearables tracking heart rate variability), behavioral data, and environmental information.
Originally developed in engineering and manufacturing, digital twin technology is now expanding into healthcare, smart cities, and personalized services. In medicine, for example, a patient’s digital twin could simulate psychopharmacological responses or disease progression in psychiatric disorders like depression, allowing physicians to test therapies virtually before real-world application. In other sectors, digital twins may model human behavior for training, workforce optimization, or personalized consumer experiences.
However, as this technology advances, it raises profound ethical questions. Digital twins involve extensive personal data collection, predictive modeling, and the creation of digital identities that may persist independently from the individual they represent. These developments challenge existing ethical frameworks concerning privacy, autonomy, consent, and accountability.
Why is this important?
The ethical implications of human digital twins are significant because the technology operates at the intersection of artificial intelligence, big data, and personal identity. If not carefully governed, it may introduce risks that affect individuals and society at large—such as a 2025 study estimating that biased twins could exacerbate healthcare disparities by up to 30% in underrepresented groups.
One major concern is privacy and data ownership. Digital twins require vast amounts of personal data, including sensitive health, behavioral, and genetic information. Questions arise about who owns this data, who controls the digital twin, and how securely this information is stored and used.
Another issue involves autonomy and consent. Individuals may consent to the use of their data at one point in time, but digital twins may continue to evolve and generate insights long after the original consent was given. This creates challenges in ensuring ongoing, informed consent and maintaining individuals’ control over how their digital representation is used.
There are also concerns about algorithmic bias and fairness. If digital twins are trained on biased datasets, they may produce inaccurate or discriminatory predictions. In healthcare, for instance, biased models could lead to unequal treatment recommendations across different populations.
Finally, the concept of a digital twin raises philosophical questions about identity and representation. If a digital twin can simulate decisions or predict behavior, to what extent does it represent the real person? And who is responsible when decisions are made based on simulations generated by the twin?
For whom is this important?
Clinical ResearcherData Protection OfficerEarly Career ResearcherHealth Care ProfessionalIT / Technical ProfessionalLegal AdvisorPolicy MakerRegulatorResearch Ethics Committee MemberTechnology Developer / EngineerResearchers, Research Ethics Committee MemberResearcherResearch Ethics ExpertOmbudsperson / Confidental counsellor
What are the best practices?
Addressing the ethical challenges of human digital twins requires a combination of technological, regulatory, and social approaches.
One important best practice is privacy-by-design, meaning that systems are designed from the beginning to minimize data collection, protect sensitive information (e.g., federated learning to avoid central data storage), and ensure strong cybersecurity measures.
Another key principle is transparent governance. Developers and organizations should clearly explain how digital twins are created, what data they use, and how predictions or simulations are generated via open-source audits or explainable AI tools. Transparency helps build trust and allows users to make informed decisions.
Dynamic and ongoing consent mechanisms are also essential. Because digital twins evolve over time, consent should not be a one-time agreement but an ongoing process allowing individuals to update or withdraw permissions as technologies change, with easy “kill switches” for twins.
In addition, fairness and bias mitigation strategies must be implemented in AI models that power digital twins. This includes diverse training datasets (prioritizing global representation), continuous monitoring of algorithmic performance, and independent audits.
Finally, multidisciplinary collaboration is crucial. Ethical governance of digital twins should involve not only engineers and data scientists but also ethicists, legal experts, healthcare professionals, and representatives of affected communities. Research communities should pilot these in controlled studies to refine standards.
contributed to this theme. Latest contribution was Mar 09, 2026
