Research on the Construction of Moral Boundaries and Ethical Norms of AI Nursing in an Aging Society

Kevin Meng

doi:10.71222/zbzzzc31

Authors

Kevin Meng RDFZ Xishan School, Beijing, China Author

DOI:

https://doi.org/10.71222/zbzzzc31

Keywords:

aging, artificial intelligence, nursing, ethics, care, governance

Abstract

With the acceleration of global population aging, artificial intelligence (AI) nursing has become an important approach to addressing the rapidly growing demand for care services. However, the ethical risks and moral uncertainties associated with AI-assisted care require systematic clarification and normative guidance. This study investigates the construction of moral boundaries and ethical norms for AI nursing in an aging society. Drawing on the ethics of care, technology ethics, and responsibility ethics, it analyzes the mechanisms through which ethical risks emerge in AI nursing, including algorithmic bias, opaque decision-making, data privacy vulnerabilities, and the potential erosion of genuine human care under the dominance of technical rationality. The paper argues that traditional nursing ethics, centered on human caregivers, faces challenges of subject generalization, role ambiguity, and unclear standards when AI systems participate in care practices. On this basis, the research demonstrates the necessity of integrating AI ethics into nursing ethics to promote a paradigm shift toward human–machine collaboration. It proposes the construction of a multidimensional nursing framework that embeds ethical review throughout the life cycle of AI nursing technologies, emphasizes algorithm interpretability and accountability, and safeguards the dignity, autonomy, and rights of older adults. The study aims to provide theoretical support and practical guidance for the healthy development of AI nursing, promote the deep integration of technological empowerment and humanistic care, and achieve both quality improvement and ethical balance in nursing services in an aging society.

References

1. C. Y. Chang, H. J. Jen, and W. S. Su, "Trends in artificial intelligence in nursing: Impacts on nursing management," Journal of Nursing Management, vol. 30, no. 8, pp. 3644–3653, 2022.

2. M. Freudendal-Pedersen, "Ethics and responsibilities," in The Routledge handbook of mobilities, Routledge, pp. 143–153, 2014.

3. K. Seibert, D. Domhoff, D. Bruch, M. Schulte-Althoff, D. Fürstenau, F. Biessmann, and K. Wolf-Ostermann, "Application scenarios for artificial intelligence in nursing care: rapid review," Journal of Medical Internet Research, vol. 23, no. 11, p. e26522, 2021.

4. N. Maestas, K. J. Mullen, and D. Powell, "The effect of population aging on economic growth, the labor force, and productivity," American Economic Journal: Macroeconomics, vol. 15, no. 2, pp. 306–332, 2023.

5. F. Stokes and A. Palmer, "Artificial intelligence and robotics in nursing: ethics of caring as a guide to dividing tasks between AI and humans," Nursing Philosophy, vol. 21, no. 4, p. e12306, 2020.

6. G. Chaudhary, "Unveiling the black box: Bringing algorithmic transparency to AI," Masaryk University Journal of Law and Technology, vol. 18, no. 1, pp. 93–122, 2024.

7. V. Gallistl, M. U. L. Banday, C. Berridge, A. Grigorovich, J. Jarke, I. Mannheim, and A. Peine, "Addressing the black box of AI—a model and research agenda on the co-constitution of aging and artificial intelligence," The Gerontologist, vol. 64, no. 6, p. gnae039, 2024.

8. A. Zhavoronkov, P. Mamoshina, Q. Vanhaelen, M. Scheibye-Knudsen, A. Moskalev, and A. Aliper, "Artificial intelligence for aging and longevity research: Recent advances and perspectives," Ageing Research Reviews, vol. 49, pp. 49–66, 2019.

9. C. H. Chu, S. Donato-Woodger, S. S. Khan, R. Nyrup, K. Leslie, A. Lyn, and A. Grenier, "Age-related bias and artificial intelligence: a scoping review," Humanities and Social Sciences Communications, vol. 10, no. 1, p. 510, 2023.

10. P. Abadir, E. Oh, R. Chellappa, N. Choudhry, G. Demiris, D. Ganesan, and J. D. Walston, "Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care," Alzheimer's & Dementia, vol. 20, no. 4, pp. 3074–3079, 2024.

11. R. L. Fritz and G. Dermody, "A nurse-driven method for developing artificial intelligence in 'smart' homes for aging-in-place," Nursing Outlook, vol. 67, no. 2, pp. 140–153, 2019.

12. S. Park, E. Ahn, T. H. Ahn, S. Ahn, S. Park, E. Kwon, and Y. Yang, "Artificial intelligence and aging in place: A scoping review of current applications and future directions," The Gerontologist, vol. 65, no. 6, p. gnaf130, 2025.

13. A. Zhavoronkov and P. Mamoshina, "Deep aging clocks: the emergence of AI-based biomarkers of aging and longevity," Trends in Pharmacological Sciences, vol. 40, no. 8, pp. 546–549, 2019.

14. C. C. Lee, J. Yan, and F. Wang, "Impact of population aging on food security in the context of artificial intelligence: Evidence from China," Technological Forecasting and Social Change, vol. 199, p. 123062, 2024.

15. L. McDaniel, I. Essien, S. Lefcourt, E. Zelleke, A. Sinha, R. Chellappa, and P. M. Abadir, "Aging with artificial intelligence: how technology enhances older adults’ health and independence," The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, vol. 80, no. 7, p. glaf086, 2025.

16. A. H. Sapci and H. A. Sapci, "Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: systematic review," JMIR Aging, vol. 2, no. 2, p. e15429, 2019.

17. S. J. Czaja and M. Ceruso, "The promise of artificial intelligence in supporting an aging population," Journal of Cognitive Engineering and Decision Making, vol. 16, no. 4, pp. 182–193, 2022.

18. S. Cho, S. Eom, D. Kim, T. H. Kim, J. S. Uhm, H. N. Pak, and B. Joung, "Artificial intelligence–derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study," European Heart Journal, vol. 46, no. 9, pp. 839–852, 2025.