A Discussion on Ethics, Trust and Security Governance in the Evolution of Artificial Intelligence Technology
DOI:
https://doi.org/10.71204/vwpczb65Keywords:
Security Risk Management, AI Ethics, Algorithmic Fairness, Explainable AIAbstract
With the rapid advancement and widespread deployment of artificial intelligence , issues related to ethics, trust, and security have become increasingly prominent, posing significant challenges to its sustainable development. This paper aims to systematically investigate these intertwined challenges by constructing a structured analytical framework encompassing three core dimensions: ethics, trust, and security.Methodologically, the study analyzes the evolutionary trajectory and application contexts of AI, and synthesizes key risk patterns and governance concerns. It examines major ethical issues, including algorithmic bias, data privacy, responsibility attribution, and value alignment, and further explores the underlying tension between technological rationality and social values.In addition, the paper proposes a multi-level trust formation mechanism based on technological reliability, institutional assurance, and public cognition, emphasizing that trust emerges from the joint interaction of technical systems and governance structures. At the security level, it highlights the complexity and cross-domain nature of AI risks, and advocates for a full life-cycle governance framework integrating risk assessment, technical auditing, and continuous supervision.The study’s key contribution lies in integrating ethical, trust, and security perspectives into a unified analytical framework, and in proposing a collaborative governance approach involving governments, enterprises, and international stakeholders.
References
Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics, 5(3), 3265–3279.
Corrêa, N. K., Galvão, C., Santos, J. W., et al. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10), 100857.
Kashefi, P., Kashefi, Y., & Ghafouri Mirsaraei, A. H. (2024). Shaping the future of AI: Balancing innovation and ethics in global regulation. Uniform Law Review, 29(3), 524–548.
Kattnig, M., Angerschmid, A., Reichel, T., et al. (2024). Assessing trustworthy AI: Technical and legal perspectives of fairness in AI. Computer Law & Security Review, 55, 106053.
Lahusen, C., Maggetti, M., & Slavkovik, M. (2024). Trust, trustworthiness and AI governance. Scientific Reports, 14(1), 20752.
Lainjo, B. (2024). The role of artificial intelligence in achieving the United Nations sustainable development goals. Journal of Sustainable Development, 17(5), 30.
Li, Y., Wu, B., Huang, Y., et al. (2024). Developing trustworthy artificial intelligence: Insights from research on interpersonal, human-automation, and human-AI trust. Frontiers in Psychology, 15, 1382693.
McCormack, L., & Bendechache, M. (2024). Ethical AI governance: Methods for evaluating trustworthy AI. arXiv preprint arXiv:2409.07473.
Ricciardi Celsi, L., & Zomaya, A. Y. (2025). Perspectives on managing AI ethics in the digital age. Information, 16(4), 318.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th US ed.). Pearson.
Sharma, S. (2023). Trustworthy artificial intelligence: Design of AI governance framework. Strategic Analysis, 47(5), 443–464.
Sistla, S. (2024). AI with integrity: The necessity of responsible AI governance. Journal of Artificial Intelligence & Cloud Computing, 3(5), 2–3.
Taeihagh, A. (2025). Governance of generative AI. Policy and Society, 44(1), 1–22.
Vercelli, A. (2024). United Nations, artificial intelligences and regulations: Analysis of the General Assembly AI resolutions and the final report of the advisory body on AI (short paper). In Proceedings of BEWARE@AI (pp. 99–106).
Whig, D. P. (2025). Ethical AI governance: A framework for ensuring transparency, fairness, and accountability. Journal of Healthcare AI and ML, 12, 12.
Xu, J., Lee, T., & Goggin, G. (2024). AI governance in Asia: Policies, praxis and approaches. Communication Research and Practice, 10(3), 275–287.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Shufeng Wang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited. Authors retain copyright of their work, and readers are free to copy, share, adapt, and build upon the material for any purpose, including commercial use, as long as appropriate attribution is given.