Building Trust in AI: How Ethics, Standards, and Blockchain are Redefining Global Governance
DOI:
https://doi.org/10.24203/wzgj8b56Keywords:
model explainability, AI with Trust, Blockchain, AI with StandardsAbstract
The rapid advancement of Artificial Intelligence (AI) has amplified concerns around trust, transparency, and accountability in automated decision-making systems. This paper explores the foundational concept of AI with Trust, examining how ethical governance, data integrity, and algorithmic transparency can be strengthened through technological and policy interventions. Drawing from emerging frameworks such as the OECD AI Principles and scholarly insights on trustworthy AI, the study highlights blockchain as a key enabler of verifiable and tamper-proof AI processes. By linking blockchain’s decentralized auditability with AI’s need for explainability and fairness, the paper argues for an integrated approach that enhances public confidence in AI systems. The discussion positions transparency and accountability as cornerstones of responsible AI adoption and offers a roadmap for aligning innovation with ethical and societal values.
References
[1]. African Union. (2024, July). Continental Artificial Intelligence Strategy: Harnessing AI for Africa’s Development and Prosperity. African Union Commission. https://au.int/sites/default/files/documents/44004-doc-EN-_Continental_AI_Strategy_July_2024.pdf
[2]. Chen, S., Bassi, L., & Ahmed, R. (2025). Building trust in AI: How blockchain enhances data integrity, security, and privacy. IEEE Computer Magazine, 58(2), 63–70. https://doi.org/10.1109/MC.2024.3505012
[3]. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
[4]. European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L (1689), 12 July 2024.
[5]. European Commission. (2024). Regulation (EU) 2024/1680: Artificial Intelligence Act. Official Journal of the European Union. Brussels: European Union.
[6]. Floridi, L., & Cowls, J. (2021). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 31(3), 375–388. https://doi.org/10.1007/s11023-021-09548-8
[7]. Government of Canada, Innovation, Science and Economic Development Canada. (2023, March 13). The Artificial Intelligence and Data Act (AIDA) – Companion Document. https://ised-isde.canada.ca/site/innovation-better-canada/en/artificial-intelligence-and-data-act-aida-companion-document
[8]. Göksal, Ş. İ., & Solarte-Vásquez, M. C. (2024). The blockchain-based trustworthy AI supported by stakeholders-in-the-loop model (BCTrustAI.SL). ResearchGate.
[9].Infocomm Media Development Authority. (2024, January). Model AI Governance Framework for Generative AI (Proposed Draft). Singapore: IMDA.
[10]. ISO. (2024). ISO/IEC 42001:2024 – Artificial Intelligence Management System Standard. Geneva: International Organization for Standardization.
[11]. Jobin, A., & Luengo-Oroz, M. (2024). Embedding ethics into AI design: From principles to practice. Nature Machine Intelligence, 6(1), 45–52.
[12]. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
[13]. Ndung'u, R. N. (2022). Blockchain as a Solution of Information Security and Data Privacy Issues. Review. International Journal of Computer Applications Technology and Research, 11(08), 337-340.
[14]. Nguyen, T., Li, K., & Mensah, R. (2024). Trustworthy AI governance: Balancing innovation and regulation in emerging economies. Journal of Artificial Intelligence Policy, 9(2), 45–61. https://doi.org/10.1080/jaip.2024.0092
[15]. Plantinga, P., Shilongo, K., Mudongo, O., Umubyeyi, A., Gastrow, M., & Razzano, G. (2024). Responsible artificial intelligence in Africa: Towards policy learning. Data & Policy, 6, e72. https://doi.org/10.1017/dap.2024.60
[16]. Organisation for Economic Co-operation and Development (OECD). (2024, May). OECD updates AI Principles to stay abreast of rapid technological developments. Press release. https://www.oecd.org/en/about/news/press releases/2024/05/oecd-updates-ai-principles-to-stay-abreast-of-rapid-technological-developments.html OECD
[17]. Organisation for Economic Co-operation and Development (OECD). (2024). AI Policy Observatory Annual Report 2024. Paris: OECD Publishing. Retrieved from https://oecd.ai
[18]. O’Neill, C., & Karim, S. (2025). Algorithmic accountability and democratic oversight in AI governance. AI Ethics and Society, 7(1), 12–28. https://doi.org/10.1016/aiethics.2025.01.003
[19]. Smart Africa. (2025). Smart Africa Alliance Annual Review 2025. Kigali: Smart Africa Secretariat.
[20]. Roberts, H., Cowls, J., Morley, J., & Floridi, L. (2023). Global AI governance: Barriers and pathways forward. AI & Society, 38(4), 1207–1222. https://doi.org/10.1007/s00146-022-01556-x
[21]. The White House. (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Washington, D.C.: The White House. Retrieved from https://www.whitehouse.gov
[22]. UNESCO. (2024). Recommendation on the Ethics of Artificial Intelligence: Implementation Status 2024. Paris: United Nations Educational, Scientific and Cultural Organization.
[23]. UNESCO. (2024). AI for Humanity dialogue series: Building collective intelligence and trust.
[24]. UNESCO Digital Policy Report. https://unesdoc.unesco.org/
[25]. Usher, M., & Barak, M. (2024). Unpacking the role of AI ethics online education for science and engineering. International Journal of STEM Education, 11, Article 35. https://doi.org/10.1186/s40594-024-00493-4
[26]. Zhang, Y., & Alami, L. (2024). Blockchain for algorithmic transparency: Emerging frameworks for AI accountability. Global Technology Governance Review, 5(3), 77–94. https://doi.org/10.1080/gtgr.2024.0214
[27]. Zhang, P. (2024). Exploiting Blockchain to Make AI Trustworthy: A Software Architecture for Transparency and Auditing. ACM.
[28]. China Cyberspace Administration. (2024). Interim Measures for the Management of Generative Artificial Intelligence Services. Beijing: Cyberspace Administration of China. Retrieved from https://www.cac.gov.cn
[29]. Japan Cabinet Office. (2024). Social Principles of Human-Centric AI (Revised 2024 Edition). Tokyo: Government of Japan.
[30]. Global Partnership on Artificial Intelligence (GPAI). (2025). Annual Report 2025: Advancing Responsible AI Through Global Collaboration. Paris: GPAI Secretariat. Retrieved from https://gpai.ai
[31]. United Nations. (2024). Interim Report of the UN High-Level Advisory Body on Artificial Intelligence. New York: United Nations. Retrieved from https://www.un.org/ai-advisory-body
[32]. United Nations Educational, Scientific and Cultural Organization (UNESCO). (2021). Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO. Retrieved from https://unesdoc.unesco.org
[33]. Ministry of Information, Communications and the Digital Economy (MICDE). (2024). Draft National Artificial Intelligence Policy of Kenya. Nairobi: Government of Kenya. Retrieved from https://ict.go.ke
[34]. Office of the Data Protection Commissioner (ODPC). (2025). Data Protection Act (Amendment) 2025: Strengthening Digital Trust and AI Governance. Nairobi: Government of Kenya. Retrieved from https://www.odpc.go.ke
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rachael Njeri Ndung’u
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
