Research Article
AI-Enabled Interoperability in Nigeria’s Public Sector: Evaluating the Role of X-Road Digital Infrastructure
Ololade Oluwatosin Adesuyi*
Issue:
Volume 10, Issue 2, December 2026
Pages:
179-188
Received:
4 May 2026
Accepted:
22 May 2026
Published:
30 June 2026
DOI:
10.11648/j.ajai.20261002.11
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Abstract: Despite the efforts being made towards digital transformation, the continued disintegration of digital systems within the public sector and the lack of interoperability frameworks are continuing to hinder effective governance, coordinated service delivery and evidence-informed policy-making in Nigeria. The study focuses on the use of the X-Road digital infrastructure model for enhancing public-sector governance in Nigeria by leveraging the artificial intelligence (AI) capabilities of the interoperability model. Specifically, the study attempts to answer four research questions: How do interoperability challenges impact governance outcomes in Nigeria? How can AI improve data exchange and decision-making? Is X-Road adaptable to Nigeria's governance environment? What institutional conditions are required for successful implementation? The study followed the qualitative document analysis and comparative desk-based research design, based on secondary data regarding Estonia's X-Road framework obtained from policy documents, institutional reports, academic literature and case materials. The results show that interoperability, powered by AI, can be highly beneficial for data integration, administrative automation, transparency, and efficient delivery of public services by enabling real-time information sharing among governments. The study also revealed that digital infrastructure, institutional coordination, technical capacity, legal frameworks and cyber security issues are still significant challenges to implementation in Nigeria. The study finds that technological innovation is not enough for achieving interoperability if institutions and policies are not strong. It therefore calls for gradual and locally tailored adoption of X-Road-like systems, adoption of a comprehensive data protection and information sharing law, investments in digital infrastructure and human capacity development, and a single coordinating body for the implementation of interoperability standards and implementation in public institutions.
Abstract: Despite the efforts being made towards digital transformation, the continued disintegration of digital systems within the public sector and the lack of interoperability frameworks are continuing to hinder effective governance, coordinated service delivery and evidence-informed policy-making in Nigeria. The study focuses on the use of the X-Road dig...
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Research Article
Verification Challenges of AI-Generated Identity Documents: Blockchain Technology as a Trust Layer and AI as a Supporting Signal
Tapendra Baduwal*
Issue:
Volume 10, Issue 2, December 2026
Pages:
189-197
Received:
14 May 2026
Accepted:
25 May 2026
Published:
2 July 2026
DOI:
10.11648/j.ajai.20261002.12
Downloads:
Views:
Abstract: The rapid advancement of generative AI has revolutionized digitalization, while simultaneously introducing new security challenges. As AI models are increasingly integrated into cameras to enhance or modify images, a fundamental question arises for verification systems: whether captured images retain authentic camera fingerprints, such as Photo-Response Non-Uniformity (PRNU), Color Filter Array (CFA) patterns, physically random sensor noise, and lens distortions, or are heavily altered or fully generated by AI. Modern generative AI models create images that are highly similar to those produced by cameras, increasing the risk of document forgery and verification challenges. To address these challenges, this research proposes blockchain technology as a foundational trust layer for digital identity, enabling secure and tamper-proof evidence recording through an immutable ledger and cryptographic mechanisms. The proposed system integrates blockchain with a layered microservices architecture, separating user management, blockchain interaction, and audit logging into independent services. Communication between services uses gRPC with clearly defined Protocol Buffer schemas for efficient communication. The API layer is implemented using FastAPI for authentication, authorization, and request routing with high performance and automatic documentation. Data is stored in MongoDB, including user profiles, authentication records, verification results, and audit logs, which ensures flexibility and high availability. AI is used as a supporting signal rather than a definitive decision-maker. Experimental evaluation was conducted on 4,550 handwritten signatures, created using real ink pens but not belonging to any specific individual, and 4,550 AI-generated signatures were created using OpenAI's GPT image models, Nano Banana 2, and Qwen image generation models. ResNet50 was used to compute the signal score and achieved an F1 score of 0.996 on the classification task. The proposed method is designed to generalize well across a wide range of document and image domains.
Abstract: The rapid advancement of generative AI has revolutionized digitalization, while simultaneously introducing new security challenges. As AI models are increasingly integrated into cameras to enhance or modify images, a fundamental question arises for verification systems: whether captured images retain authentic camera fingerprints, such as Photo-Res...
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