Evaluating the Performance of AI-Based Software Tools in Intelligent Decision-Making Systems

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1. Maqaal joornaal2026Published PDF

   Building Digital Trust in East Africa: The Role of National ID and ICT Policy in Somalia's Digital Transformation

   Mohamed Adam Isak, Abdulkadir Jeilani Mohamud — F1000Research, Volume 15, Article 1029, 2026

   Trusted digital identities sit at the heart of any modern, inclusive financial system. This paper investigates Somalia’s transition from a fragmented digital landscape to a secure, integrated ecosystem through the lens of a “Three Pillars” framework: Robust Digital Identity, Comprehensive ICT Policy, and Proactive Cybersecurity Safeguards. Utilizing a qualitative multi-method approach, the study triangulates fifteen semi-structured interviews with regulators, financial experts, and civil society against a rigorous analysis of foundational policies, including the Data Protection Act (2023) and the Cybersecurity Law (2026). Results indicate that Somalia’s digital identity ecosystem—comprising e-Aqoonsi, HUBIYE, and the Certificate Delivery System (CDS)—serves as a technical anchor for financial inclusion, significantly bolstered by a July 2025 Central Bank mandate requiring National IDs for all banking transactions. These tools are supported by legal scaffolding under the Data Protection Authority and proactive security guardrails provided by the Somalia Computer Incident Response Team (SOMCIRT). However, comparative analysis reveals that while Somalia has achieved technical parity with East African Community (EAC) peers in biometric capabilities, it remains an outlier due to its reliance on foreign data hosting, which poses risks to national data sovereignty. To realize Somalia’s Centennial Vision 2060, the study concludes that the nation must bridge implementation gaps in rural digital literacy and technical capacity while transitioning toward local data infrastructure. The paper proposes a strategic roadmap for regional harmonization and enhanced institutional coordination to sustain

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2. Maqaal joornaal2026Published PDF

   Trustworthy AI Governance Framework for Autonomous and AI-Driven Networked Systems

   Abdinasir Ismael Hashi, Abdirizak Mohamed Hashi, Osman Abdullahi Jama — International Journal of Computer Trends and Technology, 74(1), 12–27, 2026

   The rapid expansion of autonomous and AI-driven networked systems across smart cities, transportation, healthcare, and cyber–physical infrastructures has intensified concerns related to trust, safety, transparency, and regulatory compliance. Existing approaches often address governance, ethics, and technical assurance in isolation, leaving a gap between high-level principles and practical system implementation. To address this challenge, this study proposes a comprehensive trustworthy AI governance framework that integrates system-theoretic modeling with governance constrained decision-making. The framework models autonomous systems as distributed cyber–physical–social systems and embeds ethical, safety, and legal constraints directly into the learning and optimization process using constrained Markov decision processes and Lagrangian optimization. A composite trustworthiness metric is formulated by aggregating Fairness, Robustness, Privacy, Explainability, Security, and Accountability dimensions. The methodology combines analytical modeling with simulated datasets representing multi-agent autonomous networks. Experimental results demonstrate a composite trust score of approximately 0.813, explainability stability of 0.905, and near-zero governance violations, confirming improved compliance, reduced risk, and enhanced trust compared to unguided AI systems.

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3. Maqaal joornaal2025Published PDF

   Machine Learning–Driven Fintech Solutions for Credit Scoring and Financial Inclusion in the Gig Economy

   Abdinasir Ismael Hashi — International Journal of Innovative Science and Research Technology, Volume 10, Issue 8, 2025

   The rapid expansion of the gig economy has generated new challenges for financial inclusion, particularly in credit scoring where traditional models fail to accommodate the fragmented and non-standardized income streams of gig workers. This study introduces FinGig-CreditNet (Financial Inclusion Gig-Credit Neural Framework), a novel machine learning–driven framework that integrates behavioral, transactional, and alternative data to construct adaptive, explainable, and portable credit scores for gig workers. Unlike existing labor credit scoring approaches, FinGig-CreditNet employs a hybrid deep learning architecture combining graph-based feature extraction with interpretable ensemble learning, ensuring transparency and robustness across heterogeneous gig platforms. A multi-layered design enables the aggregation of platform-specific performance signals, psychometric indicators, and mobile payment histories into a unified credit profile. Experimental evaluation on a synthesized multi-platform gig dataset demonstrates that FinGig-CreditNet improves default prediction accuracy by 12.7% and fairness metrics by 9.4% compared to baseline credit scoring models. More importantly, the framework enhances the portability of creditworthiness across platforms, thereby creating an interoperable ecosystem where gig workers can leverage their digital reputation for financial access. The findings highlight FinGig-CreditNet as a scalable solution bridging fintech innovation, machine learning, and social equity, offering both theoretical and policy contributions to the design of inclusive financial infrastructures in the digital labor economy.

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