Jul 2026· 2026 ITU Kaleidoscope - AI and Frontier Technologies for Good (ITU K)· pp. 1-5· 0 citations· 1 references
Abstract
Artificial intelligence is increasingly embedded in critical digital infrastructure across emerging markets, supporting applications in telecommunications, financial inclusion, healthcare, agriculture, fraud detection and public service delivery. While global responsible AI frameworks have established widely accepted principles for fairness, transparency, accountability, and human oversight, translating these principles into operational safeguards remains uneven, particularly in contexts characterized by institutional fragmentation, evolving regulatory regimes, vendor dependence, and constrained governance capacity. This paper examines structural implementation gaps that arise when globally flexible responsible AI frameworks are applied without contextual integration. It advances a governance by design model that operationalizes trust through measurable proportional risk tiering, lifecycle-based oversight, and structured ecosystem accountability. Governance controls are embedded at decision points spanning design, procurement, deployment, and continuous monitoring, reducing governance debt and strengthening institutional resilience. An applied case illustration demonstrates how proportional governance can be integrated into high impact deployments while preserving scalability. Aligned with emerging international standards, including ISO/IEC 42001, the models offer a scalable, context-aware pathway for implementing trustworthy, inclusive and sustainable AI under real world constraints.
This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance.
M. Modi· International Journal of Eng...· 0 citations
Artificial intelligence (AI) has become essential to corporate decision‐making, yet current environmental, social, and governance (ESG) frameworks offer limited tools for assessing algorithmic responsibility. This paper examines whether AI's distinctive features, namely, lack of transparency, delegated agency, and ongoing adjustment, challenge the organizational reasoning of ESG frameworks. Drawing on organizational theory and science and technology studies (STS), we argue that ESG frameworks, which evolved through incremental adjustment, may prove insufficient for governing algorithmic systems. While integration succeeded for issues such as cybersecurity and climate risk, AI differs because algorithms operate as social and technical systems that distribute responsibility across human and non‐human networks. We propose adding a fourth pillar, Algorithmic Governance, within an extended ESGA framework to address risks that transcend traditional governance categories. It is intended as a conceptual extension of investor‐facing ESG architectures rather than a replacement of existing standards. This pillar highlights fairness, transparency, responsibility, and robustness as core dimensions of corporate responsibility. The paper contributes to organizational theory by conceptually examining conditions under which established governance architectures require structural extension and to technology governance by rethinking responsibility in mixed human‐algorithmic systems. We further discuss how algorithmic risks may vary across environmental, social, and governance domains and outline conceptual approaches for handling heterogeneity, sectoral differences, and data constraints.
Pitabas Mohanty, Supriti Mishra· Business Strategy and the En...· 0 citations
Critical infrastructure systems are becoming more digitized and interconnected, producing large amounts of data.
While this digital transformation improves operational efficiency across sectors, it also raises privacy and governance issues.
Current privacy regulations struggle to meet the demands of modern, complex infrastructure ecosystems. This study
investigates the integration of privacy-by-design principles within critical infrastructure systems, emphasizing governance
frameworks that align regulatory compliance, cybersecurity resilience, and technical system design. Findings indicate that
multi-layered governance architectures, privacy impact assessments, and automated compliance technologies enable
proactive management of privacy risks while maintaining system continuity. Case-based frameworks illustrate successful
applications where privacy-integrated infrastructure strengthened regulatory adherence, mitigated cyber threats, and
improved national resilience. The study also identifies key enablers, including enterprise risk management, workforce
capacity building, and adaptive policy mechanisms, alongside challenges such as fragmented governance models, distributed
computing environments, and evolving regulatory requirements. Ultimately, these efforts establish a scalable foundation for
secure, privacy-resilient, and compliant critical infrastructure systems, fostering stakeholder trust and sustainable digital
operations.
Babatunde Ogunsipe· International Journal of Inn...· 0 citations
Artificial intelligence (AI) adoption is accelerating across industries, introducing novel governance, risk, and compliance (GRC) challenges that traditional cybersecurity frameworks cannot fully address. Standards such as ISO/IEC 27001 and the NIST Risk Management Framework safeguard IT assets but do not comprehensively mitigate AI-specific risks like adversarial attacks, model drift, and ethical concerns such as fairness and accountability. This gap raises critical questions about how organizations can govern AI responsibly while maintaining security and compliance. This paper reviews emerging AI-GRC frameworks and regulations, including the OECD AI Principles, ISO/IEC 42001, the NIST AI Risk Management Framework, and the EU AI Act, alongside industry standards from Microsoft, Google, and IBM. Through comparative analysis, we examine how these frameworks address governance structures, risk assessment methodologies, compliance mechanisms, and transparency requirements. We also explore integration strategies with existing cybersecurity and enterprise risk models. Our findings reveal a fragmented ecosystem with overlapping principles but inconsistent enforcement and technical depth. While global standards emphasize values such as transparency and accountability, operational guidance on adversarial robustness and lifecycle risk monitoring remains limited. The review identifies best practices for embedding GRC into AI development pipelines, including continuous monitoring, documentation artifacts, and risk-tiering strategies. It also highlights gaps in interoperability, audit tooling, and liability regimes. This research speaks to policymakers, compliance officers, cybersecurity professionals, and AI developers seeking harmonized governance approaches. Future work should prioritize unified audit standards, empirical evaluation of governance effectiveness, and integration of AI risk metrics into ESG reporting to ensure trustworthy and sustainable AI deployment.
Abimbola Filani, J. Opoku· Magna Scientia Advanced Rese...· 0 citations
Artificial intelligence (AI) is rapidly transforming governance systems across sectors, yet most
institutions continue to rely on oversight models designed for pre-digital environments. As AI
becomes embedded in cyber-physical systems, organizational decision processes, and regulatory
infrastructures, governance must evolve from static compliance to adaptive,
intelligence-augmented oversight. This manuscript introduces the AI-Enabled Governance
Oversight Model (AIGOM). This layered decision-support intelligence architecture integrates
AI-driven sensing, operational observability, analytics, and adaptive decision-support into
governance systems while preserving human accountability, governance interpretation, and
ethical control. The model demonstrates how AI can serve as a governance augmentation layer,
generating decision-support intelligence, accelerating operational awareness, enhancing adaptive
oversight, and supporting real-time governance recalibration. AIGOM extends the Adaptive
Governance Systems Framework (AGSF) by specifying how AI capabilities interface with
governance processes across diverse sectors, including critical infrastructure, healthcare, finance,
and public administration. This manuscript establishes a theoretical and operational foundation
for AI-enabled governance across complex socio-technical environments.
Dr. Robb Shawe· International journal of adv...· 0 citations
This study develops a six-phase human-centred governance framework for responsible AI adoption through an integrative synthesis of academic literature, international standards, and regulatory frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the European Union Artificial Intelligence Act.